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

Chen-Yang Cheng (), Pourya Pourhejazy () and Tzu-Li Chen ()
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Chen-Yang Cheng: National Taipei University of Technology
Pourya Pourhejazy: UiT- The Arctic University of Norway
Tzu-Li Chen: National Taipei University of Technology

Flexible Services and Manufacturing Journal, 2023, vol. 35, issue 3, No 7, 797-837

Abstract: Abstract 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 and the Rolling-Horizon-based Approximate Dynamic Programming 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 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.

Keywords: Production management; Capacity planning; Approximate dynamic programming; Stochastic dynamic programming; Optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10696-022-09458-7

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