A comparative analysis of sequencing heuristics for solving the Toyota Goal Chasing problem

In this paper, the sequencing of a mixed model paced assembly line is investigated assuming the component parts usage smoothing as the goal of the sequence selection. This sequencing problem, commonly known as Toyota Goal Chasing method, is studied here taking into account not only the traditional Goal Chasing approaches, which assume zero-length assembly lines, but also models which consider the effective length of the assembly line. This means that the number of workstations and their extensions become critical parameters in the selection of the optimal sequence of models to be assembled: in fact, the epochs corresponding to the requirement of different components vary in accordance to the values of the line parameters. The sequencing of the parts is carried out here through a set of heuristic procedures, the commonly adopted Goal Chasing algorithms and a simulated annealing, whose performances are compared with respect to different line scenarios. In particular, the numbers of workstations, parts to be worked and components to be assembled are varied to statistically test their influence on the efficiency of the optimizing procedures and on the differences between zero and finite length approaches.