Stochastic Local Search for Solving Chance-Constrained Multi-Manned U-shaped Assembly Line Balancing Problem with Time and Space Constraints

Zakarai, Mohammad; Zaher, Hegazy; Ragaa, Naglaa

doi:10.51201/jusst/21/04242

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…To the best of knowledge, the only work that considers the combination of UALBP and MALBP is presented by Zakaraia et al (2021), which the problem was sovled using stochastic local search (SLS). The proposed DE algorithm herein is more intellegent than SLS, where the proposed DE contains better priority structure for constructing feasible solutions and it contains learning procedures to ignore the worse solutions from the search space by replacing them with new random ones to increase exploration.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Zakaraia

Zaher

Ragaa

2022

Int. J. Prod. Manag. Eng.

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Zakaraia

Zaher

Ragaa

2022

Int. J. Prod. Manag. Eng.

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Optimización de trabajadores y estaciones de trabajo en líneas de ensamble multi-tripuladas mediante algoritmos genéticos

Seck-Tuoh-Mora,

Anaya-Fuentes,

Hernández-Romero

et al. 2024

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Las líneas de ensamble son un mecanismo de producción que históricamente ha presentado beneficios económicos y de calidad en las organizaciones; sin embargo, diferentes problemas se presentan durante su ejecución. Entre estos destaca el problema del modelo de balanceo de líneas de ensamble con estaciones multi-tripuladas, el cual suele presentarse con mayor frecuencia en las organizaciones industriales que fabrican productos de dimensiones medianas y grandes, en comparación con el modelo de un solo tripulante por estación de trabajo; a pesar de ello, en la literatura encontramos una mayor tendencia a estudiar el segundo caso. Pocos estudios hacen referencia al primero. Adicionalmente, los métodos de búsqueda exhaustiva como los de programación lineal han encontrado barreras debido a la complejidad computacional, por lo cual las investigaciones sobre este problema se han centrado en utilizar heurísticos para su solución, en la búsqueda de algoritmos que sean cada vez más eficientes. Por lo anterior, el presente trabajo propone un algoritmo genético que en nuestro conocimiento no se ha utilizado en la búsqueda por minimizar el número de trabajadores y el número de estaciones de trabajo para el modelo de balanceo de líneas de ensamble con estaciones multi-tripuladas. Además, se propone una nueva función de costos la cual pondera el número de estaciones de trabajo y trabajadores, castigando soluciones con tiempos muertos altos para evitar su selección. Los resultados del algoritmo propuesto son evaluados mediante la comparación de instancias de prueba presentadas en la literatura. El algoritmo está disponible en <https://github.com/juanseck/GAMmALB>.

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Genetic Crossover Operator in Local Search of the Assembly Line Balancing Problem

Anaya-Fuentes,

Rivera-Gómez,

Hernández-Romero

et al. 2024

Advances in Business Information Systems and Analytics

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One of the biggest challenges facing the industry today lies in the assignment of robots to the workstations of production processes in order to perform tasks collaboratively between robots and human labor; this is known as Cobots. This document proposes to minimize the number of workstations by assigning robots, to reduce production costs. Additionally, it seeks to minimize the number of workers by assigning Cobots to a percentage of workstations. Due to the above, in this research, the Assembly Line Balancing Problem Type I (SALBP-1) is adapted with a Cobot assignment problem. This problem is considered NP-hard, so the use of a hybrid heuristic is proposed in the search to minimize the number of workers and the number of workstations. The hybrid consists of a modified genetic algorithm applying a local search within the genetic selection operator. The results demonstrate a decrease in the assignment of workstations, as well as a decrease in assignment of workers when using Cobots in some workstations using the proposed algorithm for the benefit of organizations.

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Stochastic Local Search for Solving Chance-Constrained Multi-Manned U-shaped Assembly Line Balancing Problem with Time and Space Constraints

Cited by 3 publications

References 31 publications

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

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

Optimización de trabajadores y estaciones de trabajo en líneas de ensamble multi-tripuladas mediante algoritmos genéticos

Genetic Crossover Operator in Local Search of the Assembly Line Balancing Problem

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