Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2023

Abstract

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.

Keywords

Simultaneous pickup and delivery, Occasional driver, Simulated Annealing, Vehicle routing problem

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Expert Systems with Applications

Volume

214

First Page

1

Last Page

16

ISSN

0957-4174

Identifier

10.1016/j.eswa.2022.119118

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.eswa.2022.119118

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