Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration

被引:94
作者
Wang, Shijin [1 ]
Liu, Ming [1 ]
Chu, Feng [2 ,4 ]
Chu, Chengbin [1 ,3 ]
机构
[1] Tongji Univ, Dept Management Sci & Engn, Sch Econ & Management, Shanghai 710049, Peoples R China
[2] Univ Evry Val dEssonne, Lab IBISC, F-91020 Evry, France
[3] Univ Paris Saclay, Cent Supelec, Lab Genie Ind, F-92290 Chatenay Malabry, France
[4] Xihua Univ, Management Engn Res Ctr, Chengdu 610039, Peoples R China
基金
美国国家科学基金会;
关键词
Batch scheduling; Bi-objective optimization; Energy consumption cost; Makespan; Single machine; PARTICLE SWARM OPTIMIZATION; TOTAL WEIGHTED TARDINESS; NONIDENTICAL JOB SIZES; BURN-IN OVEN; MINIMIZING MAKESPAN; RESOURCE CONSUMPTION; GENETIC ALGORITHM; SHOP; TIME; FRAMEWORK;
D O I
10.1016/j.jclepro.2016.07.206
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the increasing energy price, the rapid growth of electricity demand and severe challenges for sustainable development, energy-efficient scheduling is becoming more and more important for power intensive manufacturing industry, especially for batch production companies. This paper investigates a bi-objective single machine batch scheduling problem with non-identical job sizes, the time-of-use (TOU) electricity prices, and different energy consumption rates of the machine. The first objective is to minimize the makespan and the second is to minimize the total energy costs, by considering both the machine utilization and the economic cost The problem is formulated as an integer programming model. Then, an exact epsilon-constraint method is adapted to obtain the exact Pareto front To deal with large scale problems, based on decomposition ideas, two heuristic methods are developed to obtain approximate Pareto fronts. Computational experiments on randomly generated instances show the effectiveness of the methods. A study case of a real-world glass manufacturing company is also conducted to show that the proposed methods are promising for practical usages. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1205 / 1215
页数:11
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