A genetic algorithm based approach for simultaneously balancing and sequencing of mixed-model U-lines with parallel workstations and zoning constraints

Authors:
Alper Hamzadayi;Gokalp Yildiz
Affiliations:
Dokuz Eylul University, Engineering Faculty, Department of Industrial Engineering, 35160 Tinaztepe Campus, Buca, Izmir, Turkey;Dokuz Eylul University, Engineering Faculty, Department of Industrial Engineering, 35160 Tinaztepe Campus, Buca, Izmir, Turkey
Venue:
Computers and Industrial Engineering
Year:
2012

Citing 2
Cited 4

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Assembly Line Design: The Balancing of Mixed-Model Hybrid Assembly Lines with Genetic Algorithms (Springer Series in Advanced Manufacturing)
A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints

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A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines

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Abstract

This paper presents a Priority-Based Genetic Algorithm (PGA) based method for the simultaneously tackling of the mixed-model U-shape assembly line (MMUL) line balancing/model sequencing problems (MMUL/BS) with parallel workstations and zoning constraints and allows the decision maker to control the process to create parallel workstations and to work in different scenarios. In the presented method, simulated annealing based fitness evaluation approach (SABFEA) is developed to be able to make fitness function calculations easily and effectively. A new fitness function is adapted to MMULs for aiming at minimizing the number of workstations as primary goal and smoothing the workload between-within workstations by taking all cycles into consideration. A numerical example to clarify the solution methodology is presented. Performance of the proposed approach is tested through sets of test problem with randomly generated minimum part sets. The results of the computational experiments indicate that SABFEA works with PGA very concordantly; and it is an effective method in solving MMUL/BS with parallel workstations and zoning constraints.