Multi-Depot Pickup and Delivery Problem Optimization with Constraint Logic Programming

The Multi-Depot Pickup and Delivery Problem is a combinatorial optimization problem with a variety of applications in the logistics and transport sector, which consists of the delivery of goods from multiple origins to multiple destinations using a fleet of vehicles that are located in multiple depots. This work formulates an efficient optimization approach for the Multi-Depot Pickup and Delivery Problem through Constraint Logic Programming, a declarative paradigm most commonly used for dealing with combinatorial constraints and search spaces. To reduce travel cost and enhance operational efficiency, the proposed schedule is a combination of sophisticated constraint propagation approach and functional heuristics. One of the major novelties of this work resides in the adaptive depot assignment approach together with dynamic optimization on constraints that reduce the convergence rate for large-size problems. They show in experimental results on benchmark instances that the approach outperforms the existing ones in terms of solution quality and computational time. By formulating scheduling over these constraints, Constraint Logic Programming also seamlessly enables integration of real-world constraints directly into scheduling. These discoveries highlight the applicability of Constraint Logic Programming as a successful method for converging factorial routing challenges involving logistics and transporting systems.