Indexed by:Journal Article

Date of Publication:2017-03-01

Journal:13th International Conference on Information and Management Sciences (IMS)

Included Journals:Scopus、CPCI-S、EI、SCIE

Volume:28

Issue:3,SI

Page Number:833-845

ISSN:0956-5515

Key Words:Estimation of distribution algorithm (EDA); Multiobjective optimization model; Manufacturing scheduling; Stochastic job-shop scheduling problem (S-JSP)

Abstract:This paper proposes an effective multiobjective estimation of distribution algorithm (MoEDA) which solves the bi-criteria stochastic job-shop scheduling problem with the uncertainty of processing time. The MoEDA proposal minimizes the expected average makespan and the expected total tardiness within a reasonable amount of computational time. With the framework of proposed MoEDA, the probability model of the operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, allocation method is used to decide the operation sequence, and then the expected makespan and total tardiness of each sampling are evaluated. Subsequently, updating mechanism of the probability models is proposed according to the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain an acceptable solution in the aspects of schedule quality and computational efficiency.