A material handling scheduling method for mixed-model automotive assembly lines based on an improved static kitting strategy

Since the diversification of customer demands poses a great challenge for manufacturing enterprises and the scheduling problem of material handling affects the efficiency of assembly lines, this paper proposes a novel scheduling method, an improved static kitting strategy, to solve the scheduling problems of the material handling for automotive mixed-model assembly lines (MMALs) based on line-integrated supermarkets. Firstly, an integer programming mathematical model is established with the objective of minimizing the number of logistic workers. Then, an improved static kitting strategy is presented to solve the problem and a model based on graph theories is constructed to transform the scheduling problem to a mathematical one. Afterwards, a Kuhn-Munkres algorithm and an elite opposition-based learning adaptive dynamic differential evolution algorithm, named EOADDE algorithm, is developed to solve the scheduling problem. The elite opposition-based learning (EOL) and self-adaptive operators are applied to the proposed EOADDE algorithm to enhance the local search ability and the convergence speed. Finally, computational experiments of the proposed algorithm are carried out compared with benchmark algorithms, and the feasibility and effectiveness of proposed methods are verified by results.