Technology Category
Constraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
A parallel pull labelling algorithm for the resource constrained shortest path problem
🤖 AI Summary
This paper addresses the Resource-Constrained Shortest Path Problem (RCSPP)—finding a minimum-cost path in a directed graph subject to multiple resource constraints. To overcome its computational bottlenecks, we propose an efficient parallel pull-based labeling algorithm featuring: (i) fine-grained parallelism at the label-bucket level; (ii) a dynamic midpoint bidirectional search strategy that adaptively balances forward and backward label extensions; and (iii) the first application of SIMD vectorization to dominance checking, significantly accelerating label comparisons. Experimental evaluation on standard benchmark instances demonstrates an average speedup of 14× over state-of-the-art sequential solvers, with peak improvements reaching 200× on the most challenging instances. The proposed method substantially enhances the efficiency of RCSPP subproblems within large-scale optimization frameworks—particularly column generation—enabling faster convergence and scalability for complex combinatorial optimization tasks.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Solving resource-constrained shortest path problems in directed graphs
Developing parallel pull labelling algorithms for computational speedup
Enhancing efficiency in routing and scheduling optimization applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Parallel pull labelling algorithm for RCSPP
Bi-directional search with dynamic midpoint
Vectorised dominance criterion for parallelisation
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2024-06-16arXiv.orgCitations: 0
