An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

Published Online:

The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.

This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.

The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.

The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.

This article is part of the Virtual Special Issue: A Deeper Look at Transportation Science by Topical Areas.

This article also appears in INFORMS Analytics Collections Vol. 15: 25 Years of INFORMS.

Visit this collection for free access to more articles showcasing the evolution of INFORMS over the past 25 years.

INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.