Novel Energy- and Maintenance-Aware Collaborative Scheduling for A Hybrid Flow Shop Based on Dual Memetic Algorithms
Junkai Wang, Hangming Du, Junxia Xing, Fei Qiao, Yumin Ma
Abstract
Limited energy supply and uncertain equipment state increase the complexity of production process. Peak power and maintenance-based demand response facilitates factories to adjust scheduling strategies to actual production circumstances, so that a rise in energy cost penalty and machine breakdown could be avoided without affecting normal production on the shop floor. This letter proposes a mixed integer programming (MIP) model for a hybrid flow shop (HFS) to minimize makespan, with the consideration of machine maintenance plans and peak power consumption constraint. Two paradigms of a dual memetic algorithm (DMA) that combines genetic algorithm with two novel heuristic algorithms are proposed according to the characteristics of the problem. To enable maintenance awareness, a heuristic algorithm for machine maintenance is designed to reorganize current production sequences. To enable peak power awareness, a heuristic algorithm based on chromosome priority sequence is put forward. A case study from benchmarks demonstrates the effectiveness of the proposed model and algorithms. The performance of both paradigms is discussed.
BibTeX
@inproceedings{ral2020_novelenergyandma,
title = {Novel Energy- and Maintenance-Aware Collaborative Scheduling for A Hybrid Flow Shop Based on Dual Memetic Algorithms},
author = {Junkai Wang and Hangming Du and Junxia Xing and Fei Qiao and Yumin Ma},
booktitle = {RA-L 2020},
year = {2020}
}