This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated to specify the destination station and parts quantity of each delivery for minimizing line-side inventory levels.
An exact backtracking procedure integrating with dominance properties is presented to cope with small-scale instances. As for real-world instances, this study develops a modified discrete artificial bee colony (MDABC) metaheuristic. The neighbor search of MDABC is redefined by a novel differential evolution loop and a breadth-first search.
The backtracking method has efficaciously cut unpromising branches and solved small-scale instances to optimality. Meanwhile, the modifications have enhanced exploitation abilities of the original metaheuristic, and good approximate solutions are obtained for real-world instances. Furthermore, inventory peaks are avoided according to the simulation results which validates the effectiveness of this mathematical model to facilitate an efficient JIT parts supply.
This study is applicable only if the breakdown of transport devices is not considered. The current work has effectively facilitated the P2P JIT logistics scheduling in automotive assembly lines, and it could be modified to tackle similar distribution problems featuring time-varying demands.
Both limited vehicle capacities and no stock-outs constraints are considered, and the combined routing and loading problem is solved satisfactorily for an efficient JIT supply of material in automotive assembly lines.
