In reentrant production,decision makers need to consider whether the part should be discarded or reprocessed.It involves the production and time cost that is required by reprocessing.Therefore,an efficient and feasibl...In reentrant production,decision makers need to consider whether the part should be discarded or reprocessed.It involves the production and time cost that is required by reprocessing.Therefore,an efficient and feasible assignment method is required for reentrant production.To tackle this issue,we use the Environments-Classes,Agents,Roles,Groups,and Objects model to formalize this problem.A novel solution is designed for Reentrant Production by extending the Group Role Assignment(GRA)problem model to solve the GRA with Balance problem.With this proposed solution,we can get an allocation scheme that takes into account multi-objective optimization and Pareto equilibrium between average performance of the whole reprocessing system and high defect rate parts.Finally,large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution.The simulation results provide a solid decision-making reference for the manufacturer.展开更多
The Vehicle Allocation Problem(VAP)in the vehicle sales chain has three bottlenecks in practice.The first is to collect relevant cooperation or conflict information,the second is to accurately quantify and analyze oth...The Vehicle Allocation Problem(VAP)in the vehicle sales chain has three bottlenecks in practice.The first is to collect relevant cooperation or conflict information,the second is to accurately quantify and analyze other factors affecting the distribution of cars,and the third is to establish a stable and rapid response to the vehicle allocation management method.In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain,it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy.Therefore,this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors(GMRACCF)from a new perspective.Through the logical reasoning of closure computation,the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors(CCF).Therefore,a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales(CSs)on the all-round performance according to their needs.Based on collaborative filtering,semantic modification is applied to resolve conflicts among qualifications.Large-scale simulation results show that the proposed method is feasible and robust,and provides a reliable decision-making reference in the vehicle sales chain.展开更多
基金supported by the National Key Research and Development Program of China No.2022YFB3304400Natural Sciences and Engineering Research Council of Canada(NSERC)under Grant DDG-2024-00036.
文摘In reentrant production,decision makers need to consider whether the part should be discarded or reprocessed.It involves the production and time cost that is required by reprocessing.Therefore,an efficient and feasible assignment method is required for reentrant production.To tackle this issue,we use the Environments-Classes,Agents,Roles,Groups,and Objects model to formalize this problem.A novel solution is designed for Reentrant Production by extending the Group Role Assignment(GRA)problem model to solve the GRA with Balance problem.With this proposed solution,we can get an allocation scheme that takes into account multi-objective optimization and Pareto equilibrium between average performance of the whole reprocessing system and high defect rate parts.Finally,large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution.The simulation results provide a solid decision-making reference for the manufacturer.
基金supported by the National Key Research and Development Program of China(No.2022YFB3304400)the National Natural Science Foundation of China(No.62072120)+1 种基金the Natural Sciences and Engineering Research Council(NSERC)of Canada(No.RGPIN2018-04818)the Guangdong Provincial Key Laboratory of Cyber-Physical System(No.2020B1212060069).
文摘The Vehicle Allocation Problem(VAP)in the vehicle sales chain has three bottlenecks in practice.The first is to collect relevant cooperation or conflict information,the second is to accurately quantify and analyze other factors affecting the distribution of cars,and the third is to establish a stable and rapid response to the vehicle allocation management method.In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain,it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy.Therefore,this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors(GMRACCF)from a new perspective.Through the logical reasoning of closure computation,the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors(CCF).Therefore,a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales(CSs)on the all-round performance according to their needs.Based on collaborative filtering,semantic modification is applied to resolve conflicts among qualifications.Large-scale simulation results show that the proposed method is feasible and robust,and provides a reliable decision-making reference in the vehicle sales chain.
