Goal Chasing Method Research Articles

Sequencing Method for Products with Consideration of Assembly Time and Parts Delivery Cycle Time

To keep the consumption rate of each part is very important for mixed-model assembly line scheduling in JIT production system. The goal chasing method (GC) and a number of papers have been proposed to achieve the parts usage leveling goal. These methods keep the consumption speed of parts constant as much as possible, and aim to decrease the amount of the stock in pre-process indirectly. In this paper, we set objective function based on the stock in pre-process directly. The function is influenced by different assembly time and delivery cycle among each product. And we have proposed the sequencing method. The method is concept of time-based goal chafing method (TBCG) with consideration in parts delivery cycle. By numerical experiments, effect of proposed method is presented.

Improving the Goal-Chasing Method to Solve the Car Sequencing Problem

Abstract In the automobile industry there is a need to maintain a certain order in the vehicles as they pass through the assembly line. In this paper, we use the Goal-chasing method developed by Toyota, to solve the Car Sequencing Problem. The concept of different weights for each option is introduced to improve the ordering of the sequence. Nelder-Mead method of nonlinear optimization is applied to obtain the weights of options that minimize the cost of the resulting sequence.

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.

A new heuristic method for mixed model assembly line balancing problem

The goal chasing method is a popular algorithm in JIT system for the mixed model assembly line balancing problem. It is simple and easy to implement, but it is a very greedy algorithm and uses up ‘good’ parts in the early sequence so that the whole performance of the solution is influenced. In this paper, we provide the definition of good parts and good remaining sequence and analyze their relationship with the optimal solution's objective function value. We develop a new heuristic algorithm called ‘variance algorithm’ the numerical experiments show that the new algorithm can yield better solution with little more computation overhead.

A sequencing problem to level part usage rates and work loads for a mixed-model assembly line with a bypass subline

To meet the diversification of consumer's preferences, mixed-model assembly lines were installed in many manufacturing plants. For example, in the manufacturing of trucks, a large variation exists in assembly times among different product types. The large variation reduces production efficiency and may cause a line stoppage. The variation in the assembly times can be reduced by installing a bypass subline which processes a portion of assembly operations of products with relatively longer assembly times. This paper deals with a sequencing problem with a bypass subline. The sequencing problem with goals of levelling the part usage rates and work loads is formulated, and three different algorithms based on goal chasing method, tabu search and dynamic programming are developed to solve the problem.

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 Sequencing Problem in a Mixed-Model Assembly Line with Bypass Subline. To Level Part Consumption Rates and Work Loads.

There exists a large variation in the assembly times among different product models, for example, in the manufacturing of trucks and prefabricated houses. This reduces production efficiency and may cause a line stoppage. The variation of the assembly times can be reduced by installing a bypass subline which processes a part of assembly operations of products with relatively longer assembly times. Even if installing the bypass subline, both the part usage rates and work loads should be leveled by sequencing different products. In this paper, the sequencing problem with goals of levelling both the part usage rates and work loads is formulated as a nonlinear integer programming problem. Two algorithms based on dynamic programming and goal chasing method are developed to solve the problem and their efficiency is discussed through numerical examples.

A simulation analysis for two-level sequence-scheduling for just-in-time (JIT) mixed-model assembly lines

Toyota's goal-chasing method has proved effective for dealing with practical-sized sequencing problems for mixed-model assembly lines in just-in-time (JIT) production systems. Miltenburg introduced the multi-level concept to JIT assembly line sequencing, and his remark about Toyota's neglect of product ‘usage’ smoothing (PUS) when determining the assembly-line product sequence, primarily gave impetus to this study. We note that for most JIT systems, PUS would only be of practical significance, if it helps to achieve sub-assemblies and parts usage smoothing (SUS). In this paper, we conduct a simulation experiment for two-level sequence scheduling for JIT assembly lines. Using the SUS objective value as the metric, the results show that in a comparatively higher percentage of cases (relative to single-level SUS objective scheduling), which can be reasonably considered as statistically significant, better sequences are produced, This phenomenon is found to apply to Toyota's SUS objective as well as Miltenburg's.

Modified goal chasing for products with non-zero/one bills of material

Abstract One goal of the Toyota production system is a level usage of parts in assembly. To this end Toyota developed a ‘goal chasing’ algorithm for sequencing vehicles in its assembly plants. Since the goal chasing method 1 was computationally too burdensome for most real-world applications, Toyota developed a faster version called the ‘goal chasing method 2.’ While this modified version performs well in automotive assembly for which it was designed, it cannot sequence products with non-zero/one part requirements. For example printed circuit boards have non-zero/one product-part usage matrices. These and other electronic products can have any number of a particular chip, resistor, capacitor, or other electronic component. Hence the product-part usage matrix for these electronic products is not zero/one, and the goal chasing method 2 cannot be used. We present another modification of the goal chasing algorithm that is faster than Toyota's goal chasing method 2, and can sequence products with non-zero/one ...

Scheduling mixed-model multi-level just-in-time production systems

Many companies are converting their mixed-model multi-level production systems to just-in-time systems. This requires reducing set-up times so that small-lot production can be run. In this paper a theoretical basis is developed for scheduling these systems, and scheduling algorithms and heuristics are developed. The only other known scheduling method (the goal-chasing method), developed and used by Toyota, is shown to be a special case of these heuristics.