Open Access

ARTICLE

Stochastic Programming For Order Allocation And Production Planning

Phan Nguyen Ky Phuc^*

International University-Vietnam National University, Vietnam National University, HoChiMinh City, 70000, Vietnam

* Corresponding Author: Phan Nguyen Ky Phuc. Email:

(This article belongs to this Special Issue: Impact of Industry 4.0 on Supply Chain Management and Optimization)

Computer Systems Science and Engineering 2022, 40(1), 75-85. https://doi.org/10.32604/csse.2022.017793

Received 11 February 2021; Accepted 16 April 2021; Issue published 26 August 2021

Abstract

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.

---

Keywords

---