Integrated Production and Transportation Financial Supply Chain Scheduling via a Q‐Learning‐Based Heuristic Method

ABSTRACT This work addresses a critical need: to integrate the time value of money into production and transportation scheduling within a financial supply chain context. We propose a novel Integrated Production and Transportation Financial Supply Chain Scheduling (IPT‐FSCS) model that optimizes scheduling decisions based on the present value of revenues. The model discounts future revenues to the current decision point because the manufacturer needs to make production schedule decisions based on their current financial impact. Consequently, the present value is the relevant metric for this evaluation. A set of jobs (i.e., customer orders) are first processed at a manufacturing plant and then delivered directly to customers via dedicated vehicles. The objective is to generate a processing schedule that maximizes the total present value of revenues while considering customer service levels, measured by maximum delivery time. A time‐revenue trade‐off factor is introduced to balance these two objectives. Our theoretical analysis first establishes optimal policies for several special cases solvable in polynomial time. Building on these structural insights, we then develop two approximation algorithms with provable performance guarantees. For the general problem, we formulate a mixed‐integer programming model and propose a high‐performance Q‐learning‐based genetic algorithm (Q‐GA‐Q) for efficient, near‐optimal solutions. Numerical experiments demonstrate the algorithm's effectiveness and highlight the significant impact of the present value factor on manufacturers' earnings. Notably, the results show that incorporating the present value concept can lead to substantial improvements in profitability compared to traditional scheduling approaches. This research offers valuable managerial insights and provides a practical framework for optimizing production and transportation scheduling in a financially integrated supply chain.