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

页码：147015 / 147028

页数：14

共 50 条

[41] Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm☆
Hao, Linyuan
Zou, Zhiyuan
Liang, Xu
COMPUTERS & INDUSTRIAL ENGINEERING, 2025, 200
[42] Hybrid Pareto-based tabu search algorithm for solving the multi-objective flexible Job Shop scheduling problem
Li, Jun-Qing
Pan, Quan-Ke
Wang, Yu-Ting
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2010, 16 (07): : 1419 - 1426
[43] Optimising the job-shop scheduling problem using a multi-objective Jaya algorithm
He, Lijun
Li, Wenfeng
Chiong, Raymond
Abedi, Mehdi
Cao, Yulian
Zhang, Yu
APPLIED SOFT COMPUTING, 2021, 111
[44] Improved NSGA-II for the job-shop multi-objective scheduling problem
Jiang X.
Li Y.
International Journal of Performability Engineering, 2018, 14 (05) : 891 - 898
[45] An Improved Genetic Algorithm for Multi-objective Flexible Job-shop Scheduling Problem
Zhang, Chaoyong
Wang, Xiaojuan
Gao, Liang
MANUFACTURING SCIENCE AND ENGINEERING, PTS 1-5, 2010, 97-101 : 2449 - 2454
[46] An estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem
Wang, Shengyao
Wang, Ling
Liu, Min
Xu, Ye
PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN SCHEDULING (CISCHED), 2013, : 1 - 8
[47] Modeling the problem of multi-objective job shop scheduling based on security and its solution
Ren Q.
Zhao K.
Ren, Qingdaoerji, 1600, UK Simulation Society, Clifton Lane, Nottingham, NG11 8NS, United Kingdom (17): : 19.1 - 19.5
[48] Three Hybrid Scatter Search Algorithms for Multi-Objective Job Shop Scheduling Problem
Hernandez-Ramirez, Leo
Frausto-Solis, Juan
Castilla-Valdez, Guadalupe
Gonzalez-Barbosa, Javier
Sanchez Hernandez, Juan-Paulo
AXIOMS, 2022, 11 (02)
[49] Multi-objective flexible job shop scheduling problem using differential evolution algorithm
Cao, Yang
Shi, Haibo
Han, Zhonghua
2017 9TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC 2017), 2017, : 521 - 526
[50] Multi-objective evolutionary algorithm to solve interval flexible job shop scheduling problem
Wang C.
Wang Y.
Ji Z.-C.
Kongzhi yu Juece/Control and Decision, 2019, 34 (05): : 908 - 916

← 1 2 3 4 5 →