Authors - Christian Vasta, Rolf Dornberger, Thomas Hanne Abstract - The Inventory Routing Problem (IRP) is a critical challenge in logistics, combining vehicle routing with inventory management under a unified objective. Recent research in computational intelligence has advanced the use of metaheuristics for tackling such combinatorial problems. Among these, Simulated Annealing (SA) remains underexplored for IRP compared to more commonly applied methods. In this study, we address this gap by implementing a custom SA algorithm to solve a deterministic five-day IRP. The goal is to minimize total transportation costs while satisfying daily customer demand using a single-vehicle fleet with fixed capacity. The algorithm's performance is evaluated with 20 independent runs and compared to a modified Tabu Search benchmark using the same deterministic instance. Our results show that Simulated Annealing performs competitively, producing high-quality solutions, with moderate variation observed across different cooling schedules and repeated runs. Although it shows greater sensitivity to initial parameters and stochastic behavior, its exploratory nature allows it to overcome local optima more effectively than Tabu Search in some cases. The outcomes suggest that SA is a viable alternative for IRP under deterministic conditions, particularly when flexibility in parameter tuning is prioritized.
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