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A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems

A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems

Oğuzhan Ahmet Arık, Mehmet Duran Toksarı
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 17
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861140|DOI: 10.4018/IJAMC.2021070109
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MLA

Arık, Oğuzhan Ahmet, and Mehmet Duran Toksarı. "A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems." IJAMC vol.12, no.3 2021: pp.195-211. http://doi.org/10.4018/IJAMC.2021070109

APA

Arık, O. A. & Toksarı, M. D. (2021). A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems. International Journal of Applied Metaheuristic Computing (IJAMC), 12(3), 195-211. http://doi.org/10.4018/IJAMC.2021070109

Chicago

Arık, Oğuzhan Ahmet, and Mehmet Duran Toksarı. "A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.3: 195-211. http://doi.org/10.4018/IJAMC.2021070109

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Abstract

This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem.

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