Adaptive pull-type production control using Kalman Filters

Quintana, Rolando; Lambert, Bart; Roderick, Larry M.

doi:10.1080/002075497194381

Cited by 5 publications

(3 citation statements)

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“…Takahashi [15] also presented an inventory based reactive Kanban system. Quintana et al [16] developed an adaptive pull-type process control system that can be applied for variable yield rates of machines. To accomplish a target yield rate at a final machine, the number of parts that should be included in a production container is adjusted using the Kalman filtering method.…”

Section: Literature Surveymentioning

confidence: 99%

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

2007

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Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models.

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Section: Literature Surveymentioning

confidence: 99%

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

2007

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“…Takahashi [9] also presented an inventory based reactive Kanban system. Quintana et al [10] developed an adaptive pull-type process control system that can be applied for variable yield rates of machines. To accomplish a target yield rate at a final machine, the number of parts that should be included in a production container is adjusted using the Kalman filtering method.…”

Section: Literature Surveymentioning

confidence: 99%

An adaptive inventory control for a supply chain

Zhang

Liu

2009

2009 Chinese Control and Decision Conference

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Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, two adaptive inventory-control models, a centralized model and a decentralized one, are proposed for a supply chain consisting of one supplier and one retailers. The objective of the two models is to satisfy a target service level predefined for each retailer and to minimize the whole inventory cost. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-reward method, the control parameters are designed to adaptively change as customer demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory control models.

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“…For a mathematical derivation of the Kalman Filter and the AJITPCS, refer to Quintana and Lambert (1997). The AJITPCS algorithm with the Kalman Filter is presented without derivation as follows:…”

Section: Adaptive Closed-loop Algorithmmentioning

confidence: 99%

A production methodology for agile manufacturing in a high turnover environment

Quintana

1998

Int Jrnl of Op & Prod Mnagemnt

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Adaptive pull-type production control using Kalman Filters

Cited by 5 publications

References 0 publications

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

An adaptive inventory control for a supply chain

A production methodology for agile manufacturing in a high turnover environment

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