A real time adaptive dynamic programming approach for planning and scheduling

Pratikakis, Nikolaos E.; Lee, Jay H.; Park, Jongwoo

doi:10.1016/s1570-7946(06)80206-3

Cited by 3 publications

(1 citation statement)

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“…Since then, different simulation-based capacity planning methods have been investigated to improve solutions consistency (Seidel et al 2019). (Pratikakis et al 2006) developed a real-time adaptive dynamic programming method for planning and scheduling. (Pratikakis et al 2010) studied Approximate Dynamic Programming (ADP) method to address the curse-of-dimensionality associated with multistage capacity decision problems.…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

2022

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With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate Dynamic Programming (k-ADP) and the Rolling-Horizonbased Approximate Dynamic Programming (R-ADP) are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display (TFT-LCD) industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic Dynamic Programming (SDP).

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Section: Introductionmentioning

confidence: 99%