An efficient heuristic for the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads

In this study, we consider the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple vehicles as well as multiple stops by the same vehicle. In practice, split deliveries is a viable option in many settings where the load can be physically split, such as courier services of third party logistics operators. We propose an efficient heuristic that combines the strengths of Tabu Search and Simulated Annealing for the solution of the MPDPSL. Results from experiments on two problem sets in the literature indicate that the heuristic is capable of producing good quality solutions in reasonable time. The experiments also demonstrate that up to 33% savings can be obtained by allowing split loads; however, the magnitude of savings is dependent largely on the spatial distribution of the pickup and delivery locations.

► Introduces a multi-vehicle version of Pickup and Delivery Problem with Split Loads.
► Proposes a Tabu Embedded Simulated Annealing algorithm to solve MPDPSL.
► Utilizes innovative data and neighborhood structures.
► Proposes new test instances and first benchmark results on MPDPSL.
► Analyzes benefit of load splitting for various problem characteristics.