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Improved Estimation of Distribution Algorithm for Solving Unrelated Parallel Machine Scheduling Problem

Improved Estimation of Distribution Algorithm for Solving Unrelated Parallel Machine Scheduling Problem
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摘要 Scheduling problem is a well-known combinatorial optimization problem.An effective improved estimation of distribution algorithm(IEDA) was proposed for minimizing the makespan of the unrelated parallel machine scheduling problem(UPMSP).Mathematical description was given for the UPMSP.The IEDA which was combined with variable neighborhood search(IEDA_VNS) was proposed to solve the UPMSP in order to improve local search ability.A new encoding method was designed for representing the feasible solutions of the UPMSP.More knowledge of the UPMSP were taken consideration in IEDA_ VNS for probability matrix which was based the processing time matrix.The simulation results show that the proposed IEDA_VNS can solve the problem effectively. Scheduling problem is a well-known combinatorial optimization problem.An effective improved estimation of distribution algorithm(IEDA) was proposed for minimizing the makespan of the unrelated parallel machine scheduling problem(UPMSP).Mathematical description was given for the UPMSP.The IEDA which was combined with variable neighborhood search(IEDA_VNS) was proposed to solve the UPMSP in order to improve local search ability.A new encoding method was designed for representing the feasible solutions of the UPMSP.More knowledge of the UPMSP were taken consideration in IEDA_ VNS for probability matrix which was based the processing time matrix.The simulation results show that the proposed IEDA_VNS can solve the problem effectively.
出处 《Journal of Donghua University(English Edition)》 EI CAS 2016年第5期797-802,共6页 东华大学学报(英文版)
基金 National Natural Science Foundations of China(Nos.61573144,61174040)
关键词 estimation of distribution algorithm(EDA) unrelated parallel machine scheduling problem(UPMSP) Scheduling neighborhood scheduling minimizing processed unrelated probabilistic intelligent heuristic representing
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