Kenji Kurashige

Time-based goal chasing method for mixed-model assembly line problem with multiple work stations

In sequencing problems for mixed-model assembly line in JIT production system, the Goal Chasing method (GC) is widely used for parts used leveling goal. The difference in assembly time of each product is not taken into consideration in the Goal Chasing method. Assembly time usually varies with product types. In recent years, the Time-Based Goal Chasing method (TBGC) has been proposed. The advantage of TBGC is to consider the influence of different assembly time of each product and idle time in production period. TBGC, however, has been only applied to single work station problems. In this paper, TBGC is applied to an assembly line problem with multiple work stations. Furthermore, the sequencing method and use of Simulated Annealing (SA) or Local Search (LS) for this problem are proposed.

Sequencing method for products in consideration of assembly time

In mixed-model assembly scheduling with a single workstation, there is a new sequencing method [time-based goal chasing method (TBGC)] in consideration of the different assembly times between jobs. In this paper, we propose a new objective function in expanding the TBGC. Furthermore, we propose some heuristic methods to minimize the objective function. And through numerical experiments, we show the effectiveness of our proposal.

Mixed-Model Assembly Line Scheduling in Consideration of Assembly Time

In a mixed-model assembly line problem, two objectives has been proposed to decide the product scheduling. One is to minimize the work congestion and the other is keeping the consumption rate of each part. In JIT production system, the latter one is more important, so the goal chasing method (GC) has been prposed to achieve this goal. The difference of assembly time among each product was not cnsidered in any solution algorithms. Recently, the time based goal chasing method (TBGC) has been proposed in which the different assembly time among each product is considered. The good point of TBGC is able to consider the idle time contained in an assembly line. However, TBGC is only applied to a single work station problem. In this paper, TBGC is applied to an assembly line and numerical experiments are presented.

A Method for Determining Relative Importance in AHP Using Directly Fuzzy Numbers for Modifiers

We present the method for determining the relative importance in AHP (Analytic Hierarchy Process) by using fuzzy numbers fitted to the decision makers feeling for every modifier. This method can use the fuzzy numbers having membership functions with arbitrary spread to represent the respective fuzzy feeling. We can expect that the results of this method fits the feeling of each decision maker better than the results of existing method.