Production control problem for multi-product multi-resource make-to-stock systems

Abstract

Most of today's production systems are working with parallel production resources to increase throughput rate due to the increase in high variability in demand and product mix. Effective control and performance evaluation of such systems is of paramount importance to minimize production and inventory-related costs. We examine a production-inventory system featuring parallel production resources capable of producing various products. In many industries such as automotive, white goods, electronics, and paint, multiple/parallel production resources are widely used to produce the ideal amount and satisfy incoming demands for distinct products. In this study, shortage cost is not restricted to only one type and both lost sales and backordering cases are analyzed. In order to analyze the optimal production policies' behavior, we initially formulate dynamic programming models for both lost sales and backordering systems, treating them as Markov Decision Processes. Subsequently, we solve these models using the value iteration algorithm. Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. These policies extend the existing ones described for multi-item single-resource make-to-stock (MTS) systems to accommodate multiple resources. We construct simulation models to assess the efficacy of the heuristic policies, conducting comparisons of their performance against both the optimal policy and among one another. To the best of our knowledge, there has been no exploration of scenarios involving multiple production resources concurrently manufacturing distinct products in a MTS environment. Hence, this study serves as an extension to the examination of multi-item, multi-production resource MTS systems.