Discrete Artificial Algae Algorithm for solving Job-Shop Scheduling Problems

• Published in: Knowledge-based systems Vol. 256; p. 109711
• Main Authors: Şahman, Mehmet Akif, Korkmaz, Sedat
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28-11-2022
• Subjects: Discrete optimization; Encoding schemes; Job Shop Scheduling Problem; Metaheuristic algorithms; Encoding schemes; Discrete optimization; Job Shop Scheduling Problem; Metaheuristic algorithms
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2022.109711

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.