Car sequencing in mixed-model assembly lines from the perspective of logistics optimisation

Abstract

The objective of car sequencing problem is to find an optimal permutation for a collection of cars sharing the same basic product model to be built in the same production line of car plants. Competence in car sequencing is of great significance for efficient use of mixed-model assembly lines in that the launching sequence of cars to the lines imposes a heavy effect on the performance of logistics as well as the workload assigned to a sequence of workstations of the line. The main effort of this paper is to find an optimal permutation of cars from the perspective of logistics optimisation, considering the ratio constraint of respective workstations. However, the uncertainty caused by the stochastic arrival of customer orders in a built-to-order environment increases the complexity of car sequencing problems, and the logistics activities in particular. Confronted with the uncertainty and complexity, we put the car sequencing into a more vivid and intuitive scenario by simulating the behaviour of materials. To begin with, the logistics activities occurring in a specific automobile assembly line is investigated. Secondly, the ratio constraint of car sequencing problem is examined and a logistics model is built. Using the genetic algorithm toolbox embedded in Matlab7.0 package, we obtain subsequently an optimised permutation satisfying the proposed objective. Finally, the proposed logistics model is validated by the simulation results.