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 and Mehmet Duran Toksarı
Additional contact information
Oğuzhan Ahmet Arık: Nuh Naci Yazgan University, Turkey
Mehmet Duran Toksarı: Erciyes University, Turkey

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 3, 195-211

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2021070109 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:12:y:2021:i:3:p:195-211

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().