An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti...An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.展开更多
In the context of rising energy costs and environmental concerns,the challenge of energy consumption has become paramount for the manufacturing industry.Traditional production manufacturing systems are seeking innovat...In the context of rising energy costs and environmental concerns,the challenge of energy consumption has become paramount for the manufacturing industry.Traditional production manufacturing systems are seeking innovative solutions to enhance efficiency and reduce energy consumption.One promising approach involves the integration of Unmanned Aerial Vehicles(UAVs)into the production process,leveraging their flexibility and efficiency.This research addresses the Parallel Machine Scheduling Problem for UAVs(PMSP-UAV),aiming to minimize both makespan and total energy consumption.The energy consumption metrics considered during the production process include startup,running,and idle energy.To simultaneously optimize the makespan and energy consumption objectives,we propose a knowledge-based Multi-Objective Discrete Artificial Bee Colony(MODABC)algorithm.This algorithm incorporates External Archiving(EA)and an elite knowledge-based guidance strategy to enhance convergence.A knowledge-based local search method is applied to the elites to enhance their quality,while the elite knowledge-based guidance scout bee phase prevents premature convergence.Finally,the efficacy of the proposed algorithm is rigorously validated through extensive testing on a dataset comprising over 100 real-world instances derived from an operational factory setting.展开更多
With the popularization of industrial intelligence,Automated Guided Vehicles(AGVs)have gradually become an efficient means of transportation in manufacturing workshops.Previous studies on this issue mainly considered ...With the popularization of industrial intelligence,Automated Guided Vehicles(AGVs)have gradually become an efficient means of transportation in manufacturing workshops.Previous studies on this issue mainly considered the transportation cost of AGVs,while ignoring the optimization of customer satisfaction.This paper studies the AGV scheduling problem with time and capacity constraints for material handling in an intelligent manufacturing workshop.To better reflect real production conditions and simultaneously minimize AGV carbon emissions while maximizing customer satisfaction,a Mixed-Integer Linear Programming(MILP)model is developed.A Multi-objective Discrete Artificial Bee Colony algorithm(MDABC)is proposed,which employs an adaptive selection strategy to ensure that different neighborhoods of solutions are fully explored.The reference search strategy is introduced to carry out in-depth search according to the effective information carried by high quality solutions.In addition,in order to avoid the algorithm falling into local optimality,a high-quality generation strategy is proposed.Comprehensive comparisons with state-of-the-art algorithms and statistical analyses demonstrate that the proposed MDABC achieves superior performance.展开更多
基金Projects(61174040,61104178,61374136) supported by the National Natural Science Foundation of ChinaProject(12JC1403400) supported by Shanghai Commission of Science and Technology,ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.
基金supported by the National Natural Science Foundation of China(No.62203458).
文摘In the context of rising energy costs and environmental concerns,the challenge of energy consumption has become paramount for the manufacturing industry.Traditional production manufacturing systems are seeking innovative solutions to enhance efficiency and reduce energy consumption.One promising approach involves the integration of Unmanned Aerial Vehicles(UAVs)into the production process,leveraging their flexibility and efficiency.This research addresses the Parallel Machine Scheduling Problem for UAVs(PMSP-UAV),aiming to minimize both makespan and total energy consumption.The energy consumption metrics considered during the production process include startup,running,and idle energy.To simultaneously optimize the makespan and energy consumption objectives,we propose a knowledge-based Multi-Objective Discrete Artificial Bee Colony(MODABC)algorithm.This algorithm incorporates External Archiving(EA)and an elite knowledge-based guidance strategy to enhance convergence.A knowledge-based local search method is applied to the elites to enhance their quality,while the elite knowledge-based guidance scout bee phase prevents premature convergence.Finally,the efficacy of the proposed algorithm is rigorously validated through extensive testing on a dataset comprising over 100 real-world instances derived from an operational factory setting.
基金supported by the National Natural Science Foundation of China(No.62473186,62273221,and 62003150)the Natural Science Foundation of Shandong Province(No.ZR2024MF017).
文摘With the popularization of industrial intelligence,Automated Guided Vehicles(AGVs)have gradually become an efficient means of transportation in manufacturing workshops.Previous studies on this issue mainly considered the transportation cost of AGVs,while ignoring the optimization of customer satisfaction.This paper studies the AGV scheduling problem with time and capacity constraints for material handling in an intelligent manufacturing workshop.To better reflect real production conditions and simultaneously minimize AGV carbon emissions while maximizing customer satisfaction,a Mixed-Integer Linear Programming(MILP)model is developed.A Multi-objective Discrete Artificial Bee Colony algorithm(MDABC)is proposed,which employs an adaptive selection strategy to ensure that different neighborhoods of solutions are fully explored.The reference search strategy is introduced to carry out in-depth search according to the effective information carried by high quality solutions.In addition,in order to avoid the algorithm falling into local optimality,a high-quality generation strategy is proposed.Comprehensive comparisons with state-of-the-art algorithms and statistical analyses demonstrate that the proposed MDABC achieves superior performance.
