Paper Info
Title
Multiobjective Modeling And Optimization For Scheduling A Stochastic Hybrid Flow Shop With Maximizing Processing Quality And Minimizing Total Tardiness
Abstract
Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs' processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.
Year
DOI
Venue
2021
10.1109/JSYST.2020.3014093
IEEE SYSTEMS JOURNAL
Keywords
DocType
Volume
Job shop scheduling, Stochastic processes, Manufacturing systems, Uncertainty, Mathematical model, Artificial bee colony algorithm, hybrid flow shop, multiobjective scheduling, processing quality, stochastic scheduling, stochastic simulation
Journal
15
Issue
ISSN
Citations 
3
1932-8184
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yaping Fu1694.41
Hongfeng Wang2523.70
Wang, J.3606.72
Xujin Pu400.34