Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell

被引：10
|
作者：
Li, Xiaohui [1 ]
Yang, Xi [1 ]
Zhao, Yi [1 ]
Teng, Ying [2 ]
Dong, Yuan [1 ]
机构：
[1] Changan Univ, Sch Elect & Control Engn, Xian 710054, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610051, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
关键词：
Job shop scheduling; Approximation algorithms; Service robots; Workstations; Optimal scheduling; Robotic cell; job shop; multi-objective optimization; local search; teaching-learning based optimization; PARTICLE SWARM OPTIMIZATION; LOCAL SEARCH; ALGORITHM; MAKESPAN; MACHINE; DESIGN; TIME;
D O I：
10.1109/ACCESS.2020.3015796
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs' transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations' assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.
引用
收藏
页码：147015 / 147028
页数：14
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