Reinforcement Learning-Guided Integer Optimization for Production-Inventory-Distribution Planning in Manufacture-Circulation Industrial System

Abstract

In the manufacture-circulation industrial system (MCIS), the integration of production, inventory, and distribution planning is critical for operational efficiency and cost minimization. This paper addresses the challenges of coordinating production and distribution decisions in a MCIS with multiple distribution centers and order-specific time windows. We propose a mixed-integer linear programming (MILP) model that jointly optimizes production lot-sizing, inventory levels, and distribution decisions while considering machine capacity, storage constraints, and delivery time windows. To solve the computationally challenging problem, we develop a matheuristic framework combining a relax-and-fix (RF) approach for generating feasible solutions and a reinforcement learning-guided fix-and-optimize (FO) algorithm for iterative solution improvement. Computational experiments on realworld and synthetic instances compare the proposed method with CPLEX and a conventional rolling-horizon style RFO baseline. This work contributes to the literature by addressing the integration of multi-stage production and distribution systems with customer order time windows, providing scalable solutions for complex manufacturing problems.