A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem

Medrano, Fernando Antonio; Church, Richard Lee

doi:10.1002/mcda.1541

A Parallel Computing Framework for Finding the Supported Solutions to a Biobjective Network Optimization Problem

Journal Article · Tue Apr 07 00:00:00 EDT 2015 · Journal of Multi-Criteria Decision Analysis

DOI:https://doi.org/10.1002/mcda.1541· OSTI ID:1401385

Medrano, Fernando Antonio ^[1]; Church, Richard Lee ^[1]

Department of Geography University of California Santa Barbara California USA

Abstract

Solving multi‐objective combinatorial optimization problems can quickly become computationally challenging when applied to large networks generated from big data. Present‐day first‐rate Mixed Integer Programming (MIP) solvers have parallelism built‐in to take advantage of multicore architectures; but specialized network optimization algorithms that can often solve graph problems more efficiently than a general MIP solver are typically programmed serially. Thus, these network algorithm implementations do not take full advantage of modern multicore computer capabilities. Many parallel computing languages include a fork/join framework into their concurrency packages that allow for simple conversion of recursive serial algorithms to operate in parallel without having to use complicated low‐level message‐passing interfaces. Fork/join is particularly well suited to divide and conquer algorithms such as Non‐Inferior Set Estimation (NISE) method for computing the supported Pareto front of a multi‐objective optimization problem. This work develops a simple general parallel NISE (pNISE) implementation using the Java 7 concurrency tools, and then offers results from a specific implementation of solving a bi‐objective shortest path problem. The results indicate that this method is capable of sizeable speed‐ups on both small‐scale personal computers as well as large‐scale shared memory supercomputers. Finally, other network problems suitable to this method are discussed, including multi‐objective variants of the minimum spanning tree problem, transportation problem, assignment problem, maximum flow problem, and the minimum‐cost flow problem. Copyright © 2015 John Wiley & Sons, Ltd.

View Accepted Manuscript (Publisher)

Sponsoring Organization:: USDOE

OSTI ID:: 1401385

Journal Information:: Journal of Multi-Criteria Decision Analysis, Journal Name: Journal of Multi-Criteria Decision Analysis Journal Issue: 5-6 Vol. 22; ISSN 1057-9214

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

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