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

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.

► This paper presents a genetic algorithm based method.
► It aims at solving the balancing and sequencing problems of the mixed-model U-lines.
► Our method considers parallel workstations assignment and zoning constraints.
► A new fitness function aiming at minimizing the number of workstations is adapted.
► It also deals with smoothing the workload balance between and within workstations.