Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows

TL;DR

This paper introduces a novel temporal decomposition approach using rolling horizon optimization for solving complex offline pickup and delivery problems with narrow time windows, achieving better scalability and solution quality than existing heuristics.

Contribution

It presents the first application of rolling horizon based temporal decomposition to offline PDPTWs, improving solution speed and quality for large-scale instances.

Findings

Framework outperforms baseline in large instances

Provides high-quality solutions with faster computation times

Scalable approach suitable for real-world paratransit optimization

Abstract

The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. We compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances, the baseline approach is as competitive as our framework. However, in larger problem instances, our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.