Bi-Objective Optimization for Uniform Parallel Batch Machine Scheduling under Time-of-Use Tariffs

被引：0

作者：

Cheng, Junheng ^{[1
]}

Cheng, Jingya ^{[1
]}

Chu, Feng ^{[2
]}

机构：

[1] Fujian Normal Univ, Sch Econ, Fuzhou, Peoples R China

[2] Univ Paris Saclay, Univ Evry, Lab IBISC, Evry, France

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL, ICNSC | 2022年

基金：

中国国家自然科学基金;

关键词：

Bi-objective optimization; batch scheduling; uniform parallel machines; time-of-use tariffs; PROCESSING MACHINE; ALGORITHMS;

D O I：

10.1109/ICNSC55942.2022.10004129

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the epsilon-constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.

引用

页码：604 / 609

页数：6

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