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A Greedy Randomized Adaptive Search for Solving Chance-Constrained U-Shaped Assembly Line Balancing Problem

A Greedy Randomized Adaptive Search for Solving Chance-Constrained U-Shaped Assembly Line Balancing Problem

Mohammad Zakaraia, Hegazy Zaher, Naglaa Ragaa
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 18
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.298310
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MLA

Zakaraia, Mohammad, et al. "A Greedy Randomized Adaptive Search for Solving Chance-Constrained U-Shaped Assembly Line Balancing Problem." IJAMC vol.13, no.1 2022: pp.1-18. http://doi.org/10.4018/IJAMC.298310

APA

Zakaraia, M., Zaher, H., & Ragaa, N. (2022). A Greedy Randomized Adaptive Search for Solving Chance-Constrained U-Shaped Assembly Line Balancing Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-18. http://doi.org/10.4018/IJAMC.298310

Chicago

Zakaraia, Mohammad, Hegazy Zaher, and Naglaa Ragaa. "A Greedy Randomized Adaptive Search for Solving Chance-Constrained U-Shaped Assembly Line Balancing Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-18. http://doi.org/10.4018/IJAMC.298310

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Abstract

This paper discusses the U-shaped assembly line balancing problem in case of stochastic processing time. The problem is formulated using chance-constrained programming and the greedy randomized adaptive search procedure is used to solve the problem. In order to prove the efficiency of the proposed algorithm, 71 problems taken from well-known benchmarks are solved and compared with the theoretical lower bound and 13 of them were compared with another approach used to solve the same problem in another paper, which is beam search. The results show that 59 problems are the same as the theoretical aspiration lower bound. In addition, the results of 11 of 13 problems compared with beam search are the same and the results of 2 problems are better than beam search. The t-test statistics is applied and showed that there is no significance difference between the proposed algorithm and the theoretical lower bound thus, the proposed algorithm shows efficiency when compared with the aspired values of the theoretical lower bound.