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

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

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Article ID: iaor20121195
Volume: 62
Issue: 1
Start Page Number: 206
End Page Number: 215
Publication Date: Feb 2012
Journal: Computers & Industrial Engineering
Authors: ,
Keywords: heuristics: genetic algorithms, optimization: simulated annealing
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.

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