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

Medrano, F. Antonio; Church, Richard L.

doi:10.1002/mcda.1541

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…Objective (9) reflects that (1 − w )o 2 is subtracted from wo 1 since objective (4) has a minimization orientation. A potential issue to consider for integer programming models is that the weighting method may not be able to identify all Pareto tradeoffs [see Medrano and Church 2015]. Accordingly, the constraint method represents a potential alternative for finding the Pareto tradeoffs [ReVelle 1993;Wei and Murray 2018].…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, multiple objective modeling very much reflects such a planning and decision-making approach. ReVelle [1993], Medrano and Church [2015], and Wei and Murray [2018] highlight both the challenges and utility of multiple objective considerations. [ Setti et al 2020], individuals should maintain at least 9 feet of distance between others and remain in their assigned office with doors closed, only going to the restroom or lounge when necessary.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 and Minimizing Micro-Spatial Interactions

Burtner

Murray

2022

ACM Trans. Spatial Algorithms Syst.

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COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 and Minimizing Micro-Spatial Interactions

Burtner

Murray

2022

ACM Trans. Spatial Algorithms Syst.

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“…This analysis implemented the parallel bi-objective shortest path algorithm described in Medrano and Church [ 49 ] to compute the complete set of supported (convex) non-dominated path solutions using an origin at the lower-left corner of the raster region, and a destination at the top-right corner. This algorithm, called pNISE is a parallel implementation of the NISE algorithm commonly used to find the supported solutions of biobjective network optimization problems [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Effects of raster terrain representation on GIS shortest path analysis

Medrano

2021

PLoS ONE

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Spatial analysis extracts meaning and insights from spatially referenced data, where the results are highly dependent on the quality of the data used and the manipulations on the data when preparing it for analysis. Users should understand the impacts that data representations may have on their results in order to prevent distortions in their outcomes. We study the consequences of two common data preparations when locating a linear feature performing shortest path analysis on raster terrain data: 1) the connectivity of the network generated by connecting raster cells to their neighbors, and 2) the range of the attribute scale for assigning costs. Such analysis is commonly used to locate transmission lines, where the results could have major implications on project cost and its environmental impact. Experiments in solving biobjective shortest paths show that results are highly dependent on the parameters of the data representations, with exceedingly variable results based on the choices made in reclassifying attributes and generating networks from the raster. Based on these outcomes, we outline recommendations for ensuring geographic information system (GIS) data representations maintain analysis results that are accurate and unbiased.

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“…Their empirical evaluation demonstrates significant speedups, building upon the seemingly impractical work by Sanders and Mandow (2013). Medrano and Church (2015) propose another parallel approach for computing the set of extreme solutions in the biobjective setting, showing applicability using personal machines and shared memory supercomputers. Ahmadi et al (2021) suggest a bi-objective bi-directional search algorithm where one search runs from the source and another from the target.…”

Section: Related Workmentioning

confidence: 99%

Efficient Set Dominance Checks in Multi-Objective Shortest-Path Algorithms via Vectorized Operations

Hernández Ulloa,

Zhang,

Koenig

et al. 2024

SOCS

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In the multi-objective shortest-path problem (MOSP) we are interested in finding paths between two vertices of a graph while considering multiple objectives. A key procedure, which dominates the running time of many state-of-the-art (SOTA) algorithms for MOSP is set dominance checks (SDC). In SDC, we are given a set X of N-dimensional tuples and a new N-dimensional tuple p and we need to determine whether there exists a tuple q in X such that q dominates p (i.e., if every element in q is lower or equal than the corresponding element in p). In this work, we offer a simple-yet-effective approach to perform SDC in a parallel manner, an approach that can be seamlessly integrated with most SOTA MOSP algorithms. Specifically, by storing states in memory dimension-wise and not state-wise, we can exploit vectorized operations offered by ``Single Instruction/Multiple Data'' (SIMD) instructions to efficiently perform SDC on ubiquitous consumer CPUs. Integrating our approach for SDC allows to dramatically improve the runtime of existing MOSP algorithms.

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

Cited by 8 publications

References 36 publications

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19 and Minimizing Micro-Spatial Interactions

Effects of raster terrain representation on GIS shortest path analysis

Efficient Set Dominance Checks in Multi-Objective Shortest-Path Algorithms via Vectorized Operations

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