A multi-objective evolutionary algorithm based on adaptive clustering for energy-aware batch scheduling problem

For batch scheduling problems, more and more attentions have been paid to reducing energy consumption. In this paper, a complex batch scheduling problem on parallel batch processing machines considering time-of-use electricity price is investigated to minimize makespan and total electricity cost, si...

Full description

Saved in:
Bibliographic Details
Published in:Future generation computer systems Vol. 113; pp. 441 - 453
Main Authors: Qian, Si-yuan, Jia, Zhao-hong, Li, Kai
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2020
Subjects:
Adaptive clustering
Batch processing machines
Energy-aware scheduling
Evolutionary algorithm
Multi-objective optimization
Energy-aware scheduling
Multi-objective optimization
Evolutionary algorithm
Batch processing machines
Adaptive clustering
ISSN:0167-739X, 1872-7115
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For batch scheduling problems, more and more attentions have been paid to reducing energy consumption. In this paper, a complex batch scheduling problem on parallel batch processing machines considering time-of-use electricity price is investigated to minimize makespan and total electricity cost, simultaneously. Due to NP-hardness of the studied problem, a multi-objective evolutionary algorithm based on adaptive clustering is proposed, where an improved adaptive clustering method is incorporated to mine the distribution structure of solutions, which can be used to guide the search. Moreover, a new recombination strategy based on both distribution characteristics and mating probability is designed to select individuals for mating. In addition, to better balance exploration and exploitation, the mating probability is adaptively adjusted according to historical information. The experimental results demonstrate the competitiveness of the proposed algorithm in terms of solution quality. •Distribution characteristics of solutions are extracted by adaptive clustering.•A new recombination restriction strategy in reproduction is designed .•An adaptive adjustment of mating probability is proposed.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2020.06.010