Part-supply scheduling of automobile assembly line with hybrid teaching-learning-based optimization algorithm

A just-in-time part distribution model with multiple transportation devices was analyzed under consideration of no stock-outs constraints in order to solve the part-supply scheduling problem of the automobile assembly line. The problem domain was described and a mathematical programming model was developed to minimize the line-side inventory levels in the planning horizon. A hybrid teaching-learning-based optimization (HTLBO) approach was established for this complicated combinatorial optimization problem according to the framework of the standard teaching-learning-based optimization (TLBO). A specified encoding and decoding method was proposed to assign and sequence the distribution tasks on each device according to the nature of the proposed scheduling problem. A local search procedure was presented to enhance the exploration ability of the algorithm by incorporating with swap, reversion and insertion operators. A beam-search-based pruning method was proposed by using domain properties in order to enhance the algorithm's exploiting capability. Experiments were conducted. The simulation results validated the feasibility and effectiveness of the proposed scheduling algorithm. © 2018, Zhejiang University Press. All right reserved.