Setting Inventory Levels of CONWIP Flow Lines via Linear Programming

Helber, Stefan; Schimmelpfeng, Katja; Stolletz, Raik

doi:10.1007/bf03342728

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…CONWIP is a mixture of a push-pull system. Many researchers have analyzed the CONWIP system: Geraghty and Heavey ( 2004), Marek et al (2001), Spearman et al (1990), Helber et al (2011), Gaury et al (2000), Ryan et al (2000), Herer and Masin (1997), and Gstettner and Kuhn (1996).…”

Section: Serial Production System Release Policymentioning

confidence: 99%

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Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

Muttulingam¹

2021

Preprint

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Learning and forgetting are two important characteristics in manufacturing environments where workers are cross-trained to increase their flexibility of adapting to different tasks. Cross-training is introduced by industries so that one worker can work on multiple stations. This thesis develops two models: (i) a probabilistic learning curve approach to the production lot size problem to determine the economic manufactured quantity (EMQ); (ii) a real options approach to the valuation of cross-training with product life cycle. Different workers perceive the complexity of a certain task differently and each worker will have his/her learning curve with its individual characteristics. So, it is more realistic to assume that the learning curve characteristics are random variables with given probability density functions. Furthermore, for the second model, the demand of the product follows three-regime product life cycle. Each regime is modeled by a geometric Brownian motion. The net present value (NPV) is calculated using the real options. The results show that there is a significant change in the NPV compared to standard model with simplified assumption

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Section: Serial Production System Release Policymentioning

confidence: 99%

“…But the jobs do not have to wait long. It will give maximum throughput without excessive flow time or WIP (Helber et al 2011).…”

Section: Serial Production System Release Policymentioning

confidence: 99%

Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

Muttulingam¹

2021

Preprint

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“…The subsystems were modeled by a continuous-time Markov chain and solved as described in [13]. Helber, Schimmelpfeng, and Stolletz [26] analyzed closed queueing systems using a linear program. Lagershausen, Manitz, and Tempelmeier [29] proposed a method to evaluate closed assembly/disassembly systems by applying a decomposition approach.…”

Section: Related Literaturementioning

confidence: 99%

Markov-chain model and algorithmic procedure for the performance analysis of closed cyclic queues

Lagershausen

2015

Performance Evaluation

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“…They try to decompose closed manufacturing systems into their building blocks for easy analyzing. They report the suitability of their solution for Conwip and closed loop systems using pallets as Helbert et al did in [29]. Ip et al in [30] treat Conwip system as a closed loop and evaluate the difference between single and multi-loop Conwip system.…”

Section: Closed Loop System Reviewmentioning

confidence: 99%

Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

Mehrsai

Karimi

Scholz‐Reiter

2012

Int J Adv Manuf Technol

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Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks' structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central monitoring and controlling, show some limitations for organizing every operation on time or in the right time. Therefore, in the recent decade, a great attention is put on decentralized control and, to some extent, autonomy. This paper tries to investigate the possibility of combining this new research paradigm with existing strategies in production logistics, in order to improve material handling and control task according to material flow criteria. To show this, an exemplary plant after decoupling point out of a logistic network is considered for simulation and analysis. This combines Conwip system with learning autonomous pallets' concept in a discrete event simulation model. Several decentralized control scenarios are experimented and compared together. Here, the learn methodology is brought to pallets based on fuzzy rules and advantage of closed loop systems.

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Setting Inventory Levels of CONWIP Flow Lines via Linear Programming

Cited by 13 publications

References 12 publications

Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

Markov-chain model and algorithmic procedure for the performance analysis of closed cyclic queues

Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

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