A genetic algorithm-based approach for single-machine scheduling with learning effect and release time
type: Item
instance of: scholarly article
title: A genetic algorithm-based approach for single-machine scheduling with learning effect and release time
zbMATH Open document ID: 1407.90160
DOI: 10.1155/2014/249493
author: Der-Chiang Li
author: Peng-Hsiang Hsu
author: Chih-Chieh Chang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: The way to gain knowledge and experience of producing a product in a firm can be seen as new solution for reducing the unit cost in scheduling problems, which is known as ``learning effects.'' In the scheduling of batch processing machines, it is sometimes advantageous to form a nonfull batch, while in other situations it is a better strategy to wait for future job arrivals in order to increase the fullness of the batch. However, research with learning effect and release times is relatively unexplored. Motivated by this observation, we consider a single-machine problem with learning effect and release times where the objective is to minimize the total completion times. We develop a branch-and-bound algorithm and a genetic algorithm-based heuristic for this problem. The performances of the proposed algorithms are evaluated and compared via computational experiments, which showed that our approach has superior ability in this scenario.
Mathematics Subject Classification ID: 90B35
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C57
zbMATH DE Number: 7015991
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/249493
OpenAlex ID: W2083550095
Wikidata QID: Q59065051
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description: scientific article; zbMATH DE number 7015991