EURO 2024 Copenhagen
Abstract Submission

EURO-Online login

1603. Solving General Assemble-to-Order systems via Component-Based and Product-Based Decomposition Methods

Invited abstract in session MD-39: Stochastic Models in Manufacturing, stream Stochastic Modelling.

Monday, 14:30-16:00
Room: 35 (building: 306)

Authors (first author is the speaker)

1. Mohsen Elhafsi
School of Business Administration, University of California
2. Jianxin Fang
Department of Intelligent Operations and Marketing, Xi'an Jiaotong-Liverpool University
3. essia Hamouda
IDS, CSUSB

Abstract

Assemble-to-order (ATO) strategies are widely used in various industries. Despite their popularity, ATO systems remain challenging, both analytically and computationally. We study a general ATO problem modeled as an infinite horizon Markov decision process. In particular, we consider a system with mixed-Erlang distributed component production/leadtimes, and Poisson demand for products. Demand is lost if not immediately satisfied. As the optimal policy of such system is computationally intractable, we develop two heuristic policies based on decomposition methods: component-based and product-based. In order to evaluate the performance of the heuristics, we develop a tight lower bound using an Approximate Linear Programming approach that relies on a judicial choice of basis-functions, for approximating the optimal value function. Our results show that the heuristics perform within only few Average Percentage Deviation (ADP) from the lower bound and even a smaller ADP when compared to systems where the optimal policy could be obtained. Moreover, we show that our component-based decomposition heuristic only scales linearly with the number of components in the ATO system, and therefore is suitable for solving large-scale ATO systems.

Keywords

Status: accepted

Back to the list of papers