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Frank Werner

Otto-von-Guericke-University Magdeburg, Faculty of Mathematics, Faculty Member

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Phone: +49 391 67 52025
Address: Faculty of Mathematics, Otto-von-Guericke University Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany

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Papers by Frank Werner

Orangutan Optimization Algorithm: An Innovative Bio-Inspired Metaheuristic Approach for Solving Engineering Optimization Problems

International Journal of Intelligent Engineering and Systems, 2025

In this paper, a new metaheuristic algorithm called Orangutan Optimization Algorithm (OOA) is des... more In this paper, a new metaheuristic algorithm called Orangutan Optimization Algorithm (OOA) is designed,
which imitates the behaviors of Orangutans in nature. The fundamental inspiration of OOA is the foraging strategy of
Orangutans and the skills of these animals in nesting. The theory of OOA is explained and then the implementation
steps of OOA in two phases of exploration and exploitation are mathematically modeled. The performance of OOA in
dealing with real-world applications is evaluated on twenty-two constrained optimization problems from the CEC
2011 test suite. The optimization results show that the proposed OOA approach, by balancing exploration and
exploitation during the search process, is able to provide suitable solutions for the benchmark functions. Also, in order
to measure the quality of OOA, the results obtained from the proposed approach are compared with twelve well-known
metaheuristic algorithms. Analysis of the simulation results shows that OOA has provided superior performance by
providing better results in 100% of the benchmark functions compared to competitor algorithms.

Spider-Tailed Horned Viper Optimization: An Effective Bio-Inspired Metaheuristic Algorithm for Solving Engineering Applications

International Journal of Intelligent Engineering and Systems, 2025

This study presents a new optimization algorithm called Spider-Tailed Horned Viper Optimization (... more This study presents a new optimization algorithm called Spider-Tailed Horned Viper Optimization
(STHVO), inspired by the spider-tailed horned viper. The viper's unique hunting strategy, which involves using its
spider-like tail to attract prey, serves as the basis for this algorithm. STHVO incorporates two key processes:
exploration and exploitation. Exploration allows the algorithm to search broadly for potential solutions, similar to how
the viper moves through varied terrains in search of prey. Exploitation refines these solutions, akin to the snake
focusing on its target once it has been lured. STHVO was rigorously tested across twenty-three benchmark functions,
including unimodal and multimodal test suites. These benchmarks provide a comprehensive framework for assessing
the algorithm's performance. The results showed that STHVO effectively balances exploration and exploitation,
consistently finding high-quality solutions and outperforming a dozen established metaheuristic algorithms on most
benchmarks. The algorithm's superior performance in both theoretical and practical contexts highlights its robustness
and versatility. Overall, STHVO offers a novel, nature-inspired approach to optimization, proving to be a powerful
tool for achieving optimal solutions across diverse applications.

Cancers, 2024

Background/Objectives: Breast cancer remains one of the biggest health challenges for women world... more Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings.

Sales Training Based Optimization: A New Human-inspired Metaheuristic Approach for Supply Chain Management

International Journal if Intelligent Engineering & Systems, 2024

Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seek... more Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seeks to balance the economic goals of minimizing costs with environmental and social objectives, ensuring efficient production and inventory management while reducing environmental impact and enhancing social responsibility. Metaheuristic algorithms play a crucial role in solving Sustainable Lot Size Optimization problems by efficiently exploring large and complex search spaces to find near-optimal solutions that balance economic, environmental, and social objectives, often outperforming traditional optimization methods in terms of flexibility and scalability. With this attitude, in this paper, a new metaheuristic algorithm called Sales Training Based Optimization (STBO) is designed to solve Sustainable Lot Size Optimization applications. The fundamental inspiration in the design of STBO draws upon human behaviors observed during sales training. The theoretical framework of STBO is thoroughly described, and its implementation process is mathematically formulated in two distinct stages: the exploration phase and the exploitation phase. The efficiency of STBO to address Sustainable Lot Size Optimization applications has been evaluated on 10 study scenarios. The optimization outcomes reveal that STBO consistently delivers highly effective solutions by seamlessly integrating exploration with exploitation throughout the search. Furthermore, a thorough comparison was conducted, revealing how STBO's results stack up against those from twelve widely recognized metaheuristic algorithms. The simulation findings conclusively demonstrate that the STBO approach consistently outperforms competitors, achieving superior performance across all study scenarios. These insights confirm that the STBO approach serves as a highly reliable and potent optimization tool, capable of addressing a wide range of optimization challenges in diverse applications.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

Processes, Jun 25, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Computers, materials & continua/Computers, materials & continua (Print), 2024

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Opti... more This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm&#39;s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard&#39;s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard&#39;s retreat to the treetops after feeding. To assess FLO&#39;s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO&#39;s performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO&#39;s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

An overview of some single machine scheduling problems: polynomial algorithms, complexity and approximability

arXiv (Cornell University), May 29, 2024

Since the publication of the first scheduling paper in 1954, a huge number of works dealing with ... more Since the publication of the first scheduling paper in 1954, a huge number of works dealing with different types of single machine problems appeared. They addressed many heuristics and enumerative procedures, complexity results or structural properties of certain problems. Regarding surveys, often particular subjects like special objective functions are discussed, or more general scheduling problems were surveyed, where a substantial part is devoted to single machine problems. In this paper we present some results on polynomial algorithms, complexity and approximation issues, where the main focus is on results, which have been published during the last decades in papers, where at least one of the first two authors of this paper was involved. We hope that the reviewed results will stimulate further investigation in related research fields.

Special Issue “Recent Advances of Discrete Optimization and Scheduling”

Mathematics, Mar 8, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data

Algorithms, Jan 13, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

LX-BBSCA: Laplacian biogeography-based sine cosine algorithm for structural engineering design optimization

AIMS mathematics, Dec 31, 2022

Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications

CMES - Computer Modeling in ENgineering & Science, 2024

This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired b... more This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine's adeptness in locating carrion and pursuing live prey. The algorithm's uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA's robust performance in exploration, exploitation, and maintaining a balance between these phases throughout the search process. Compared to twelve established metaheuristic algorithms, WoOA consistently demonstrates a superior performance across diverse benchmark functions. Statistical analyses, including paired t-tests, Friedman test, and Wilcoxon rank-sum tests, validate WoOA's significant competitive edge over its counterparts. Additionally, WoOA's practical applicability is illustrated through its successful resolution of twentytwo constrained scenarios from the CEC 2011 suite and four complex engineering design challenges. These applications underscore WoOA's efficacy in tackling real-world optimization challenges, further highlighting its potential for widespread adoption in engineering and scientific domains.

Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms

Algorithms, 2024

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) sys... more In this paper, we consider some problems that arise in connected and autonomous vehicle
(CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore,
scheduling solution algorithms can be used as a part of solution algorithms for real-world problems.
For four variants of such problems, mathematical models and solution algorithms are presented. In
particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV
scheduling problems are considered in the literature for the first time. More complicated NP-hard
scheduling problems related to CAVs can be considered in the future.

Fossa Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Engineering Applications

International Journal of Intelligent Engineering and Systems, 2024

This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Al... more This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Algorithm (FOA), which emulates the natural hunting behavior of the fossa in its habitat. FOA draws its core inspiration from the two-stage hunting technique of the fossa, involving an initial attack on a spotted lemur followed by a pursuit through the trees. The theoretical framework of FOA is elaborated, and its implementation is mathematically modeled in two distinct phases: (i) exploration, which simulates the fossa's positional adjustments during the initial attack on the lemur, and (ii) exploitation, which models the positional changes of the fossa during the chase. The efficacy of FOA is tested against twenty-two constrained optimization problems from the CEC 2011 test suite, as well as four engineering design challenges. The optimization results demonstrate FOA's strong capabilities in both exploration and exploitation, maintaining a balance that facilitates convergence to optimal solutions. FOA's performance is benchmarked against twelve established algorithms, showing that it consistently outperforms its competitors by delivering superior results and ranking as the top optimizer in most of the evaluated functions. These findings indicate that FOA is highly effective in addressing optimization tasks in real-world scenarios.

GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

Mathematical Biosciences and Engineering, 2024

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective to... more Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of
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Mathematical Biosciences and Engineering Volume 21, Issue 8, 6847-6869.
the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%.

Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

Computers, Materials & Continua, 2024

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Opti... more This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird's kleptoparasitic behavior in two distinct phases: exploration and exploitation. During the exploration phase, the algorithm searches for new potential solutions across a broad area, akin to the frigatebird scouting for vulnerable seabirds. In the exploitation phase, the algorithm fine-tunes the solutions, similar to the frigatebird focusing on a single target to secure its meal. To evaluate MFO's performance, the algorithm is tested on twentythree standard benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The results from these evaluations highlight MFO's proficiency in balancing exploration and exploitation throughout the optimization process. Comparative studies with twelve well-known metaheuristic algorithms demonstrate that MFO consistently achieves superior optimization results, outperforming its competitors across various metrics. In addition, the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications, thereby validating its practical utility and robustness.

Home health care planning with considering flexible starting/ending points and service features

arXiv (Cornell University), May 2, 2022

One of the recently proposed strategies in health systems is providing services to patients at ho... more One of the recently proposed strategies in health systems is providing services to patients at home, improving the service quality, besides reducing the health system costs. In the real world, some services, such as biological tests or blood sampling, force the nurses to start or end his/her route from/at the laboratory instead of the depot, changing the whole optimal planning. The effect of these special service requirements and features has not been considered so far. In this study, a new mathematical model is suggested considering the flexibility of starting/ending places of each nurse's route according to the specific characteristics of each service. Then several sets of problems in various sizes are solved using the proposed model, where the results confirm the efficiency of the proposed approach. In addition, some sensitivity analyses are performed on the parameters of the required features of the services, followed by some managerial insights and directions for future studies.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

by Frank Werner and Carmen García-Mata

Processes, 2024

Scheduling and optimization have a central place in the research area of computing because it is ... more Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics.

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Computers, Materials & Continua, 2024

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Opti... more This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm's core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard's sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard's retreat to the treetops after feeding. To assess FLO's efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO's performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO's superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Special Issue "Recent Advances of Discrete Optimization and Scheduling"

by Frank Werner and Bertrand M . T . Lin

Mathematics, 2024

This is the Editorial of the special issue with 10 published research papers.

Special issue "Discrete optimization: Theory, algorithms and new applications"

Mathematics, 2024

This is the Editorial for the special issue with 21 published papers.

Orangutan Optimization Algorithm: An Innovative Bio-Inspired Metaheuristic Approach for Solving Engineering Optimization Problems

International Journal of Intelligent Engineering and Systems, 2025

In this paper, a new metaheuristic algorithm called Orangutan Optimization Algorithm (OOA) is des... more In this paper, a new metaheuristic algorithm called Orangutan Optimization Algorithm (OOA) is designed,
which imitates the behaviors of Orangutans in nature. The fundamental inspiration of OOA is the foraging strategy of
Orangutans and the skills of these animals in nesting. The theory of OOA is explained and then the implementation
steps of OOA in two phases of exploration and exploitation are mathematically modeled. The performance of OOA in
dealing with real-world applications is evaluated on twenty-two constrained optimization problems from the CEC
2011 test suite. The optimization results show that the proposed OOA approach, by balancing exploration and
exploitation during the search process, is able to provide suitable solutions for the benchmark functions. Also, in order
to measure the quality of OOA, the results obtained from the proposed approach are compared with twelve well-known
metaheuristic algorithms. Analysis of the simulation results shows that OOA has provided superior performance by
providing better results in 100% of the benchmark functions compared to competitor algorithms.

Spider-Tailed Horned Viper Optimization: An Effective Bio-Inspired Metaheuristic Algorithm for Solving Engineering Applications

International Journal of Intelligent Engineering and Systems, 2025

This study presents a new optimization algorithm called Spider-Tailed Horned Viper Optimization (... more This study presents a new optimization algorithm called Spider-Tailed Horned Viper Optimization
(STHVO), inspired by the spider-tailed horned viper. The viper's unique hunting strategy, which involves using its
spider-like tail to attract prey, serves as the basis for this algorithm. STHVO incorporates two key processes:
exploration and exploitation. Exploration allows the algorithm to search broadly for potential solutions, similar to how
the viper moves through varied terrains in search of prey. Exploitation refines these solutions, akin to the snake
focusing on its target once it has been lured. STHVO was rigorously tested across twenty-three benchmark functions,
including unimodal and multimodal test suites. These benchmarks provide a comprehensive framework for assessing
the algorithm's performance. The results showed that STHVO effectively balances exploration and exploitation,
consistently finding high-quality solutions and outperforming a dozen established metaheuristic algorithms on most
benchmarks. The algorithm's superior performance in both theoretical and practical contexts highlights its robustness
and versatility. Overall, STHVO offers a novel, nature-inspired approach to optimization, proving to be a powerful
tool for achieving optimal solutions across diverse applications.

Cancers, 2024

Background/Objectives: Breast cancer remains one of the biggest health challenges for women world... more Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings.

Sales Training Based Optimization: A New Human-inspired Metaheuristic Approach for Supply Chain Management

International Journal if Intelligent Engineering & Systems, 2024

Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seek... more Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seeks to balance the economic goals of minimizing costs with environmental and social objectives, ensuring efficient production and inventory management while reducing environmental impact and enhancing social responsibility. Metaheuristic algorithms play a crucial role in solving Sustainable Lot Size Optimization problems by efficiently exploring large and complex search spaces to find near-optimal solutions that balance economic, environmental, and social objectives, often outperforming traditional optimization methods in terms of flexibility and scalability. With this attitude, in this paper, a new metaheuristic algorithm called Sales Training Based Optimization (STBO) is designed to solve Sustainable Lot Size Optimization applications. The fundamental inspiration in the design of STBO draws upon human behaviors observed during sales training. The theoretical framework of STBO is thoroughly described, and its implementation process is mathematically formulated in two distinct stages: the exploration phase and the exploitation phase. The efficiency of STBO to address Sustainable Lot Size Optimization applications has been evaluated on 10 study scenarios. The optimization outcomes reveal that STBO consistently delivers highly effective solutions by seamlessly integrating exploration with exploitation throughout the search. Furthermore, a thorough comparison was conducted, revealing how STBO's results stack up against those from twelve widely recognized metaheuristic algorithms. The simulation findings conclusively demonstrate that the STBO approach consistently outperforms competitors, achieving superior performance across all study scenarios. These insights confirm that the STBO approach serves as a highly reliable and potent optimization tool, capable of addressing a wide range of optimization challenges in diverse applications.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

Processes, Jun 25, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Computers, materials & continua/Computers, materials & continua (Print), 2024

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Opti... more This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm&#39;s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard&#39;s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard&#39;s retreat to the treetops after feeding. To assess FLO&#39;s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO&#39;s performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO&#39;s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

An overview of some single machine scheduling problems: polynomial algorithms, complexity and approximability

arXiv (Cornell University), May 29, 2024

Since the publication of the first scheduling paper in 1954, a huge number of works dealing with ... more Since the publication of the first scheduling paper in 1954, a huge number of works dealing with different types of single machine problems appeared. They addressed many heuristics and enumerative procedures, complexity results or structural properties of certain problems. Regarding surveys, often particular subjects like special objective functions are discussed, or more general scheduling problems were surveyed, where a substantial part is devoted to single machine problems. In this paper we present some results on polynomial algorithms, complexity and approximation issues, where the main focus is on results, which have been published during the last decades in papers, where at least one of the first two authors of this paper was involved. We hope that the reviewed results will stimulate further investigation in related research fields.

Special Issue “Recent Advances of Discrete Optimization and Scheduling”

Mathematics, Mar 8, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

Ensemble Heuristic–Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data

Algorithms, Jan 13, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more

LX-BBSCA: Laplacian biogeography-based sine cosine algorithm for structural engineering design optimization

AIMS mathematics, Dec 31, 2022

Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications

CMES - Computer Modeling in ENgineering & Science, 2024

This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired b... more This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine's adeptness in locating carrion and pursuing live prey. The algorithm's uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA's robust performance in exploration, exploitation, and maintaining a balance between these phases throughout the search process. Compared to twelve established metaheuristic algorithms, WoOA consistently demonstrates a superior performance across diverse benchmark functions. Statistical analyses, including paired t-tests, Friedman test, and Wilcoxon rank-sum tests, validate WoOA's significant competitive edge over its counterparts. Additionally, WoOA's practical applicability is illustrated through its successful resolution of twentytwo constrained scenarios from the CEC 2011 suite and four complex engineering design challenges. These applications underscore WoOA's efficacy in tackling real-world optimization challenges, further highlighting its potential for widespread adoption in engineering and scientific domains.

Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms

Algorithms, 2024

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) sys... more In this paper, we consider some problems that arise in connected and autonomous vehicle
(CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore,
scheduling solution algorithms can be used as a part of solution algorithms for real-world problems.
For four variants of such problems, mathematical models and solution algorithms are presented. In
particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV
scheduling problems are considered in the literature for the first time. More complicated NP-hard
scheduling problems related to CAVs can be considered in the future.

Fossa Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Engineering Applications

International Journal of Intelligent Engineering and Systems, 2024

This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Al... more This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Algorithm (FOA), which emulates the natural hunting behavior of the fossa in its habitat. FOA draws its core inspiration from the two-stage hunting technique of the fossa, involving an initial attack on a spotted lemur followed by a pursuit through the trees. The theoretical framework of FOA is elaborated, and its implementation is mathematically modeled in two distinct phases: (i) exploration, which simulates the fossa's positional adjustments during the initial attack on the lemur, and (ii) exploitation, which models the positional changes of the fossa during the chase. The efficacy of FOA is tested against twenty-two constrained optimization problems from the CEC 2011 test suite, as well as four engineering design challenges. The optimization results demonstrate FOA's strong capabilities in both exploration and exploitation, maintaining a balance that facilitates convergence to optimal solutions. FOA's performance is benchmarked against twelve established algorithms, showing that it consistently outperforms its competitors by delivering superior results and ranking as the top optimizer in most of the evaluated functions. These findings indicate that FOA is highly effective in addressing optimization tasks in real-world scenarios.

GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

Mathematical Biosciences and Engineering, 2024

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective to... more Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of
6848
Mathematical Biosciences and Engineering Volume 21, Issue 8, 6847-6869.
the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%.

Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

Computers, Materials & Continua, 2024

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Opti... more This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird's kleptoparasitic behavior in two distinct phases: exploration and exploitation. During the exploration phase, the algorithm searches for new potential solutions across a broad area, akin to the frigatebird scouting for vulnerable seabirds. In the exploitation phase, the algorithm fine-tunes the solutions, similar to the frigatebird focusing on a single target to secure its meal. To evaluate MFO's performance, the algorithm is tested on twentythree standard benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The results from these evaluations highlight MFO's proficiency in balancing exploration and exploitation throughout the optimization process. Comparative studies with twelve well-known metaheuristic algorithms demonstrate that MFO consistently achieves superior optimization results, outperforming its competitors across various metrics. In addition, the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications, thereby validating its practical utility and robustness.

Home health care planning with considering flexible starting/ending points and service features

arXiv (Cornell University), May 2, 2022

One of the recently proposed strategies in health systems is providing services to patients at ho... more One of the recently proposed strategies in health systems is providing services to patients at home, improving the service quality, besides reducing the health system costs. In the real world, some services, such as biological tests or blood sampling, force the nurses to start or end his/her route from/at the laboratory instead of the depot, changing the whole optimal planning. The effect of these special service requirements and features has not been considered so far. In this study, a new mathematical model is suggested considering the flexibility of starting/ending places of each nurse's route according to the specific characteristics of each service. Then several sets of problems in various sizes are solved using the proposed model, where the results confirm the efficiency of the proposed approach. In addition, some sensitivity analyses are performed on the parameters of the required features of the services, followed by some managerial insights and directions for future studies.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

by Frank Werner and Carmen García-Mata

Processes, 2024

Scheduling and optimization have a central place in the research area of computing because it is ... more Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics.

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Computers, Materials & Continua, 2024

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Opti... more This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm's core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard's sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard's retreat to the treetops after feeding. To assess FLO's efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO's performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO's superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Special Issue "Recent Advances of Discrete Optimization and Scheduling"

by Frank Werner and Bertrand M . T . Lin

Mathematics, 2024

This is the Editorial of the special issue with 10 published research papers.

Special issue "Discrete optimization: Theory, algorithms and new applications"

Mathematics, 2024

This is the Editorial for the special issue with 21 published papers.

A permutation-based neighborhood for the blocking job-shop problem with total tardiness minimization

Material Handling Tools for a Discrete Manufacturing System: A Comparison of Optimization and Simulation

These are the slides from the plenary talk at the 2017 International Conference on Circuits, Syst... more

Slides of talk at Optima2012 in Costa de Caparica

by Frank Werner and Yuri Sotskov

 Szpigel (1973): B&B algorithm, results for 5 sections and 10 trains  Cai and Goh (1994): greed... more  Szpigel (1973): B&B algorithm, results for 5 sections and 10 trains  Cai and Goh (1994): greedy algorithm  Carey and Lockwood (1995): binary mixed integer programming model  Mladenovic and Cangalovic (2007): constraint programming approach  Zhou and Zhong (2007): B&B algorithm, resource-constrained project scheduling problem  Liu and Kozan (2011): no-wait condition for prioritized trains, recursive procedure  Sotskov and Gholami (2012): shifting bottleneck procedure

A FPTAS for Minimizing Positive Half-Product

The problem of minimizing a quadratic function of boolean variables, which we call PHP (positive ... more The problem of minimizing a quadratic function of boolean variables, which we call PHP (positive half-product), is formulated. A fully polynomial time approximation scheme (FPTAS) for PHP is derived. Several NP-hard scheduling problems can be formulated in terms of PHP. In this presentation, we concentrate on a single machine scheduling problem with controllable job processing times to minimize a linear combination of the total weighted job completion time and the total weighted processing time compression.

Metaheuristics for Late Work Minimization in Two-Machine Flow Shop with Common Due Date

Lecture Notes in Computer Science / Artificial Intelligence, Vol. 3698, Springer, 2005, 222 - 234., 2005

In this paper, metaheuristic approaches for the weighted late work minimization in the two-machin... more In this paper, metaheuristic approaches for the weighted late work minimization in the two-machine flow shop problem with a common due date (F2 | dj=d | Yw) are presented. The late work performance measure estimates the quality of a solution with regard to the duration of the late parts of jobs not taking into account the quantity of the delay for the fully late activities. Since, the problem mentioned is known to be NP-hard, three trajectory based methods, namely simulated annealing, tabu search and variable neighborhood search were designed and compared to an exact approach and a list scheduling algorithm.

Scheduling under uncertainty

Proceedings of the 13th International Conference on Project Management and Scheduling (PMS2012), Leuven / Belgium, 1 - 4 April 2012, 67 - 70.

Scheduling under Uncertainty

Slides of Plenary Talk at the 13th International Conference on Project Management and Scheduling ... more

Hierarchical Scheduling of Mobile Robots in Production-Transportation Supply Chains

In this paper we propose a decomposition approach that hierarchically integrates the batching and... more In this paper we propose a decomposition approach that hierarchically integrates the batching and local search heuristics in manufacturing scheduling. The problem comprises two main interrelated stages embedded in any production-transportation supply chain, namely (i) scheduling processing of raw materials and robot's transportation operation within each individual cell, and (ii) scheduling of transportation and distribution of batches of semi-finished products between cells. Several efficient heuristic algorithms are proposed. This work has been motivated by a real-life problem of production planning for a CIM system for manufacturing of multi-component products served by robots.

Flexible Planning Model for a High Tech Company with High Volume - High Mixture

Within an electrical test planning system in the semiconductor industry, various models that allo... more Within an electrical test planning system in the semiconductor industry, various models that allow a better utilization of installed capacity were developed. The workflow in the plant and the information flow in the electrical test planning area were analyzed in order to minimize setup times on the equipment. A paradigm shift is proposed, with which planning is done at the product family level instead of at the level of the part number, always starting with priority products required by the market.

МЕТОД «НАПОЛНЕНИЯ МНОЖЕСТВ» РЕШЕНИЯ ЗАДАЧ ТЕОРИИ РАСПИСАНИЙ ДЛЯ ОДНОГО ПРИБОРА

MATHEMATICAL MODELS AND HEURISTIC ALGORITHMS FOR SCHEDULING IDENTICAL PARALLEL MACHINES WITH A SERVER: A SURVEY

by Frank Werner and Svetlana Kravchenko

In this paper, the problem of scheduling jobs on a set of identical parallel machines with a sing... more In this paper, the problem of scheduling jobs on a set of identical parallel machines with a single server (robot) is considered. Models having these features are common in network computing. We survey recent advances in the development of mathematical models and heuristic algorithms for such problems. The major focus is on own results by the authors obtained in the last years.

Simulation and Modeling Methodologies, Technologies and Applications 13th International Conference, SIMULTECH 2023 Rome, Italy, July 12–14, 2023 Revised Selected Papers

Springer, 2024

This edited book contains the extended version of selected papers from the SIMULTECH confernce in... more

2022 And 2023 Selected Papers from Algorithms Editorial Board Members

mdpi books, 2024

This is the print version of a special issue with the same title of the journal Algorithms. The p... more

Recent Advances of Discrete Optimization and Scheduling

by Frank Werner and ALEXANDER LAZAREV

mdpi books, 2024

This is the print version of a special issue 'recent Advances of Discrete Optimization and Schedu... more

Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022)

Springer Nature, Lecture Notes in Networks and Systems, Vol. 780, 2023

This present book includes a set of selected best extended papers from the 12th International Con... more This present book includes a set of selected best extended papers from the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022), that was held in Lisbon, Portugal, from July 14 to 16, 2022. The conference brought together researchers, engineers and practitioners interested in methodologies and applications of modeling and simulation. New and innovative solutions are reported in this book. A selection was made after the conference, based also on the conference chairs assessment, reviewers’ assessment, quality of presentation and audience interest, so that this book includes the extended and revised versions of the very best papers of the conference. New and innovative solutions are reported in this book.

Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023)

ScitePress, 2023

These are the proceedings of the SIMULTECH 2023 conference in Rome / Itly (July 12 - 14, 2023).

Scheduling: Algorithms and Applications

MDPI Books, 2023

This is the printed edition of a special issue with the same title published in Algorithns. The b... more

Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021)

by Frank Werner, Gerd Wagner, and Floriano De Rango

Springer, 2023

The present book includes a set of selected papers from the 11th International Conference on Simu... more The present book includes a set of selected papers from the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021) that was held as an online event, from July 7 to 9, 2021. The conference brought together researchers and practitioners interested in methodologies and applications of modeling and simulation. New and innovative solutions are reported in this book. A selection was made after the conference, based also on the conference chairs assessment, reviewers’ assessment, quality of presentation and audience interest, so that this book includes the extended and revised versions of the very best papers of the conference.

Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022)

ScitePress, 2022

This book contains the proceedings of the 12th International Conference on Simulation and Modelin... more This book contains the proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022), held in Lisbon, Portugal from 14– 16 July, 2022. SIMULTECH 222 is sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and held in cooperation with the ACM SIGSIM - Special Interest Group on Simulation and Modeling, the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), the European Council for Modeling and Simulation (ECMS), the European Training and Simulation Association (ETSA), the Federation of Asia Simulation Societies (ASIASIM) and the Japan Society for Simulation Technology (JSST). This conference, which is the twelfth in a row, has again met with much interest of the research community worldwide, which is the best proof that, first, the idea of the SIMULTECH conferences that has been conceived some years ago by a group of dedicated people with vision has been a proper choice. And, second, that the research and scholarly community working in the field of broadly perceived simulation has responded with great interest in spite of such an abundance of conferences on all possible topics that we presently face. SIMULTECH 2022 has again proven that the high expectations of the “founding fathers” of this conference series has been fulfilled, and the interest of the research and scholarly community has been not only maintained, but even amplified as, in addition to regular participants, many new people have submitted results of their new research. As in the previous editions, the participation of younger scientists and engineers is a very important factor and very highly encouraged. This year we have again succeeded to bring together top researchers, engineers and practitioners interested in methodologies and applications of modeling and simulation from all parts of the world. The accepted papers demonstrate new and innovative methodologies and solutions. They also highlight technical issues, challenges, and trends in this field. The high quality of the SIMULTECH 2022 program is enhanced by three distinguished keynote lectures, delivered by renowned international experts in their fields: Frank Dignum (Umeå University, Sweden), Benjamin Uekermann (University of Stuttgart, Germany) and Andrea D'Ambrogio (Università di Roma "Tor Vergata", Italy). SIMULTECH 2022 received 52 paper submissions from 23 countries in all continents, of which only 28% have been accepted as full papers, showing the intention of preserving a high-quality forum for the next editions of this conference. A double blind peer paper review was performed by the International Program Committee members, all of them recognized in at least one of the main conference topic areas, with an invaluable help by leading experts in the fields. As in previous editions of the Conference, based on the reviewer’s evaluations and the presentations, selected authors from the conference will be invited to submit extended versions of their papers for a book that will be published by Springer with the best papers of SIMULTECH 2022. Building an interesting and successful program for the conference requires the dedicated efforts of many people; we thank them all. Firstly, we must thank the authors, whose research and development efforts are recorded here. Secondly, we thank the members of the International Program Committee and additional reviewers for their diligence and expert reviewing. Finally, we would like to thank the invited distinguished keynote speakers for their invaluable contribution and for taking the time to synthesize and deliver their great talks. A successful conference involves more than paper presentations alone. It is also a meeting place, where ideas about new research projects and other ventures are discussed and debated. Therefore, a social event including a conference dinner/banquet has been planned for evening of July 14th in order to promote this kind of social networking. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly capable and energetic team. We hope you find the program of SIMULTECH 2022 interesting and inspiring for your future research, and offering helpful references in future works. We wish you all a constructive conference and we hope to meet you next year for SIMULTECH 2023. Details for the next edition will soon be available at https://simultech.scitevents.org/

Production Planning and Scheduling for Lot Processing

Cambridge Scholars Publishing, 2022

This book is dedicated to questions of production planning and scheduling activities both in gene... more This book is dedicated to questions of production planning and scheduling activities both in general and in semiconductor manufacturing environments, which have the characteristics of high volume and high mixture. It explores topics such as shop models, work-in-process management, the treatment of setup times, basic techniques of lot batching and splitting, lot sizing and group technology approaches, as well as rescheduling questions. A number of directions for further research is suggested in the book, and a broad collection of references is provided.

The 1st International Electronic Conference on Algorithms

MDPI books, 2022

This is a printed edition of the special issue in Computer Sciences and Mathematics Forum with th... more

Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

by Frank Werner and Floriano De Rango

SIMULTECH 2021, 2021

This book contains the proceedings of the 11th International Conference on Simulation and Modelin... more This book contains the proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021), which is sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and held in cooperation with the ACM SIGSIM - Special Interest Group on Simulation and Modeling, the Institute of Engineering and Management (IEM), the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), the European Council for Modeling and Simulation (ECMS), the European Training and Simulation Association (ETSA) and the Bulgarian Modeling and Simulation Association (BULSIM). SIMULTECH 2021 is held as an online web-based event, due to the COVID-19 pandemic, from 7 – 9 July, 2021. This conference, which is the eleventh in a row, has again met with much interest of the research community worldwide, which is the best proof that, first, the idea of the SIMULTECH conferences that has been conceived some years ago by a group of dedicated people with vision has been a proper choice. And, second, that the research and scholarly community working in the field of broadly perceived simulation has responded with great interest in spite of such an abundance of conferences on all possible topics that we presently face. SIMULTECH 2021 has again proven that the high expectations of the “founding fathers” of this conference series has been fulfilled, and the interest of the research and scholarly community has been not only maintained, but even amplified as, in addition to regular participants, many new people have submitted results of their new research. As in the previous editions, the participation of younger scientists and engineers is a very important factor and very highly encouraged. This year we have again succeeded to bring together top researchers, engineers and practitioners interested in methodologies and applications of modeling and simulation from all parts of the world. The accepted papers demonstrate new and innovative methodologies and solutions. They also highlight technical issues, challenges, and trends in this field. The high quality of the SIMULTECH 2021 program is enhanced by three distinguished keynote lectures, delivered by renowned international experts in their fields: Gregory Monakhov (AnyLogic, United States), Marlon Dumas (University of Tartu, Estonia) and Peter Fritzson (Linköping University, Sweden). SIMULTECH 2021 received 64 paper submissions from 30 countries in all continents, of which only 26% have been accepted as full papers, showing the intention of preserving a high-quality forum for the next editions of this conference. A double blind peer paper review was performed by the International Program Committee members, all of them recognized in at least one of the main conference topic areas, with an invaluable help by leading experts in the fields. Building an interesting and successful program for the conference requires the dedicated efforts of many people; we thank them all. Firstly, we must thank the authors, whose research and development efforts are recorded here. Secondly, we thank the members of the International Program Committee and additional reviewers for their diligence and expert reviewing. We also gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for SIMULTECH 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. Finally, we would like to thank the invited distinguished keynote speakers for their invaluable contribution and for taking the time to synthesize and deliver their great talks.

Multicriteria Optimization: Pareto-Optimality and Threshold Optimality

IntechOpen, 2020

Multi-criteria optimization problems naturally arise in practice when there is no single criterio... more Multi-criteria optimization problems naturally arise in practice when there is no single criterion for measuring the quality of a feasible solution. Since different criteria are contradictory, it is difficult and often impossible to find a single feasible solution that is good for all the criteria. Hence, some compromise is needed. As such, this book examines the commonly accepted compromise of the traditional Pareto-optimality approach. It also proposes one new alternative approach for generating feasible solutions to multi-criteria optimization problems. Finally, the book presents two chapters on the existing solution methods for two real-life, multi-criteria optimization problems.
(OPEN ACCESS BOOK)

Advances and Novel Approaches in Discrete Optimization

MDPI, 2020

This is the print version of a special issue with the same title in the journal Mathematics. The ... more

Graph-Theoretic Problems and Their New Applications

MDPI, 2020

This is the printed edition of a special issue with the same title in the mdpi journal Mathematic... more

Exact and Heuristic Scheduling Algorithms

MDPI, 2020

This is the Printed Edition of a Special Issue with the same title in the journal Algorithms. It ... more

Algorithms for Scheduling Problems

This is a printed edition of a special issue on Algorithms for Scheduling Problems published in t... more

Scheduling under Uncertainty: Theory and Algorithms

by Yuri Sotskov and Frank Werner

Belarusian Science, 2010, 328 p., ISBN 978-985-08-1173-8

Mathematics of Economics and Business

Routledge, UK / USA, 2006

The Ebook can be now FREELY DOWNLOADED (open archive), e.g. under https://www.taylorfrancis.c... more The Ebook can be now FREELY DOWNLOADED (open archive), e.g. under

https://www.taylorfrancis.com/books/9781134319312

Taylor & Francis group. Now the Ebook can also bee FREELY DOWNLOADED at all KINDLE STORES OF AMAZON, e.g.
https://www.amazon.in/Mathematics-Economics-Business-Frank-Werner-ebook/dp/B000Q7ZFKW

Since it is still the first edition from 2006 without any changes, a list of identified misprints can be found at my homepage under:

http://www.math.uni-magdeburg.de/~werner/misprints.pdf .

(I hope the list is rather complete since the book was intensively used in the tutorials for my lectures since the publication in 2006).

The book is based on the lectures which the first author presented at the Otto-von-Guericke University Magdeburg for students of the Faculty of Economics and Management within a two-semester course since 1997. Here only the preface is presented. Of course, I appreciate any feedback.

Sequencing and Scheduling with Inaccurate Data

Nova Science Publishers, Hauppauge / New York, 2014

PREFACE The usual assumption that the scheduling parameters (such as processing times, setup time... more PREFACE The usual assumption that the scheduling parameters (such as processing times, setup times, release dates or due dates) are exactly known before scheduling restricts practical aspects of scheduling theory since it is often not valid for real-world processes. These problem data may be given either as random variables, fuzzy numbers, or they may be uncertain. As a consequence, during the last decades, four different approaches for dealing with scheduling problems with inaccurate data have been developed. In a stochastic approach, particular scheduling parameters (e.g. the processing times) are assumed to be random variables with known probability distributions. In a fuzzy approach, the job processing times or other numerical data are given as fuzzy numbers. Fuzzy scheduling techniques either fuzzify directly existing scheduling rules or solve a mathematical programming problem to determine an optimal schedule. For problems with uncertain (e.g. interval) processing times, there basically exist two approaches , namely a robust approach and a stability approach. In the robust approach, a solution is chosen using a particular robust criterion, e.g. the min-max criterion or the min-max regret criterion. In the former case, one determines a solution minimizing the largest cost over all possible scenarios. The min-max regret criterion is a less conservative criterion, where one determines a solution minimizing the largest deviation from the optimum over all possible scenarios. The strategy of the stability approach consists in separating the structural input data (e.g. precedence and capacity constraints) from the numerical input data. This approach is based on a stability analysis, a multi-stage scheduling decision framework and the solution concept of a minimal dominant set. This book consists of four parts. Each of this part deals with one of the approaches mentioned above. It starts with a survey chapter and contains between two and three further chapters. Part I deals with the stochastic approach and contains three chapters. In Chapter 1, Cai, Wu, Zhang and Zhou give an overview on scheduling with the stochastic approach. This chapter surveys the literature on scheduling problems with random data, e.g. processing times, due dates and patterns of machine breakdowns. Both problems with regular and non-regular optimization criteria are considered. In Chapter 2, Vredeveld considers a combination of online and stochastic scheduling. For this combined model, known as stochastic online scheduling, it is assumed that the scheduler has no knowledge of future jobs to arrive and when a job arrives, the data of this job is only known via random variables. Various stochastic online scheduling policies are presented and their approximation guarantees are proved. In Chapter 3, stochastic scheduling problems are considered with general distributions of the activity durations. The method presented estimates the distribution of the makespan of an activity

A Refresher Course in Mathematics

Bookboon Publishers / Denmark, 2016

The complete ad-supported book (284 pages) can be freely downloaded from the publisher's websit... more The complete ad-supported book (284 pages) can be freely downloaded from
the publisher's website (bookboon) under:

http://bookboon.com/en/a-refresher-course-in-mathematics-ebook

It is based on several courses which I taught for beginners of a study at several affiliations in Magdeburg / Germany.

According to the desire of the publisher, there is also a multiple choice test together with the right answers downloadable from the above website.

A Brief Look at Multi-Criteria Problems: Multi-Threshold Optimization versus Pareto-Optimization

IntechOpen

Multi-objective optimization problems are important as they arise in many practical circumstances... more Multi-objective optimization problems are important as they arise in many practical circumstances. In such problems, there is no general notion of optimality, as there are different objective criteria which can be contradictory. In practice, often there is no unique optimality criterion for measuring the solution quality. The latter is rather determined by the value of the solution for each objective criterion. In fact, a practitioner seeks for a solution that has an acceptable value of each of the objective functions and, in practice, there may be different tolerances to the quality of the delivered solution for different objective functions: for some objective criteria, solutions that are far away from an optimal one can be acceptable. Traditional Pareto-optimality approach aims to create all non-dominated feasible solutions in respect to all the optimality criteria. This often requires an inadmissible time. Besides, it is not evident how to choose an appropriate solution from the Pareto-optimal set of feasible solutions, which can be very large. Here we propose a new approach and call it multi-threshold optimization setting that takes into account different requirements for different objective criteria and so is more flexible and can often be solved in a more efficient way.

On the Recursive Representation of the Permutation Flow Shop Scheduling Problem and Its Extension

preprints.org, 2024

In this paper, we propose a formulation of the permutation flow shop scheduling problem (PFSP) by... more In this paper, we propose a formulation of the permutation flow shop scheduling problem (PFSP) by special recursive functions and show its equivalence to the existing classical formulation. Equivalence is understood in the sense that both ways of defining the problem describe the same set of feasible schedules for each pair of job and machine numbers. In fact, this paper attempts to use the apparatus of recursive functions for scheduling problems. Moreover, the predicate and is introduced into the problem, and the problem PFS is expanded from a chain of machines to an acyclic graph. To construct an optimal schedule, the branch and bound method is considered based on the recursive function, which is included into the corresponding job permutation algorithm. The complexity of the optimization algorithm does not increase compared to the non-recursive PFS problem. Finally, we present some initial computational results.

A Completely Different and Innovative Bio-Inspired Metaheuristic Approach for Effectively Solving Complex Optimization Problems Across Various Domains

mdpi peprints, 2024

In this paper, a completely different metaheuristic algorithm called the Orangutan Optimization A... more In this paper, a completely different metaheuristic algorithm called the Orangutan Optimization Algorithm (OOA) is introduced, which replicates the complex and adaptive behaviors of Orangutans in their natural habitat. The main inspiration for OOA lies in the strategic foraging techniques of Orangutans, alongside their highly developed nesting abilities. More words are used to provide a detailed theoretical explanation of OOA, followed by a comprehensive modeling of the algorithm's implementation in two key phases: exploration and exploitation. These phases are mathematically structured to ensure the balance required for optimal search processes. The performance of OOA is rigorously evaluated using twenty-nine benchmark functions from the CEC 2017 test suite, with problem dimensions of 10, 30, 50, and 100. More sentences are included to elaborate on the optimization outcomes, which reveal that OOA effectively balances the exploration and exploitation stages, yielding suitable and competitive solutions across the benchmark functions. Additionally, to further validate its quality, the results from OOA are compared against twelve other well-established metaheuristic algorithms. The analysis of these simulation results demonstrates that OOA consistently outperforms competing algorithms by achieving superior results across most benchmark functions. Furthermore, to assess its real-world applicability, OOA is implemented on a set of twenty-two constrained optimization problems drawn from the CEC 2011 test suite. The extended simulation results clearly indicate that OOA is highly efficient in solving real-world optimization challenges, consistently delivering a superior performance when compared to other competitor algorithms.

An Overview of Some Single Machine Scheduling Problems: Polynomial Algorithms, Complexity and Approximability

Arxiv Preprints, 2024

Since the publication of the first scheduling paper in 1954, a huge number of works dealing with ... more Since the publication of the first scheduling paper in 1954, a huge number of works dealing with different types of single machine problems appeared. They addressed many heuristics and enumerative procedures, complexity results or structural properties of certain problems. Regarding surveys, often particular subjects like special objective functions are discussed, or more general scheduling problems were surveyed, where a substantial part is devoted to single machine problems. In this paper we present some results on polynomial algorithms, complexity and approximation issues, where the main focus is on results, which have been published during the last decades in papers, where at least one of the first two authors of this paper was involved. We hope that the reviewed results will stimulate further investigation in related research fields.

Nature-Inspired Metaheuristic Algorithm for Solving Optimization Problems

by Frank Werner and Osama al-Baik

mdpi preprints, 2024

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Opt... more This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws inspiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

A multi-objective sustainable planning for a real hazardous waste production problem

arxiv preprints, 2022

A significant amount of hazardous waste generated from health sectors and industrial processes ha... more A significant amount of hazardous waste generated from health sectors and industrial processes has posed a major threat to human health by causing environmental issues and contamination of air, soil, and water resources. This paper presents a multi-objective mixed-integer nonlinear programming (MINLP) formulation for a sustainable hazardous waste location-routing problem. The location of the facilities and routing decisions for transporting hazardous waste and the waste residue is considered to design a suitable waste collection system. The presented model simultaneously minimizes the total costs of the waste management system, total risks from transportation and facilities, along with CO 2 emissions. A real-world case study is presented to illustrate the applicability of the proposed model. To illustrate the significance of sustainability, the results of the original model are compared with the results of the model without considering sustainability. It indicates that, under the condition when sustainability is not taken into account, total cost, transportation, and site risk along with CO 2 emission increased, which in turn demonstrated the importance of sustainability. Furthermore, the managerial insights gained from the optimal results would enable the managers to make better decisions in the hazardous waste management system.