TY - JOUR
T1 - Scheduling Dual-Objective Stochastic Hybrid Flow Shop with Deteriorating Jobs via Bi-Population Evolutionary Algorithm
AU - Fu, Yaping
AU - Zhou, Mengchu
AU - Guo, Xiwang
AU - Qi, Liang
N1 - Funding Information:
Manuscript received January 1, 2019; accepted March 14, 2019. Date of publication April 16, 2019; date of current version November 18, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61703220 and Grant 71871105, in part by the Shandong Provincial Natural Science Foundation, China, under Grant ZR2016FP02, in part by the Post-Doctoral Science Foundation Project of China under Grant 2017M610407, and in part by the Qingdao Post-Doctoral Research Project under Grant 2016027. This paper was recommended by Associate Editor J.-H. Chou. (Corresponding author: Mengchu Zhou.) Y. Fu is with the School of Business, Qingdao University, Qingdao 266071, China (e-mail: fuyaping0432@163.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs' processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines' abrasion and jobs' feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.
AB - Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs' processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines' abrasion and jobs' feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.
KW - Deteriorating scheduling
KW - dual-objective hybrid flow shop
KW - hybrid multiobjective evolutionary algorithm (HMOEA)
KW - stochastic scheduling
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U2 - 10.1109/TSMC.2019.2907575
DO - 10.1109/TSMC.2019.2907575
M3 - Article
AN - SCOPUS:85096785100
SN - 2168-2216
VL - 50
SP - 5037
EP - 5048
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
M1 - 8692751
ER -