This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti...This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.展开更多
A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calcu...A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes.The power rating of the ES is adjusted on the basis of the available active power at the bus.And in the optimization problem,it is expressed as the power ratio of the noncritical load(NCL)and critical load(CL).The implemented ES model is flexible,which can be used on any bus and any phase.The model determines the output voltage from the parameters and operating conditions at the point of common coupling(PCC).These conditions are integrated using the backward/forward sweep method(BFSM)and are updated during power flow calculations.The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm,which computes power flow and ES placement simultaneously.The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.展开更多
基金Thailand Research Fund (Grant #MRG5480176)National Research University Project of Thailand Office of Higher Education Commission
文摘This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.
基金supported by Consejo Nacional de Humanidades,Ciencia y Tecnología(CONAHCYT)—México(No.863547)the fellowship 2021-000001-01NACF-00604 given to the G.H.Valencia-Riverathe scholarships 175599,64698,253652,and 296574,given to G.Tapia-Tinoco,A.Garcia-Perez,D.Granados-Lieberman,and M.Valtierra-Rodriguez,respectively,through the Sistema Nacional de Investigadoras e Investigadores(SNII)-CONAHCYT-México.
文摘A novel planning tool for optimizing the placement of electric springs(ESs)in unbalanced distribution networks is introduced in this study.The total voltage deviation is used as the optimization criterion and is calculated when the ESs operate at their maximum reactive power either in the inductive or capacitive modes.The power rating of the ES is adjusted on the basis of the available active power at the bus.And in the optimization problem,it is expressed as the power ratio of the noncritical load(NCL)and critical load(CL).The implemented ES model is flexible,which can be used on any bus and any phase.The model determines the output voltage from the parameters and operating conditions at the point of common coupling(PCC).These conditions are integrated using the backward/forward sweep method(BFSM)and are updated during power flow calculations.The problem is described as a mixed-integer nonlinear problem and solved efficiently using an improved BFSM-based genetic algorithm,which computes power flow and ES placement simultaneously.The effectiveness of this method is evaluated through testing in IEEE 13-bus and 34-bus systems.
