Optimal control for production inventory system with various cost criterion

TL;DR

This paper develops an optimal control framework for a production-inventory system with stochastic demand and production times, deriving policies that minimize various cost criteria using Markov decision processes.

Contribution

It introduces a Markov decision process model for production-inventory control with explicit steady-state solutions and optimal policies derived via value and policy iteration algorithms.

Findings

Explicit steady-state probability distribution obtained.

Optimal policies for different cost criteria derived.

Numerical examples verify the effectiveness of algorithms.

Abstract

In this article, we investigate a dynamic control problem of a production-inventory system. Here, demands arrive at the production unit according to a Poisson process and are processed in an FCFS manner. The processing time of the customers' demand is the exponential distribution. The production manufacturers produce the items on a make-to-order basis to meet customer demands. The production is run until the inventory level becomes sufficiently large. We assume that an item's production time follows exponential distribution and the amount of time for the produced item to reach the retail shop is negligible. Also, we assume that no new customer joins the queue when there is a void inventory. This yields an explicit product-form solution for the steady-state probability vector of the system. The optimal policy that minimizes the discounted/average/pathwise average total cost per production is derived using a Markov decision process approach. We find optimal policy using value/policy iteration algorithms. Numerical examples are discussed to verify the proposed algorithms.