Under a reasonable assumption, we derive an analytical approach that verifies uniqueness of the o... more Under a reasonable assumption, we derive an analytical approach that verifies uniqueness of the optimal solution for stochastic inventory models with defective items. Our approach implies a robust method to find the optimal solution.
This study focuses on a class of single-machine scheduling problems with a common due date where ... more This study focuses on a class of single-machine scheduling problems with a common due date where the objective is to minimize the total earlinesstardiness penalty for the jobs. A sequential exchange approach utilizing a job exchange procedure and three previously established ...
Employing maintenance threshold plays a critical step in determining an optimal maintenance polic... more Employing maintenance threshold plays a critical step in determining an optimal maintenance policy for an offshore wind system to reduce maintenance costs while increasing system reliability. Considering the limited works on this topic, we propose a two-stage procedure to determine the optimal maintenance thresholds for multiple components of an offshore wind power system in order to minimize maintenance costs while achieving the highest possible system reliability. First, using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds of individual components where the cost of maintenance and the rate of failure are critical. Then, fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold considering all components. A variety of factors including weather conditions, system reliability, power generation losses, and electricity market price are carefully considered to enhance the system’s reliability and reduce t...
ABSTRACT The order acceptance and scheduling (OAS) problem is important in make-to-order producti... more ABSTRACT The order acceptance and scheduling (OAS) problem is important in make-to-order production systems in which production capacity is limited and order delivery requirements are applied. This study proposes a multi-initiator simulated annealing (MSA) algorithm to maximize the total net revenue for the permutation flowshop scheduling problem with order acceptance and weighted tardiness. To evaluate the performance of the proposed MSA algorithm, computational experiments are performed and compared for a benchmark problem set of test instances with up to 500 orders. Experimental results reveal that the proposed heuristic outperforms the state-of-the-art algorithm and obtains the best solutions in 140 out of 160 benchmark instances.
This work addresses four single-machine scheduling problems (SMSPs) with learning effects and var... more This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the already processed jobs. Maintenance activity must start before a deadline and its duration increases with the starting time of the maintenance activity. This work proposes a polynomial-time algorithm for optimally solving two SMSPs to minimize the total completion time and the total tardiness with a common due date.
IEEE International Conference on Networking, Sensing and Control, 2004, 2004
In modern prosperous cities, there is a great need for advanced parking assistant systems to redu... more In modern prosperous cities, there is a great need for advanced parking assistant systems to reduce the hustle for the drivers. Such systems are a kind of intelligent transportation system, which provides drivers with parking information. Existing parking information systems usually ignore the parking price factor and do not automatically provide optimal car parks matching drivers' demand. Currently, the parking price has no negotiable space; consumers lose their bargaining position to obtain better and cheaper parking for their purposes. This paper takes into account the negotiable space on parking prices, makes a strategic decision by adopting the intelligent agent system, and then selects the optimal car park for the driver. These modern intelligent agents have the capability of planning, mobility, execution monitoring, coordination, etc. These characteristics can be utilized to build an integrated parking assistant system. The autonomous coordination activities challenge traditional approaches and call for new paradigms and supporting middleware. An agent-based coordination network was proposed to truly bring benefit to drivers and carpark operators.
ABSTRACT To date, the topic of unrelated parallel machine scheduling problems with machine-depend... more ABSTRACT To date, the topic of unrelated parallel machine scheduling problems with machine-dependent and job sequence-dependent setup times has received relatively little research attention. In this study, a hybrid artificial bee colony (HABC) algorithm is presented to solve this problem with the objective of minimizing the makespan. The performance of the proposed HABC algorithm was evaluated by comparing its solutions to state-of-the-art metaheuristic algorithms and a high performing artificial bee colony (ABC)-based algorithm. Extensive computational results indicate that the proposed HABC algorithm significantly outperforms these best-so-far algorithms. Since the problem addressed in this study is a core topic for numerous industrial applications, this article may help to reduce the gap between theoretical progress and industrial practice.
ABSTRACT This research addresses a single machine scheduling problem with uncertain processing ti... more ABSTRACT This research addresses a single machine scheduling problem with uncertain processing times and sequence-dependent setup times represented by intervals. Our objective is to obtain a robust schedule with the minimum absolute deviation from the optimal makespan in the worst-case scenario. The problem is reformulated as a robust traveling salesman problem (RTSP), whereby a property is utilized to efficiently identify worst-case scenarios. A local search-based heuristic that incorporates this property is proposed to solve the RTSP, along with a simulated annealing-based implementation. The effectiveness and efficiency of the proposed heuristic are compared to those of an exact solution method in the literature.
Under a reasonable assumption, we derive an analytical approach that verifies uniqueness of the o... more Under a reasonable assumption, we derive an analytical approach that verifies uniqueness of the optimal solution for stochastic inventory models with defective items. Our approach implies a robust method to find the optimal solution.
This study focuses on a class of single-machine scheduling problems with a common due date where ... more This study focuses on a class of single-machine scheduling problems with a common due date where the objective is to minimize the total earlinesstardiness penalty for the jobs. A sequential exchange approach utilizing a job exchange procedure and three previously established ...
Employing maintenance threshold plays a critical step in determining an optimal maintenance polic... more Employing maintenance threshold plays a critical step in determining an optimal maintenance policy for an offshore wind system to reduce maintenance costs while increasing system reliability. Considering the limited works on this topic, we propose a two-stage procedure to determine the optimal maintenance thresholds for multiple components of an offshore wind power system in order to minimize maintenance costs while achieving the highest possible system reliability. First, using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds of individual components where the cost of maintenance and the rate of failure are critical. Then, fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold considering all components. A variety of factors including weather conditions, system reliability, power generation losses, and electricity market price are carefully considered to enhance the system’s reliability and reduce t...
ABSTRACT The order acceptance and scheduling (OAS) problem is important in make-to-order producti... more ABSTRACT The order acceptance and scheduling (OAS) problem is important in make-to-order production systems in which production capacity is limited and order delivery requirements are applied. This study proposes a multi-initiator simulated annealing (MSA) algorithm to maximize the total net revenue for the permutation flowshop scheduling problem with order acceptance and weighted tardiness. To evaluate the performance of the proposed MSA algorithm, computational experiments are performed and compared for a benchmark problem set of test instances with up to 500 orders. Experimental results reveal that the proposed heuristic outperforms the state-of-the-art algorithm and obtains the best solutions in 140 out of 160 benchmark instances.
This work addresses four single-machine scheduling problems (SMSPs) with learning effects and var... more This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the already processed jobs. Maintenance activity must start before a deadline and its duration increases with the starting time of the maintenance activity. This work proposes a polynomial-time algorithm for optimally solving two SMSPs to minimize the total completion time and the total tardiness with a common due date.
IEEE International Conference on Networking, Sensing and Control, 2004, 2004
In modern prosperous cities, there is a great need for advanced parking assistant systems to redu... more In modern prosperous cities, there is a great need for advanced parking assistant systems to reduce the hustle for the drivers. Such systems are a kind of intelligent transportation system, which provides drivers with parking information. Existing parking information systems usually ignore the parking price factor and do not automatically provide optimal car parks matching drivers' demand. Currently, the parking price has no negotiable space; consumers lose their bargaining position to obtain better and cheaper parking for their purposes. This paper takes into account the negotiable space on parking prices, makes a strategic decision by adopting the intelligent agent system, and then selects the optimal car park for the driver. These modern intelligent agents have the capability of planning, mobility, execution monitoring, coordination, etc. These characteristics can be utilized to build an integrated parking assistant system. The autonomous coordination activities challenge traditional approaches and call for new paradigms and supporting middleware. An agent-based coordination network was proposed to truly bring benefit to drivers and carpark operators.
ABSTRACT To date, the topic of unrelated parallel machine scheduling problems with machine-depend... more ABSTRACT To date, the topic of unrelated parallel machine scheduling problems with machine-dependent and job sequence-dependent setup times has received relatively little research attention. In this study, a hybrid artificial bee colony (HABC) algorithm is presented to solve this problem with the objective of minimizing the makespan. The performance of the proposed HABC algorithm was evaluated by comparing its solutions to state-of-the-art metaheuristic algorithms and a high performing artificial bee colony (ABC)-based algorithm. Extensive computational results indicate that the proposed HABC algorithm significantly outperforms these best-so-far algorithms. Since the problem addressed in this study is a core topic for numerous industrial applications, this article may help to reduce the gap between theoretical progress and industrial practice.
ABSTRACT This research addresses a single machine scheduling problem with uncertain processing ti... more ABSTRACT This research addresses a single machine scheduling problem with uncertain processing times and sequence-dependent setup times represented by intervals. Our objective is to obtain a robust schedule with the minimum absolute deviation from the optimal makespan in the worst-case scenario. The problem is reformulated as a robust traveling salesman problem (RTSP), whereby a property is utilized to efficiently identify worst-case scenarios. A local search-based heuristic that incorporates this property is proposed to solve the RTSP, along with a simulated annealing-based implementation. The effectiveness and efficiency of the proposed heuristic are compared to those of an exact solution method in the literature.
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Papers by Shih-Wei Lin