Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coeffici...Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.展开更多
Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life produc...Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life products.This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic,environmental,and social sustainability.Firstly,a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures.Secondly,a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem.In the designed approach,problem-specific encoding and decoding methods are employed to represent and produce feasible solutions.The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments.Rank and crowding distance approaches are introduced to realize ability grouping,namely,dividing the population into two groups.Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution,and an adaptive local search method is developed to enhance exploitation.Thirdly,comparison experiments on some real-world test problems with different scales are carried out.Through dissecting the experimental results with three performance metrics,it can be observed that the devised approach outperforms its competitors by 9.39%-10.00%,11.37%-59.86%,and 2.36%-7.73%regarding performance,respectively.The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.展开更多
基金supported by National Natural Science Foundation of China (No. 71171199)Natural Science Foundation of Shaanxi Province of China (No. 2013JM1003)
文摘Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.
基金supported in part by the National Natural Science Foundation of China(Nos.62173356 and 61703320)Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities(No.2020RWG011)+4 种基金Natural Science Foundation of Shandong Province(No.ZR202111110025)Science and Technology Development Fund(FDCT)Macao SAR(No.0019/2021/A)Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University(No.SZSK202208)Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC).
文摘Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life products.This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic,environmental,and social sustainability.Firstly,a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures.Secondly,a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem.In the designed approach,problem-specific encoding and decoding methods are employed to represent and produce feasible solutions.The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments.Rank and crowding distance approaches are introduced to realize ability grouping,namely,dividing the population into two groups.Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution,and an adaptive local search method is developed to enhance exploitation.Thirdly,comparison experiments on some real-world test problems with different scales are carried out.Through dissecting the experimental results with three performance metrics,it can be observed that the devised approach outperforms its competitors by 9.39%-10.00%,11.37%-59.86%,and 2.36%-7.73%regarding performance,respectively.The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.
