Design of multi-stage adaptive kanban system

Sivakumar, Gandhi; Shahabudeen, P.

doi:10.1007/s00170-007-1093-x

Cited by 22 publications

(8 citation statements)

References 24 publications

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“…Simulation modelling approaches, such as in Spearman et al (1990), Bonvik et al (1997), Geraghty and Heavey (2005) and Hochreiter (1999) and some analytical techniques have been applied in conducting studies to determine the suitability of available strategies under different conditions. Markov time chain analysis, as in Sivakumar and Shahabudeen (2008), multiclass queuing network approximation technique, for example Satyam and Krishnamurthy (2008), Baynat (2001) and Duri et al (1995), and state space representation approach, as in the case of Boonlertvanich (2005) and Karaesmen and Dallery (2000), are some of the most commonly used analytical methods of study. But according to Gupta et al (1999) as well as Kochel and Nielander (2002), simulation is the preferred approach because of its ability to handle the dynamics that occur in real manufacturing systems, where analytical methods have to make unrealistic assumptions to avoid intractability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All of these strategies except the last two are considered in this study. Another class of KCS found in the literature are the flexible or adaptive KCS (Gupta et al , 1999; Sivakumar and Shahabudeen, 2008) that propose real time adjustment of the number of Kanbans in response to demand fluctuation and inventory build up.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Genetic algorithms, as in the case of Sivakumar and Shahabudeen (2008), Kochel and Nielander (2002), Bowden et al (1996), Cochran and Kaylani (2008) and Szczerbicka et al (1998), Simulated Annealing, as in Geraghty and Heavey (2004) and Shahabudeen et al (2003) and Tabu search used by Alabas et al (2000), are some of the optimisation methods that have been linked to simulation models in past studies. Some simulation software have inbuilt optimisation blocks that use some of these algorithms; ExtendSim for instance uses a genetic algorithm to optimise the objective function specified for it.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some simulation software have inbuilt optimisation blocks that use some of these algorithms; ExtendSim for instance uses a genetic algorithm to optimise the objective function specified for it. Sivakumar and Shahabudeen (2008), Karaesmen and Dallery (2000) and Nori and Sarker (1998) built objective functions in the form of an equation consisting of inventory holding and demand backlog costs to be minimised. Another commonly used approach, found in Bonvik et al (1997) and Geraghty and Heavey (2005) is to set the objective function to achieve a target service level.…”

Section: Literature Reviewmentioning

confidence: 99%

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Evaluation of production control strategies for negligible‐setup, multi‐product, serial lines with consideration for robustness

Olaitan

Geraghty

2013

Journal of Manufacturing Technology Management

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PurposeThe aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.Design/methodology/approachDiscrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban control strategy (EKCS) and generalised kanban control strategy (GKCS) as well as the shared versions of EKCS and GKCS. All‐pairwise comparisons and a ranking and selection technique were employed to compare the performances of the strategies and select the best strategy without consideration of robustness to uncertainty. A latin hypercube sampling experimental design and stochastic dominance testing were utilised to determine the preferred strategy when robustness to uncertainty is considered.FindingsThe findings of this work show that shared GKCS outperforms other strategies when robustness is not considered. However, when robustness of the strategies to uncertainty in the production environment is considered, the results of our research show that the dedicated EKCS is preferred. The effect of system bottleneck location on the inventory accumulation behaviour of different strategies is reported and this was also observed to have a relationship to the nature of a PCS's kanban information transmission.Practical implicationsThe findings of this study are directly relevant to industry where increasing market pressures for product diversity require operating multi‐product production lines with negligible set‐up times. The optimization and robustness test approaches employed in this work can be extended to the analysis of more complicated system configurations and higher number of product types.Originality/valueThis work involves further investigation into the performance of multi‐product kanban‐like PCS by examining their robustness to common sources of uncertainties after they have been initially optimized for base scenarios. The results of the robustness tests also provide new insights into how dedicated kanban card allocation policies might offer higher flexibility and robustness over shared policies under conditions of uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of production control strategies for negligible‐setup, multi‐product, serial lines with consideration for robustness

Olaitan

Geraghty

2013

Journal of Manufacturing Technology Management

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“…can effectively describe the relationship between the various events of DEDS, and to analyze the dynamic nature of the system, is a collection of system modeling,analysis, design and control for the integration of mathematical tools [9,10] . This paper use Petri nets to describe the product production process.…”

Section: Introductionmentioning

confidence: 99%

Simulation on the Push/Conwip Control Mode for Manufacturing Process

Wang

Cui

2013

2013 6th International Conference on Intelligent Networks and Intelligent Systems

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This research was conducted using simulation optimization methods to solve the bottleneck problem in manufacturing process, aiming at optimize the system performance. The Petri nets is used to describe the production process under Push/Conwip control mode for a typical JIT flexible manufacturing system. The ExSpect simulation platform is used to analog JIT flexible manufacturing system production process. A large amount of simulation tests have been done, with different quantity of demands and different buffer size limitation. Simulation processes give out visual description of the machine utilization, the position of bottleneck, and the relationship between the number of Kanban with the system performance, as well as the characteristics of control modes, and other helpful information. Simulation results show that the problem of bottleneck or hunger of the production line can be solve by setting bottleneck area kanban number and buffer size; and setting processing ratio between bottleneck area and parallel area can eliminate the bottleneck and improve system performance.

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