Solving stochastic multi-manned U-shaped assembly line balancing problem using differential evolution algorithm

Authors

  • Mohammad Zakaraia Cairo University
  • Hegazy Zaher Cairo University
  • Naglaa Ragaa Cairo University

DOI:

https://doi.org/10.4995/ijpme.2021.16084

Keywords:

Metaheurisitcs, Differential evolution algorithm, U-shaped assembly line balancing problem, Multi-manned assembly line balancing problem, Chance-constrained programming

Abstract

The U-shaped assembly lines help to have more flexibility than the straight assembly lines, where the operators can perform tasks in both sides of the line, the entrance and the exit sides. Having more than one operator in any station of the line can reduce the line length and thereby affects the number of produced products. This paper combines the U-shaped assembly line balancing problem with the multi-manned assembly line balancing problem in one problem. In addition, the processing times of the tasks are considered as stochastic, where they are represented as random variables with known means and variances. The problem is formulated as a mixed-integer linear programming and the cycle time constraints are formulated as chance-constraints. The proposed algorithm for solving the problem is a differential evolution algorithm. The parameter of the algorithm is optimized using experimental design and the computational results are done on 71 adapted problems selected from well-known benchmarks.

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Author Biographies

Mohammad Zakaraia, Cairo University

PhD candidate in operations research, Faculty of graduate studies for statistical research

Hegazy Zaher, Cairo University

Professor Doctor in mathematical statistics, Faculty of graduate studies for statistical research

Naglaa Ragaa, Cairo University

Associate professor in operations research, Faculty of graduate studies for statistical research

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Published

2022-01-31

How to Cite

Zakaraia, M., Zaher, H., & Ragaa, N. (2022). Solving stochastic multi-manned U-shaped assembly line balancing problem using differential evolution algorithm. International Journal of Production Management and Engineering, 10(1), 13–22. https://doi.org/10.4995/ijpme.2021.16084

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Papers