Qatar Foundation Annual Research Forum Volume 2013 Issue 1, 2013
Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficient... more Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficiently allocate underwater sonars to achieve a reasonable amount of coverage within a limited budget. Coverage is defined as the capability of sonars to detect threats. Though total coverage is desired, priority is given to the criticality/importance attached to the location of an area of interest on a grid-based system. Unlike other works in the literature, the developed model takes into consideration uncertainty inherent in the detection probability of sonars. Apart from issues of sonar reliability, underwater terrain, with its changing conditions, is bound to affect detection probabilities. While taking into consideration the specific physics of sonars in the model development, the model also adopts a hexagonal grid-based system to ensure more efficient placement of sonars. Based on an initially proposed mixed-integer program, a robust optimization model also is proposed to take care of uncertainties. With smaller scale problems, the model works adequately within a relatively short time period. However, large scale problems require extensive memory, taking much longer. As such, a heuristic is proposed as an alternative to the proposed model. Experimental results indicate the heuristic works effectively under most circumstances and performs less effectively under a few limited scenarios.
Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, 2014
We study a difficult real life scheduling problem encountered in oil and petrochemical industry, ... more We study a difficult real life scheduling problem encountered in oil and petrochemical industry, involving inventory and distribution operations, which requires integrated scheduling. The problem itself is NP-complete, however we show some special cases, and propose polynomial time solution methods. These could be used as a starting point for a heuristic making use of these simplified cases. This study proposes two alternative approaches for the main problem, one of them making use of one of the special cases using minimum cost flow formulation, and the other one using Benders Decomposition once the problem is reformulated to make it easier to handle. Both results show promising results and computation time. Benders Decomposition approach allows exact solutions to be found in a much faster fashion.
Qatar Foundation Annual Research Forum Proceedings, 2013
Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficient... more Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficiently allocate underwater sonars to achieve a reasonable amount of coverage within a limited budget. Coverage is defined as the capability of sonars to detect threats. Though total coverage is desired, priority is given to the criticality/importance attached to the location of an area of interest on a grid-based system. Unlike other works in the literature, the developed model takes into consideration uncertainty inherent in the detection probability of sonars. Apart from issues of sonar reliability, underwater terrain, with its changing conditions, is bound to affect detection probabilities. While taking into consideration the specific physics of sonars in the model development, the model also adopts a hexagonal grid-based system to ensure more efficient placement of sonars. Based on an initially proposed mixed-integer program, a robust optimization model also is proposed to take care of uncertainties. With smaller scale problems, the model works adequately within a relatively short time period. However, large scale problems require extensive memory, taking much longer. As such, a heuristic is proposed as an alternative to the proposed model. Experimental results indicate the heuristic works effectively under most circumstances and performs less effectively under a few limited scenarios.
We study a difficult real life scheduling problem encountered in oil and petrochemical industry, ... more We study a difficult real life scheduling problem encountered in oil and petrochemical industry, involving inventory and distribution operations, which requires integrated scheduling. The problem itself is NP-complete, however we show some special cases, and propose polynomial time solution methods. These could be used as a starting point for a heuristic making use of these simplified cases. This study proposes two alternative approaches for the main problem, one of them making use of one of the special cases using minimum cost flow formulation, and the other one using Benders Decomposition once the problem is reformulated to make it easier to handle. Both results show promising results and computation time. Benders Decomposition approach allows exact solutions to be found in a much faster fashion.
Providing surveillance to maritime infrastructures against imminent threats is studied in this pa... more Providing surveillance to maritime infrastructures against imminent threats is studied in this paper. Surveillance is achieved with the aid of optimally placed sonars. Under a budget-limited deployment strategy involving both static and mobile sonars, a new optimization model using hexagonal grid systems is proposed. Numerical results suggest our approach delivers acceptable detection coverage under a multi-period placement scheme.
Due to the undesirable implications of maritime mishaps such as ship collisions and the consequen... more Due to the undesirable implications of maritime mishaps such as ship collisions and the consequent damages to maritime property; the safety and security of waterways, ports and other maritime assets are of the utmost importance to authorities and researches. Terrorist attacks, piracy, accidents and environmental damages are some of the concerns. This paper provides a detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms. The methods are then categorized into three main groups: statistical, simulation and optimization models. Corresponding statistics of papers based on year of publication, region of case studies and methodology are also presented.
Given a marine area of interest (e.g., water-ways, ports), this paper attempts to efficiently all... more Given a marine area of interest (e.g., water-ways, ports), this paper attempts to efficiently allocate sonar sensors (utilized for under water detection) within a limited budget to achieve a reasonable amount of coverage, thereby keeping ports and waterways under surveillance against impending threats. Though total coverage of the entire area of interest is desired, priority is given to the criticality/importance attached to specific locations within the area of interest, on a grid-based system. We present two optimization models to detect potential threats by placing underwater sonars within a port or waterway of interest. The first model (mixed integer linear programming model) developed maximizes the detection coverage based on the importance attached to a sub-region. The second model is a mixed integer nonlinear programming model that is designed to achieve optimal detection based on a desired detection probability threshold. Both models satisfy a budget constraint that limits n...
In supply chain management under disruption, definition of resilience is based on many factors, a... more In supply chain management under disruption, definition of resilience is based on many factors, and a consensus is yet to be agreed upon. In addition, there is very limited quantitative research in the supply chain resiliency field. Therefore, we propose a mathematical formulation for constructing a resilient supply chain under disruption. Our goal is to design supply chain networks that are resilient under stress by incorporating the concept of node importance. For quantifying resilience, mathematical formulation is a new approach, and the objective function of the model is based on the idea of resiliency triangle and node importance. Solving this problem, allows evaluation of the resiliency of a supply chain as a whole, as well as the resilience of individual building blocks. Disruptions and recovery are stochastic processes in real life. The problem itself has non-polynomial many binary variables. Therefore, we have developed a simulation model for validating the mathematical mod...
This paper proposes a quantitative approach for measuring the resilience of the network component... more This paper proposes a quantitative approach for measuring the resilience of the network components and the network itself. This study introduces a conceptual framework featuring the ability of the network system to adopt alternative plans when a component is disrupted. As a first step toward measuring network resilience, the component resilience is defined and quantified as a function of criticality, disruption frequency, disruption impact, and recovery capability. This quantification approach reflects the effect of component level disruption on the network. Hence, it is proposed that the network resilience is measured by the resilience of network component having the lowest resilience index. Efforts to enhance network resilience often require financial resources. Therefore, an optimization model is further introduced to maximize the network resilience under budget constraint through reinforcing the weakest components in the network. The proposed approach can help decision-makers assess the network resilience status and compare with it other networks, identify and improve components with low resilience, evaluate the cost of resilience improvement, and determine the extent of enhancement that can be achieved under a given budget limitation. Numerical experiments are conducted to illustrate our methodology using a supply chain network and a power network. Both examples show that considering the ability of the network to use alternative plans can enhance network resilience and result in lower demand loss when a disruption occurs.
Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1, 2014
This study is about a two-stage stochastic LNG inventory routing and scheduling problem under wea... more This study is about a two-stage stochastic LNG inventory routing and scheduling problem under weather disruptions which can cover all contract durations including long-term, short-term and spot demand. We consider a fleet of heterogeneous LNG carriers with partial tank filling capability of cargoes to travel multiple customers in routes. In this case, as it has to satisfy numerous customers in a route, the sailing time of an assigned vessel may be longer than the time from the current transportation model which serves single demand point. Understanding the ideas of this model made us to pay attention on boil-off gas (BOG) losses during voyage so that the resulting model can be more accurate in estimating the overall shipping cost. The stochastic approach aims to use historical information to find a stable solution that withstands disruptions caused by dust storm in Persian Gulf as a test-bed. The proposed LNG scheduling model formulated as a multiple vehicle routing problem (VRP) considering weather disruptions as a random variable. Since the stochastic VRP is combinatorial optimization problem in nature, we use Monte Carlo sampling technique to overcome the computational burden while ensuring good quality of solutions. Performance of our approach was compared against the deterministic counterpart using two performance measures: expected value of perfect information (EVPI) and value of stochastic solution (VSS).
Providing surveillance to maritime infrastructures against imminent threats is studied in this pa... more Providing surveillance to maritime infrastructures against imminent threats is studied in this paper. Surveillance is achieved with the aid of optimally placed sonars. Under a budget-limited deployment strategy involving both static and mobile sonars, a new optimization model using hexagonal grid systems is proposed. Numerical results suggest our approach delivers acceptable detection coverage under a multi-period placement scheme.
This paper presents a concise review on extant literature in threat detection within the maritime... more This paper presents a concise review on extant literature in threat detection within the maritime domain with specific emphasis on solution methodologies, sensing technologies, regional affiliations of authors and research contributions related to underwater threat detection. The review attempts to provide an introductory framework on threat detection and surveillance within the maritime domain as opposed to its widely studied terrestrial counterpart. In all, over 200 engineering/science journal papers and conference proceedings were identified. Of these, a total of 136 articles are eventually chosen and reviewed. The analysis of this review suggests that opportunities abound in the research area, especially in Operations Research/ Management Science. Specifically, Sensor placement and deployment for underwater threat detection is identified as a significant sub-research area worth studying.
We consider a new biannual liquefied natural gas (LNG) ship routing and scheduling problem and a ... more We consider a new biannual liquefied natural gas (LNG) ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fleet of heterogeneous vessels. We are motivated not only by contract trend changes to shorter ones but also by technological advancesin LNG vessel design. The mutual coincidence of both transitions enables developing a new LNG shipping strategy to keep up with emerging market trend. We fi rst propose a deterministic LNG scheduling model formulated as a multiple vehicle routing problem (VRP). The model is then extended to consider BOG using a two-stage stochastic modeling approach in which BOG is a random variable. Since the VRP is typically a combinatorial optimization problem, its stochastic extension is much harder to solve. In order to overcome this computational burden, a Monte Carlo sampling optimization is used to reduce the number of scenarios in the stochas...
Qatar Foundation Annual Research Forum Volume 2013 Issue 1, 2013
Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficient... more Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficiently allocate underwater sonars to achieve a reasonable amount of coverage within a limited budget. Coverage is defined as the capability of sonars to detect threats. Though total coverage is desired, priority is given to the criticality/importance attached to the location of an area of interest on a grid-based system. Unlike other works in the literature, the developed model takes into consideration uncertainty inherent in the detection probability of sonars. Apart from issues of sonar reliability, underwater terrain, with its changing conditions, is bound to affect detection probabilities. While taking into consideration the specific physics of sonars in the model development, the model also adopts a hexagonal grid-based system to ensure more efficient placement of sonars. Based on an initially proposed mixed-integer program, a robust optimization model also is proposed to take care of uncertainties. With smaller scale problems, the model works adequately within a relatively short time period. However, large scale problems require extensive memory, taking much longer. As such, a heuristic is proposed as an alternative to the proposed model. Experimental results indicate the heuristic works effectively under most circumstances and performs less effectively under a few limited scenarios.
Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, 2014
We study a difficult real life scheduling problem encountered in oil and petrochemical industry, ... more We study a difficult real life scheduling problem encountered in oil and petrochemical industry, involving inventory and distribution operations, which requires integrated scheduling. The problem itself is NP-complete, however we show some special cases, and propose polynomial time solution methods. These could be used as a starting point for a heuristic making use of these simplified cases. This study proposes two alternative approaches for the main problem, one of them making use of one of the special cases using minimum cost flow formulation, and the other one using Benders Decomposition once the problem is reformulated to make it easier to handle. Both results show promising results and computation time. Benders Decomposition approach allows exact solutions to be found in a much faster fashion.
Qatar Foundation Annual Research Forum Proceedings, 2013
Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficient... more Given a water-terrain area of interest (waterways, ports, etc.), this paper attempts to efficiently allocate underwater sonars to achieve a reasonable amount of coverage within a limited budget. Coverage is defined as the capability of sonars to detect threats. Though total coverage is desired, priority is given to the criticality/importance attached to the location of an area of interest on a grid-based system. Unlike other works in the literature, the developed model takes into consideration uncertainty inherent in the detection probability of sonars. Apart from issues of sonar reliability, underwater terrain, with its changing conditions, is bound to affect detection probabilities. While taking into consideration the specific physics of sonars in the model development, the model also adopts a hexagonal grid-based system to ensure more efficient placement of sonars. Based on an initially proposed mixed-integer program, a robust optimization model also is proposed to take care of uncertainties. With smaller scale problems, the model works adequately within a relatively short time period. However, large scale problems require extensive memory, taking much longer. As such, a heuristic is proposed as an alternative to the proposed model. Experimental results indicate the heuristic works effectively under most circumstances and performs less effectively under a few limited scenarios.
We study a difficult real life scheduling problem encountered in oil and petrochemical industry, ... more We study a difficult real life scheduling problem encountered in oil and petrochemical industry, involving inventory and distribution operations, which requires integrated scheduling. The problem itself is NP-complete, however we show some special cases, and propose polynomial time solution methods. These could be used as a starting point for a heuristic making use of these simplified cases. This study proposes two alternative approaches for the main problem, one of them making use of one of the special cases using minimum cost flow formulation, and the other one using Benders Decomposition once the problem is reformulated to make it easier to handle. Both results show promising results and computation time. Benders Decomposition approach allows exact solutions to be found in a much faster fashion.
Providing surveillance to maritime infrastructures against imminent threats is studied in this pa... more Providing surveillance to maritime infrastructures against imminent threats is studied in this paper. Surveillance is achieved with the aid of optimally placed sonars. Under a budget-limited deployment strategy involving both static and mobile sonars, a new optimization model using hexagonal grid systems is proposed. Numerical results suggest our approach delivers acceptable detection coverage under a multi-period placement scheme.
Due to the undesirable implications of maritime mishaps such as ship collisions and the consequen... more Due to the undesirable implications of maritime mishaps such as ship collisions and the consequent damages to maritime property; the safety and security of waterways, ports and other maritime assets are of the utmost importance to authorities and researches. Terrorist attacks, piracy, accidents and environmental damages are some of the concerns. This paper provides a detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms. The methods are then categorized into three main groups: statistical, simulation and optimization models. Corresponding statistics of papers based on year of publication, region of case studies and methodology are also presented.
Given a marine area of interest (e.g., water-ways, ports), this paper attempts to efficiently all... more Given a marine area of interest (e.g., water-ways, ports), this paper attempts to efficiently allocate sonar sensors (utilized for under water detection) within a limited budget to achieve a reasonable amount of coverage, thereby keeping ports and waterways under surveillance against impending threats. Though total coverage of the entire area of interest is desired, priority is given to the criticality/importance attached to specific locations within the area of interest, on a grid-based system. We present two optimization models to detect potential threats by placing underwater sonars within a port or waterway of interest. The first model (mixed integer linear programming model) developed maximizes the detection coverage based on the importance attached to a sub-region. The second model is a mixed integer nonlinear programming model that is designed to achieve optimal detection based on a desired detection probability threshold. Both models satisfy a budget constraint that limits n...
In supply chain management under disruption, definition of resilience is based on many factors, a... more In supply chain management under disruption, definition of resilience is based on many factors, and a consensus is yet to be agreed upon. In addition, there is very limited quantitative research in the supply chain resiliency field. Therefore, we propose a mathematical formulation for constructing a resilient supply chain under disruption. Our goal is to design supply chain networks that are resilient under stress by incorporating the concept of node importance. For quantifying resilience, mathematical formulation is a new approach, and the objective function of the model is based on the idea of resiliency triangle and node importance. Solving this problem, allows evaluation of the resiliency of a supply chain as a whole, as well as the resilience of individual building blocks. Disruptions and recovery are stochastic processes in real life. The problem itself has non-polynomial many binary variables. Therefore, we have developed a simulation model for validating the mathematical mod...
This paper proposes a quantitative approach for measuring the resilience of the network component... more This paper proposes a quantitative approach for measuring the resilience of the network components and the network itself. This study introduces a conceptual framework featuring the ability of the network system to adopt alternative plans when a component is disrupted. As a first step toward measuring network resilience, the component resilience is defined and quantified as a function of criticality, disruption frequency, disruption impact, and recovery capability. This quantification approach reflects the effect of component level disruption on the network. Hence, it is proposed that the network resilience is measured by the resilience of network component having the lowest resilience index. Efforts to enhance network resilience often require financial resources. Therefore, an optimization model is further introduced to maximize the network resilience under budget constraint through reinforcing the weakest components in the network. The proposed approach can help decision-makers assess the network resilience status and compare with it other networks, identify and improve components with low resilience, evaluate the cost of resilience improvement, and determine the extent of enhancement that can be achieved under a given budget limitation. Numerical experiments are conducted to illustrate our methodology using a supply chain network and a power network. Both examples show that considering the ability of the network to use alternative plans can enhance network resilience and result in lower demand loss when a disruption occurs.
Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1, 2014
This study is about a two-stage stochastic LNG inventory routing and scheduling problem under wea... more This study is about a two-stage stochastic LNG inventory routing and scheduling problem under weather disruptions which can cover all contract durations including long-term, short-term and spot demand. We consider a fleet of heterogeneous LNG carriers with partial tank filling capability of cargoes to travel multiple customers in routes. In this case, as it has to satisfy numerous customers in a route, the sailing time of an assigned vessel may be longer than the time from the current transportation model which serves single demand point. Understanding the ideas of this model made us to pay attention on boil-off gas (BOG) losses during voyage so that the resulting model can be more accurate in estimating the overall shipping cost. The stochastic approach aims to use historical information to find a stable solution that withstands disruptions caused by dust storm in Persian Gulf as a test-bed. The proposed LNG scheduling model formulated as a multiple vehicle routing problem (VRP) considering weather disruptions as a random variable. Since the stochastic VRP is combinatorial optimization problem in nature, we use Monte Carlo sampling technique to overcome the computational burden while ensuring good quality of solutions. Performance of our approach was compared against the deterministic counterpart using two performance measures: expected value of perfect information (EVPI) and value of stochastic solution (VSS).
Providing surveillance to maritime infrastructures against imminent threats is studied in this pa... more Providing surveillance to maritime infrastructures against imminent threats is studied in this paper. Surveillance is achieved with the aid of optimally placed sonars. Under a budget-limited deployment strategy involving both static and mobile sonars, a new optimization model using hexagonal grid systems is proposed. Numerical results suggest our approach delivers acceptable detection coverage under a multi-period placement scheme.
This paper presents a concise review on extant literature in threat detection within the maritime... more This paper presents a concise review on extant literature in threat detection within the maritime domain with specific emphasis on solution methodologies, sensing technologies, regional affiliations of authors and research contributions related to underwater threat detection. The review attempts to provide an introductory framework on threat detection and surveillance within the maritime domain as opposed to its widely studied terrestrial counterpart. In all, over 200 engineering/science journal papers and conference proceedings were identified. Of these, a total of 136 articles are eventually chosen and reviewed. The analysis of this review suggests that opportunities abound in the research area, especially in Operations Research/ Management Science. Specifically, Sensor placement and deployment for underwater threat detection is identified as a significant sub-research area worth studying.
We consider a new biannual liquefied natural gas (LNG) ship routing and scheduling problem and a ... more We consider a new biannual liquefied natural gas (LNG) ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fleet of heterogeneous vessels. We are motivated not only by contract trend changes to shorter ones but also by technological advancesin LNG vessel design. The mutual coincidence of both transitions enables developing a new LNG shipping strategy to keep up with emerging market trend. We fi rst propose a deterministic LNG scheduling model formulated as a multiple vehicle routing problem (VRP). The model is then extended to consider BOG using a two-stage stochastic modeling approach in which BOG is a random variable. Since the VRP is typically a combinatorial optimization problem, its stochastic extension is much harder to solve. In order to overcome this computational burden, a Monte Carlo sampling optimization is used to reduce the number of scenarios in the stochas...
Uploads
Papers by Selim Bora