This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassemb...This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.展开更多
Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has bec...Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.展开更多
Chemical reaction optimization algorithm(CRO)is an intelligent heuristic evolutionary algorithm suitable for multi-objective optimization problems.In this paper,an adaptive PID controller based on classified-disturban...Chemical reaction optimization algorithm(CRO)is an intelligent heuristic evolutionary algorithm suitable for multi-objective optimization problems.In this paper,an adaptive PID controller based on classified-disturbance chemical reaction optimization(CDCRO)algorithm is proposed to realize the control of multiple-input multiple-output(MIMO)nonlinear systems.First,for the multi-channel PID control,an expanded ITSE function(mITSE(x))is designed as the objective function of the optimization algorithm.Then,in difference from the classic CRO algorithm,the information between population individuals is taken into consideration,and an individual similarity function is introduced to classify the population for the purpose of reducing inefficient evolution and speeding up the global optimization.In addition,an elite retention strategy is adopted and the randomness of operators is improved.Furthermore,to improve the robustness of the system,an adaptive intelligent control system has been designed by adding an adaptive feedback link and establishing the adaptive rule.Finally,simulation results illustrate the effectiveness and robustness of the proposed intelligent control approach,and comparing results indicates that the CDCRO-PID controller has better performance against other optimization algorithms.展开更多
文摘This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.
基金This work was supported by the National Natural Science Foundation of China(Nos.61973120,62076095,61673175,and 61573144).
文摘Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.
基金funded by the National Natural Science Foundation of China(No.61733017)and the Natural Science Foundation of Shanghai(No.18ZR1419000).
文摘Chemical reaction optimization algorithm(CRO)is an intelligent heuristic evolutionary algorithm suitable for multi-objective optimization problems.In this paper,an adaptive PID controller based on classified-disturbance chemical reaction optimization(CDCRO)algorithm is proposed to realize the control of multiple-input multiple-output(MIMO)nonlinear systems.First,for the multi-channel PID control,an expanded ITSE function(mITSE(x))is designed as the objective function of the optimization algorithm.Then,in difference from the classic CRO algorithm,the information between population individuals is taken into consideration,and an individual similarity function is introduced to classify the population for the purpose of reducing inefficient evolution and speeding up the global optimization.In addition,an elite retention strategy is adopted and the randomness of operators is improved.Furthermore,to improve the robustness of the system,an adaptive intelligent control system has been designed by adding an adaptive feedback link and establishing the adaptive rule.Finally,simulation results illustrate the effectiveness and robustness of the proposed intelligent control approach,and comparing results indicates that the CDCRO-PID controller has better performance against other optimization algorithms.
