The vehicle routing problem with simultaneous pickup and delivery and occasional drivers

• Main Authors: YU, Vincent F., ALOINA, Grace, JODIAWAN, Panca, GUNAWAN, Aldy, HUANG, Tsung-C.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Simultaneous pickup and delivery; Occasional driver; Simulated Annealing; Vehicle routing problem; Artificial Intelligence and Robotics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7554; https://ink.library.smu.edu.sg/context/sis_research/article/8557/viewcontent/1_s2.0_S0957417422021364_main.pdf
• Institution: Singapore Management University

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total compensation paid to employed occasional drivers. We cast the problem into a mixed integer linear programming model and propose a simulated annealing (SA) heuristic with a mathematical programming-based construction heuristic to solve newly generated VRPSPDOD benchmark instances. The proposed SA incorporates a set of neighborhood operators specifically designed to address the existence of regular vehicles and occasional drivers. Extensive computational experiments show that the proposed SA obtains comparable results with the state-of-the-art algorithms for solving VRPSPD benchmark instances – i.e., the special case of VRPSPDOD – and outperforms the off-the-shelf exact solver – i.e., CPLEX – in terms of solution quality and computational time for solving VRPSPDOD benchmark instances. Lastly, sensitivity analyses are presented to understand the impact of various OD parameters on the objective value of VRPSPDOD and to derive insightful managerial insights.