Genetic and Memetic Algorithms for Sequencing a New ‎JIT Mixed-Model Assembly Line

This paper presents a new mathematical programming model for the bi-criteria mixed-model assembly ‎line balancing problem in a just-in-time (JIT) production system. There is a set of criteria to judge ‎sequences of the product mix in terms of the effective utilization of the system. The primary goal of this ‎model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. Because of its ‎complexity to be optimally solved in a reasonable time, we propose and develop two evolutionary meta-‎heuristics based on a genetic algorithm (GA) and a memetic algorithm (MA). The proposed heuristics are ‎evaluated by the use of random iterations, and the related results obtained confirm their efficiency and ‎effectiveness in order to provide good solutions for medium and large-scale problems.‎