Job shop scheduling with the Best-so-far ABC
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Author list: Banharnsakun A., Sirinaovakul B., Achalakul T.
Publisher: Elsevier
Publication year: 2012
Journal: Engineering Applications of Artificial Intelligence (0952-1976)
Volume number: 25
Issue number: 3
Start page: 583
End page: 593
Number of pages: 11
ISSN: 0952-1976
Languages: English-Great Britain (EN-GB)
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Abstract
The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the solution direction toward the Best-so-far solution rather a neighboring solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The solution quality is measured based on Best, Average, Standard Deviation (S.D.), and Relative Percent Error (RPE) of the objective value. The results demonstrate that the proposed method is able to produce higher quality solutions than the current state-of-the-art heuristic-based algorithms. ฉ 2011 Published by Elsevier Ltd. All rights reserved.
Keywords
Job Shop Scheduling Problem (JSSP), Variable Neighboring Search (VNS)
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