An Approximate Solution to Deterministic Kanban Systems

Moeeni, Farhad; Chang, Yih–Long

doi:10.1111/j.1540-5915.1990.tb00337.x

Cited by 24 publications

(21 citation statements)

References 7 publications

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“…They have also deployed advanced manufacturing technologies such as computerised process control, automatic guided vehicles (AGV), and computerised short setups for change of jobs. The traditional Kanban system was designed for a deterministic environment (Moeeni and Chang 1990). The number of Kanbans was usually held fixed in a deterministic environment.…”

Section: A Brief Literature Review: Kanban Controlling and Schedulingmentioning

confidence: 99%

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

Lee

2007

International Journal of Production Research

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This paper evaluates several artificial intelligence heuristics for a simultaneous Kanban controlling and scheduling on a flexible Kanban system. The objective of the problem is to minimise a total production cost that includes due date penalty, inventory, and machining costs. We show that the simultaneous Kanban controlling and scheduling is critical in minimising the total production cost (approximately 30% cost reduction over scheduling without a Kanban controlling). To identify the most effective search method for the simultaneous Kanban controlling and scheduling, we evaluated widely known artificial intelligence heuristics: genetic algorithm, simulated annealing, tabu search, and neighbourhood search. Computational results show that the tabu search performs the best in terms of solution quality. The tabu search also requires a much less computational time than the genetic algorithm and the simulated annealing. To further improve the solution quality and computational time for a simultaneous Kanban controlling and scheduling on a flexible Kanban system, we developed a two-stage tabu search. At the beginning of the tabu search process, an initial solution is constructed by utilising the customer due date information given by a decision support system. The two-stage tabu search performs better than the tabu search with a randomly generated initial solution in both the solution quality and computational time across all problem sizes. The difference in the solution quality is more pronounced at the early stages of the search.

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Section: A Brief Literature Review: Kanban Controlling and Schedulingmentioning

confidence: 99%

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

Lee

2007

International Journal of Production Research

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“…We set the container size to 10 units and the kanban review period to 480 min, so a detached kanban becomes a production order at the beginning of the next day. We then set the number of kanbans to 15 using the procedure of Moeeni and Chang (1990).…”

Section: A Kanban Examplementioning

confidence: 99%

So many factors, so little time…Simulation experiments in the frequency domain

Sanchez

Moeeni

Sánchez

2006

International Journal of Production Economics

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“…Multistage, multiperiod mathematical models have been developed to model lines having deterministic processing times (Bitran and Chang 1987, Moeeni and Chang 1990, Bard and Golany 1991. Several queueing and Markov chain models have been developed which incorporate variations in processing time (Jordan 1988, Deleersnyder et al 1989, Berkley 1990, Wang and Wang 1991; these models are limited in application to small production lines and systems whose behaviour conforms to the underlying assumptions on the system variables (e.g.…”

Section: Analytic Versus Simulation Models Of Kanban Systemsmentioning

confidence: 99%

Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

Moore¹,

Gupta²

1999

International Journal of Production Research

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Modelling and analysis of kanban-controlled (just-in-time) production systems under realistic assumptions presents a number of challenges, including the ability to conduct both qualitative and quantitative analysis, and the ability to model control policies. These challenges are due, in part, to station interdependence, blocking and starvation due to limited bu er spaces, and the necessity of modelling both material and kanban¯ows. Petri nets (PNs) have recently emerged as a promising approach for modelling manufacturing systems. PNs are a graphical and mathematical technique useful for modelling concurrent, asynchronous, distributed, parallel, non-deterministic and stochastic systems. PN models can be analysed to determine both their qualitative and quantitative properties. In this paper, we develop stochastic, coloured PN (SCPN) models of a JIT system utilizing two di erent kanban control policies: a traditional kanban system (TKS) policy and a¯exible kanban system (FKS) policy. The resulting models can be used to represent JIT systems of arbitrary size, producing single or multiple types of products, with ® xed order points 1. The models are shown to be live and bounded, and can be simulated to produce quantitative results. Sample simulation results are presented to illustrate the models' capabilities.

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An Approximate Solution to Deterministic Kanban Systems

Cited by 24 publications

References 7 publications

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

So many factors, so little time…Simulation experiments in the frequency domain

Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

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