A Logistics Model for Warehouses and Route Optimization: For Perishable Goods

In this article, we study Subscription-based distribution of perishable goods, such as daily milk, newspapers, and magazines, requires tightly coordinated decisions on facility location, fleet composition, and vehicle routing under strict capacity and delivery-time constraints. These interdependent decisions give rise to large-scale NP-hard optimization problems that are poorly served by traditional sequential planning approaches. This study proposes an integrated optimization framework that jointly addresses customer clustering, warehouse assignment, routing, and fleet mix selection within a unified decision architecture. The proposed methodology combines constraint-aware clustering with a hybrid Ant Colony Optimization (ACO) metaheuristic. A nested solution structure is introduced in which stochastic ants construct feasible routing and assignment solutions, while the fleet mix sub-problem is solved exactly through an embedded knapsack algorithm, enabling computational efficiency without sacrificing optimality at the tactical level. The framework is evaluated through comparative experiments against a mixed-integer linear programming benchmark, demonstrating consistent improvements in total logistics cost and solution scalability for recurring perishable distribution scenarios. The results highlight the value of hybrid metaheuristic–exact optimization architectures for integrated supply chain design and operational planning.