Toyota Production System

Monden, Yasuhiro

doi:10.1007/978-1-4615-9714-8

Cited by 609 publications

(219 citation statements)

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“…Monden (1983) develops a greedy heuristic, the Goal Chasing Method (GCM), to level parts usage by selecting the model that yields the minimum part-usage variation at any level. Hence, it is a myopic heuristic that generally yields low quality solutions.…”

Section: Existing Solution Methodsmentioning

confidence: 99%

“…These studies have different objectives such as minimizing line length or line stoppages, but their common feature is that they all study the final assembly line and ignore other levels in the multi-level production systems. The first analyses of mixed-model, multi-level production systems are made by Monden (1983), and Miltenburg and Sinnamon (1989). A detailed explanation of research on the MMAL sequencing problem is given below (also summarized in table 1).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similar to Monden (1983), we study variability at the subassembly level. Variability is typically calculated as the deviation between actual usage and desired usage at the subassembly level.…”

Section: Introductionmentioning

confidence: 99%

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Mixed-model assembly line sequencing using beam search

Erel

Göçgün

Sabuncuoğlu

2007

International Journal of Production Research

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In today's manufacturing environments, companies have to produce a large variety of products in small quantities on a single assembly line. In this paper, we use a beam search (BS) approach to solve the model-sequencing problem of mixed-model assembly lines (MMALs). Specifically, we develop six BS algorithms for part-usage variation and load-leveling performance measures. The results of computational experiments indicate that the proposed BS methods are competitive with the well-known heuristics in the literature. A comprehensive bibliography is also provided.

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Section: Existing Solution Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Mixed-model assembly line sequencing using beam search

Erel

Göçgün

Sabuncuoğlu

2007

International Journal of Production Research

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“…Highly influenced by the loss of market share, some of the major North American companies adopted JIT by the early 1980's (Schonberger, 1986). The main objective of JIT is to increase productivity and reduce cost by completely eliminating waste; this is done by producing just what is needed, in the needed quantity, when needed (Monden, 1993). A key concept of the JIT production system is a flexible work force.…”

Section: Rationalementioning

confidence: 99%

“…A key concept of the JIT production system is a flexible work force. In order to avoid excess work force, operators must be trained to be skilled in multiple jobs of various processes (Monden, 1993). This caused a significant shift in the quality inspection task responsibilities.…”

Section: Rationalementioning

confidence: 99%

The effects of multitasking on quality inspection in advanced manufacturing systems

Pesante

Williges

Woldstad

2001

Hum Ftrs & Erg Mfg Svc

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Technological and strategic developments have changed the role of human operators in the manufacturing environment. The highly specialized work force of the low-tech manufacturing system has evolved into the multi-functional work force of the high-tech manufacturing system. Among the multiple tasks that an operator is expected to conduct in advanced manufacturing systems (AMS) are job scheduling, inventory planning, machine set-up, problem solving, and quality inspection.The quality inspection task in AMS consists of a search component, frequently conducted by a machine, and a decision making component conducted by the operator.This quality inspection system is often referred to as a hybrid inspection system (HIS). It has been demonstrated that in general the performance of HIS is better than that of pure human or pure automated inspection systems. This research investigated the effects of different types of defects (presented at the same time in the inspected parts), multitasking (concurrently conducting independent tasks), and their interaction on the operator's performance in the quality inspection task (with a memorized quality criteria) in an AMS.The results indicate that the performance of the operator in the quality inspection task while multitasking in an AMS will be determined not only by the variety of defects that can be present in the inspected parts, but also by the mental processing resources required to meet the demand imposed by the multiple independent tasks and the memorized quality criteria. The best performance will be obtained when the additional tasks' load minimizes the monotony of the quality inspection task without interfering with the processing resources needed for the memorized quality criteria.iii Dedicated to the memory of my father, José Pesante iv

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