To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rul...To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.展开更多
Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals,and scheduling optimization is a very important research issue in medical service.In thi...Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals,and scheduling optimization is a very important research issue in medical service.In this paper,to cope with the actualities of operation resources scheduling,such as poor planning,lack of standardized scheduling rules,chaotic use of the operating rooms,and many human interference factors,we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources.We frst design a framework that includes the composite dispatching rules(CDR),optimization ideology,and feedback mechanism,in which the CDR integrates fexible operating time,hold-up time of medical facilities,available time of medical staf,and multiple constraints.The optimization ideology is carried out through a learning model based on the weighted random forest(WRF)algorithm.The feedback mechanism enables the approach to realize closed-loop optimizations adaptively.Finally,the superiority of the systematic scheduling approach(SSA)is analyzed through numerical experiments on a simulation platform.Results of the simulation experiments show that the proposed scheduling method can improve performances signifcantly,especially in the waiting time of patients.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51805152 and 52075401)the Green Industry Technology Leading Program of Hubei University of Technology(No.XJ2021005001)+1 种基金the Scientific Research Foundation for High-level Talents of Hubei University of Technology(No.GCRC2020009)the Natural Science Foundation of Hubei Province(No.2022CFB445).
文摘To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.
基金This research was supported by the National Key R&D Program of China(No.2018YFE0105000)the Shanghai Municipal Commission of Science and Technology(No.19511132100)the National Natural Science Foundation of China(No.51475334).
文摘Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals,and scheduling optimization is a very important research issue in medical service.In this paper,to cope with the actualities of operation resources scheduling,such as poor planning,lack of standardized scheduling rules,chaotic use of the operating rooms,and many human interference factors,we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources.We frst design a framework that includes the composite dispatching rules(CDR),optimization ideology,and feedback mechanism,in which the CDR integrates fexible operating time,hold-up time of medical facilities,available time of medical staf,and multiple constraints.The optimization ideology is carried out through a learning model based on the weighted random forest(WRF)algorithm.The feedback mechanism enables the approach to realize closed-loop optimizations adaptively.Finally,the superiority of the systematic scheduling approach(SSA)is analyzed through numerical experiments on a simulation platform.Results of the simulation experiments show that the proposed scheduling method can improve performances signifcantly,especially in the waiting time of patients.
