Open Access

ARTICLE

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

by Lei Wang^1,2,*, Yuxin Qi^1,2

1 School of Event and Economic Management, Shanghai Institute of Tourism, Shanghai, 201418, China
2 School of Tourism, Shanghai Normal University, Shanghai, 201418, China

* Corresponding Author: Lei Wang. Email:

(This article belongs to the Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)

Computer Modeling in Engineering & Sciences 2023, 135(1), 325-339. https://doi.org/10.32604/cmes.2022.019730

Received 11 October 2021; Accepted 28 March 2022; Issue published 29 September 2022

Abstract

Currently, energy conservation draws wide attention in industrial manufacturing systems. In recent years, many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach. This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects. In it, the real processing time of jobs is calculated by using their processing speed and normal processing time. To describe this problem in a mathematical way, a multi-objective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated. Furthermore, we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it. In this approach, the multi-verse optimization is adopted to find favorable solutions from the huge solution domain, while the stochastic simulation method is employed to assess them. By conducting comparison experiments on test problems, it can be verified that the developed approach has better performance in coping with the considered problem, compared to two classic multi-objective evolutionary algorithms.

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Keywords

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