An Analysis on Inventory Routing Problem with Service Level Constraints

Abstract

This study addresses a single-depot stochastic inventory-routing problem with static inventory control policies and vehicle routing decisions. The model integrates precomputed safety stock values into a mixed-integer linear programming formulation, accounting for customer-specific demand variability and service level requirements. Replenishment timing, delivery quantities, and coverage intervals are jointly optimized over a finite planning horizon. The computational findings indicate that the model effectively manages inventory and transportation expenses in diverse operational contexts. When vehicle capacity limits are tight or demand grows more unpredictable, total system costs generally increase. Similarly, imposing fixed delivery schedules might reduce inventory, but can cause more intricate and expensive routing. Scenario-based analyses with fluctuating demand patterns show that aligning replenishment timing with demand trends can result in more efficient results. In summary, the model exhibits robust performance in uncertain settings and provides insightful guidance for logistics planning, particularly where service-level requisites are crucial.