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Title: Discrete Artificial Algae Algorithm for solving Job-Shop Scheduling Problems
Authors: Şahman, Mehmet Akif
Korkmaz, Sedat
Keywords: Discrete optimization
Encoding schemes
Job Shop Scheduling Problem
Metaheuristic algorithms
Encoding (symbols)
Heuristic algorithms
Heuristic methods
Job shop scheduling
Signal encoding
Algorithm for solving
Coding scheme
Discrete optimization
Encoding schemes
Exact methods
Heuristics algorithm
Job shop scheduling problems
Meta-heuristics algorithms
Position value
Proper solutions
Issue Date: 2022
Publisher: Elsevier B.V.
Abstract: The Job-Shop Scheduling Problem (JSSP) is an NP-hard problem and can be solved with both exact methods and heuristic algorithms. When the dimensionality is increased, exact methods cannot produce proper solutions, but heuristic algorithms can produce optimal or near-optimal results for high-dimensional JSSPs in a reasonable time. In this work, novel versions of the Artificial Algae Algorithm (AAA) have been proposed to solve discrete optimization problems. Three encoding schemes (Random-Key (RK), Smallest Position Value (SPV), and Ranked-Over Value (ROV) Encoding Schemes) were integrated with AAA to solve JSSPs. In addition, the comparison of these three encoding schemes was carried out for the first time in this study. In the experiments, 48 JSSP problems that have 36 to 300 dimensions were solved with 24 different approaches obtained by integrating 3 different coding schemes into 8 state-of-the-art algorithms. As a result of the comparative and detailed analysis, the best results in terms of makespan value were obtained by integrating the SPV coding scheme into the AAA method. © 2022 Elsevier B.V.
ISSN: 0950-7051
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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