Hybrid genetic algorithm to solve resource constrained assembly line balancing problem in footwear manufacturing

This paper aims to develop a hybrid genetic algorithm (HGA) to solve the resource constrained assembly line balancing problem (RCALBP) in the sewing line of a footwear manufacturing plant. Sewing, which is the most critical process in footwear manufacturing, has a series of processes, such as punching, trimming, attaching shoelaces. RCALBP in the sewing line considers not only the precedence constraints of product assembly but also the resource constraints, such as operators and equipment. A novel HGA that includes two stages is proposed to optimize the resources in the sewing line. The first stage uses the priority rule-based method (PRBM) to determine the feasible solutions of assigning tasks and machines to workstations. The solutions of PRBM are used to construct the initial population of genetic algorithm (GA) in the second stage. To ensure that the solution of GA is feasible, a two-point-order crossover with the new technique of searching feasible solution patterns is proposed. Moreover, the mutation procedure of GA is modified to avoid the building block from breaking, which may cause unfeasible solutions in RCALBP. A self-tuning method is also applied recursively to exclude unfeasible solutions. The proposed HGA is compared with the manual procedure adopted practically in factories, the existing heuristic model in the literature, and the traditional GA. Based on actual data from a footwear factory, computational results demonstrate that the proposed HGA can achieve better results than the other algorithms.