The Experts below are selected from a list of 103602 Experts worldwide ranked by ideXlab platform
Emil M. Petriu - One of the best experts on this subject based on the ideXlab platform.
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distributed multi objective cooperative coevolution algorithm for big data enabled vessel schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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CogSIMA - Distributed Multi-Objective Cooperative Coevolution Algorithm for Big-Data-Enabled Vessel Schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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Modeling the speed-based vessel schedule Recovery Problem using evolutionary multiobjective optimization
Information Sciences, 2018Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:Abstract Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule Recovery Problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule Recovery Problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) Problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stakeholders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the Problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the Problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios.
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big data enabled modelling and optimization of granular speed based vessel schedule Recovery Problem
International Conference on Big Data, 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
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BigData - Big-data-enabled modelling and optimization of granular speed-based vessel schedule Recovery Problem
2017 IEEE International Conference on Big Data (Big Data), 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
Bo Vaaben - One of the best experts on this subject based on the ideXlab platform.
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the vessel schedule Recovery Problem vsrp a mip model for handling disruptions in liner shipping
European Journal of Operational Research, 2013Co-Authors: Berit Dangaard Brouer, Jakob Dirksen, David Pisinger, Christian Edinger Munk Plum, Bo VaabenAbstract:Containerized transport by liner shipping companies is a multi billion dollar industry carrying a major part of the world trade between suppliers and customers. The liner shipping industry has come under stress in the last few years due to the economic crisis, increasing fuel costs, and capacity outgrowing demand. The push to reduce CO2 emissions and costs have increasingly committed liner shipping to slow-steaming policies. This increased focus on fuel consumption, has illuminated the huge impacts of operational disruptions in liner shipping on both costs and delayed cargo. Disruptions can occur due to adverse weather conditions, port contingencies, and many other issues. A common scenario for recovering a schedule is to either increase the speed at the cost of a significant increase in the fuel consumption or delaying cargo. Advanced Recovery options might exist by swapping two port calls or even omitting one. We present the Vessel Schedule Recovery Problem (VSRP) to evaluate a given disruption scenario and to select a Recovery action balancing the trade off between increased bunker consumption and the impact on cargo in the remaining network and the customer service level. It is proven that the VSRP is NP-hard. The model is applied to four real life cases from Maersk Line and results are achieved in less than 5seconds with solutions comparable or superior to those chosen by operations managers in real life. Cost savings of up to 58% may be achieved by the suggested solutions compared to realized recoveries of the real life cases.
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The Vessel Schedule Recovery Problem (VSRP) – A MIP model for handling disruptions in liner shipping
European Journal of Operational Research, 2013Co-Authors: Berit Dangaard Brouer, Jakob Dirksen, David Pisinger, Christian Edinger Munk Plum, Bo VaabenAbstract:Abstract Containerized transport by liner shipping companies is a multi billion dollar industry carrying a major part of the world trade between suppliers and customers. The liner shipping industry has come under stress in the last few years due to the economic crisis, increasing fuel costs, and capacity outgrowing demand. The push to reduce CO2 emissions and costs have increasingly committed liner shipping to slow-steaming policies. This increased focus on fuel consumption, has illuminated the huge impacts of operational disruptions in liner shipping on both costs and delayed cargo. Disruptions can occur due to adverse weather conditions, port contingencies, and many other issues. A common scenario for recovering a schedule is to either increase the speed at the cost of a significant increase in the fuel consumption or delaying cargo. Advanced Recovery options might exist by swapping two port calls or even omitting one. We present the Vessel Schedule Recovery Problem (VSRP) to evaluate a given disruption scenario and to select a Recovery action balancing the trade off between increased bunker consumption and the impact on cargo in the remaining network and the customer service level. It is proven that the VSRP is NP -hard. The model is applied to four real life cases from Maersk Line and results are achieved in less than 5 seconds with solutions comparable or superior to those chosen by operations managers in real life. Cost savings of up to 58% may be achieved by the suggested solutions compared to realized recoveries of the real life cases.
Fatemeh Cheraghchi - One of the best experts on this subject based on the ideXlab platform.
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distributed multi objective cooperative coevolution algorithm for big data enabled vessel schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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CogSIMA - Distributed Multi-Objective Cooperative Coevolution Algorithm for Big-Data-Enabled Vessel Schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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Modeling the speed-based vessel schedule Recovery Problem using evolutionary multiobjective optimization
Information Sciences, 2018Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:Abstract Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule Recovery Problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule Recovery Problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) Problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stakeholders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the Problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the Problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios.
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big data enabled modelling and optimization of granular speed based vessel schedule Recovery Problem
International Conference on Big Data, 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
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BigData - Big-data-enabled modelling and optimization of granular speed-based vessel schedule Recovery Problem
2017 IEEE International Conference on Big Data (Big Data), 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
Victor M. Preciado - One of the best experts on this subject based on the ideXlab platform.
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ACC - Resilient Structural Stabilizability of Undirected Networks
2019 American Control Conference (ACC), 2019Co-Authors: Jingqi Li, Ximing Chen, Sergio Pequito, George J. Pappas, Victor M. PreciadoAbstract:In this paper, we consider the structural stabilizability Problem of undirected networks. More specifically, we are tasked to infer the stabilizability of an undirected network from its underlying topology, where the undirected networks are modeled as continuous-time linear time-invariant (LTI) systems involving symmetric state matrices. Firstly, we derive a graph-theoretic necessary and sufficient condition for structural stabilizability of undirected networks. Then, we propose a method to determine the maximum dimension of the stabilizable subspace solely based on the network structure. Based on these results, on one hand, we study the optimal actuator-disabling attack Problem, i.e., removing a limited number of actuators to minimize the maximum dimension of the stabilizable subspace. We show this Problem is NP-hard. On the other hand, we study the optimal Recovery Problem with respect to the same kind of attacks, i.e., adding a limited number of new actuators such that the maximum dimension of the stabilizable subspace is maximized. We prove the optimal Recovery Problem is also NP-hard, and we develop a $(1-1/e)$ approximation algorithm to this Problem.
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Resilient Structural Stabilizability of Undirected Networks
arXiv: Optimization and Control, 2018Co-Authors: Jingqi Li, Ximing Chen, Sergio Pequito, George J. Pappas, Victor M. PreciadoAbstract:In this paper, we consider the structural stabilizability Problem of undirected networks. More specifically, we are tasked to infer the stabilizability of an undirected network from its underlying topology, where the undirected networks are modeled as continuous-time linear time-invariant (LTI) systems involving symmetric state matrices. Firstly, we derive a graph-theoretic necessary and sufficient condition for structural stabilizability of undirected networks. Then, we propose a method to infer the maximum dimension of stabilizable subspace solely based on the network structure. Based on these results, on one hand, we study the optimal actuator-disabling attack Problem, i.e., removing a limited number of actuators to minimize the maximum dimension of stabilizable subspace. We show this Problem is NP-hard. On the other hand, we study the optimal Recovery Problem with respect to the same kind of attacks, i.e., adding a limited number of new actuators such that the maximum dimension of stabilizable subspace is maximized. We prove the optimal Recovery Problem is also NP-hard, and we develop a (1-1/e) approximation algorithm to this Problem.
Ibrahim Abualhaol - One of the best experts on this subject based on the ideXlab platform.
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distributed multi objective cooperative coevolution algorithm for big data enabled vessel schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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CogSIMA - Distributed Multi-Objective Cooperative Coevolution Algorithm for Big-Data-Enabled Vessel Schedule Recovery Problem
2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2020Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:During a maritime voyage, delays due to disruptive events could result in financial and reputation losses. The vessel schedule Recovery Problem (VSRP) aims at adjusting vessel speeds to mitigate the negative impact of such delays. The granulated speed-based vessel schedule Recovery Problem (G-S-VSRP) is a big-data-enabled VSRP. It is a multiobjective optimization Problem defined by dividing the trajectory between ports into regions (encoded by geohashed system) and mining the speed profiles in these regions from Automatic Identification System (AIS) data. The G-S-VSRP minimizes delay and financial loss of a vessel voyage; it also maximizes the speed compliance with the historical navigational patterns. Using geohash-based speed mining on AIS data in the G-S-VSRP gives rise to a large-scale optimization Problem, where the number of speed variables in geohashed regions grows to the order of thousands. Due to the complexity of such a Problem, traditional multiobjective evolutionary algorithms (MOEAs) would stop improving or showing steady behavior. We improve the MOEA's performance using a cooperative coevolution algorithm based on a divide-and-conquer approach to deal with large-scale optimization Problems. We introduce a novel Distributed Multiobjective Cooperative Coevolutionary Algorithm (DMOCCA) to improve the performance of MOEAs.
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Modeling the speed-based vessel schedule Recovery Problem using evolutionary multiobjective optimization
Information Sciences, 2018Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:Abstract Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule Recovery Problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule Recovery Problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) Problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stakeholders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the Problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the Problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios.
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big data enabled modelling and optimization of granular speed based vessel schedule Recovery Problem
International Conference on Big Data, 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
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BigData - Big-data-enabled modelling and optimization of granular speed-based vessel schedule Recovery Problem
2017 IEEE International Conference on Big Data (Big Data), 2017Co-Authors: Fatemeh Cheraghchi, Ibrahim Abualhaol, Rafael Falcon, Rami Abielmona, Bijan Raahemi, Emil M. PetriuAbstract:The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed Recovery Problem by formulating it as a multi-objective optimization (MOO) Problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
