A parallel pull labelling algorithm for the resource constrained shortest path problem

TL;DR

This paper introduces a highly parallel pull labelling algorithm for the resource constrained shortest path problem, achieving significant speed-ups and enabling larger-scale applications in various optimization fields.

Contribution

The paper presents a novel parallel pull labelling algorithm with bi-directional search and vectorised dominance, improving computational efficiency for RCSPP.

Findings

14x speed-up over baseline on hard instances

up to 200x speed-up on the hardest instances

Potential to solve larger-scale routing and scheduling problems

Abstract

The Resource Constrained Shortest Path Problem (RCSPP) is a fundamental combinatorial optimisation problem in which the goal is to find a least-cost path in a directed graph subject to one or more resource constraints. In this paper we present a pull labelling algorithm for the RCSPP that introduces i) a highly parallelisable approach at a label bucket level, ii) an extension to bi-directional search with a dynamic midpoint, and iii) a vectorised dominance criterion that uses vector instructions to speed-up the label comparison with another level of parallelisation. Compared to a baseline version of the algorithm the optimisations result in a speed-up of around 14x on a set of hard instances and up to 200x on some of the hardest instances. The proposed algorithm demonstrates significant computational improvements that may enhance the efficiency of column generation frameworks incorporating resource constrained shortest path sub-problems, potentially enabling the efficient solution of larger-scale instances in routing, scheduling, supply chain and transportation network optimisation applications.