In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identi®cation of machine groups based on their ability to process speci®c part families. Past research has shown that one key aspect of attaining e cient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGA's solutions using the measurements of e ciency (Chandrasekharan and Rajagopalan 1986a) and e cacy (Kumar and Chandrasekharan 1990). The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.
When mixed‐model assembly lines use components fabricated in‐house, the demand for these components is not uniform over time and is affected by the sequence of models on the assembly line. Thus, without proper mixed‐model sequencing and the subsequent smoothing of component demand, the effectiveness of a just‐in‐time (JIT) production system is limited. This paper focuses on making component usage uniform. Five sequencing methods are reviewed, two suggested by Monden (1983) and used at Toyota (GC1 and GC2), and three proposed by Miltenburg (1989) (M‐A1, MA3H1, and M‐A3H2). Their performance is evaluated for the special case when all models use the same components. For all of the sequencing methods tested the mean absolute deviation of model usage varies directly with the number of models produced. There is no clear relationship between the mean absolute deviation of model usage and demand pattern or length of production sequence. Method M‐A3H2 produces the highest quality feasible solutions under all conditions tested. The relative performance of the methods does not appear to be related to the number of models, demand type, or length of production sequence. To compare methods for the more general case of different models requiring different components, a mixed integer programming (MIP) model is presented as a way to find an optimal sequence. The M1P creates optimal solutions but is too slow to be used in practice. The two goal chasing heuristics used by Toyota were considered on the basis of their ability to schedule production to use components linearly over time. It is shown that these methods differ widely in their ability to generate good sequences. The performance of both is best when the products assembled have simple product structures. When models require more than one of a given component or when models require many different components, the performance worsens. The difference in performance is very small for GC1, but significant for GC2.
In this paper several procedures for sequencing products on a mixed-model assemblyline in a just-in-time production system are statistically compared using simulation analysis. The procedures include two 'goal chasing' heuristics developed at Toyota that focus on constant component usage, an algorithm developed by Miltenberg designed to achieve uniform production rates for each model, a time spread method developed by the authors that smooths the work load at each assemblyline station, and the familiar batch sequencingprocedure frequently used in practice. These five sequencing procedures are evaluated according to four measures of assembly line inefficiency, work not completed, worker idleness, worker station time and a measure of variability in uniform component usage. The results provide assembly line managers with guidelines for selectingthe most effective sequencing procedure for achieving their operational objectives. IntroductionJust-in-time (JIT) production systems frequently include mixed-model assembly lines to produce a variety of different product models while maintaining quality without incurring excessive inventories. Monden (1983) in his book on JIT at Toyota specifically addresses the use of mixed-model assembly lines in a JIT environment. In his discussion of this topic, Monden points out that if products with relatively longer processing times are successively fed into the assembly line (i.e. usage is not constant), a delay in model completion will eventually occur, which may stop the (conveyor) line. In order to avoid this problem, the processing time at each station must be smoothed by sequencing models so that, in general, a model with relatively short processing time at a station follows soon after a model with relatively long processing time at that station. Also, the quantity of each part used per unit time must be as near constant as possible. It is crucial for the processes preceding the assembly line supplying components, that the demand for these components be uniform. Uniform demand allows the JTT 'pull' system to minimize work-in-process inventories. Thus there are two major considerations when sequencing models: avoiding unfinished work which may stop the conveyor and assuring uniform demand for the components feeding the assembly line.As the popularity of JIT has evolved during the 1980s a number of different sequencing methods have been developed and reported in the research literature. The purpose of this paper is to compare several of the more prominent procedures for sequencing products on a mixed-model assembly line in a JIT environment. Two of the sequencing procedures included in this evaluation, referred to as 'goal chasing method' and 'goal chasing method II', are heuristics developed at Toyota and
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