Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

Abstract

This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.

Keywords:

• manufacturing
• order acceptance
• multiple demand classes
• dynamic programming
• stochastic approximation

Acknowledgements

The work described in this article was partially supported by the General Research Fund (GRF) of the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC # CityU 113609 and RGC # HKU 718809).