Abstract
ABSTRACT The Petrol Station Replenishment Problem (PSRP) is a complex logistical challenge that involves several factors, including multi-compartment vehicles of different types, multiple returns to the depot(s), and evaporation losses. This research proposes a Deteriorating Inventory-Routing Problem (DIRP) approach to solve the PSRP, optimize routing costs, minimize evaporation losses, and optimize inventory levels. The study uses a Mixed Integer Non-Linear Programming (MINLP) model that takes into account vehicle scheduling, allowing petrol stations to be replenished multiple times during the depot’s working hours depending on their consumption rate. The inventory amount at the stations is monitored throughout the period, not just at the beginning and end. The proposed Genetic Algorithm and Tabu Search (GATS) and Genetic Algorithm and Greedy sub-algorithms (GAG) are tested on small and large instances of DIRP. The results show that the GATS algorithm produces optimal solutions for most instances compared to the best values obtained, while the GAG algorithm provides satisfactory results for small problems with a reasonable computation time and reduces computation time while improving the objective function value for large problems. For large datasets, GATS surpasses GAG by 4.45% in the best-known solution and, on average, is 8.81% lower in the mean-known solution.
Published Version
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