Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

Xin Yang; Zhenxiang Zeng; Ruidong Wang; Xueshan Sun

doi:10.1371/journal.pone.0167427

Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

PLoS One. 2016 Dec 1;11(12):e0167427. doi: 10.1371/journal.pone.0167427. eCollection 2016.

Authors

Xin Yang^{1

2}, Zhenxiang Zeng¹, Ruidong Wang³, Xueshan Sun²

Affiliations

¹ School of Economics and Management, Hebei University of Technology, Tianjin, China.
² ZhongHuan Information College Tianjin University of Technology, Tianjin, China.
³ Department of Mathematics, Tianjin University of Technology, Tianjin, China.

Abstract

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

MeSH terms

Algorithms
Energy Metabolism*
Models, Theoretical*
Stochastic Processes*

Grants and funding

This research was funded by a Science-technology Support Plan of Hebei Province (Grant Number: 14210102D) http://www.hensf.gov.cn/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.