Embedding Assignment Routing Constraints through Multi Dimensional Network Construction for Solving the Multi Vehicle Routing Problem with Pickup and Delivery with Time Windows

TL;DR

This paper presents a novel multi-dimensional network approach for solving the complex multi-vehicle routing problem with pickup, delivery, and time windows, demonstrating improved efficiency and optimality on real-world datasets.

Contribution

It introduces a multi vehicle state space time network and a hyper path examination method to effectively handle assignment constraints and symmetry issues in routing problems.

Findings

Demonstrates computational efficiency on benchmark and real-world datasets.

Achieves near-optimal solutions for complex routing scenarios.

Effectively manages symmetry issues in combinatorial optimization.

Abstract

The multi Vehicle Routing Problem with Pickup and Delivery with Time Windows is a challenging version of the Vehicle Routing Problem. In this paper, by embedding many complex assignment routing constraints through constructing a multi dimensional network, we intend to reach optimality for local clusters derived from a reasonably large set of passengers on real world transportation networks. More specifically, we introduce a multi vehicle state space time network representation in which only the non dominated assignment based hyper paths are examined. In addition, by the aid of passengers cumulative service patterns defined in this paper, our solution approach is able to take control of symmetry issue, a common issue in the combinatorial problems. At the end, extensive computational results over the instances proposed by Ropke and Cordeau 2009 and a randomly generated data sets from the Phoenix subarea, City of Tempe, show the computational efficiency and solution optimality of our developed algorithm.