Multi-granularity hybrid parallel network simplex algorithm for minimum-cost flow problems

Jiang, Jincheng; Chen, Jinsong; Wang, Chisheng

doi:10.1007/s11227-020-03227-9

Cited by 2 publications

(3 citation statements)

References 47 publications

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“…Jiang et al propose a multi-granular approach for parallel network simplex algorithm. 31 Fine and coarse grained parallel strategies are to be used for shared and distributed memory parallelism, respectively. A maximum speedup of 18.7 is reported in the article with a mix of both strategies.…”

Section: Related Workmentioning

confidence: 99%

Parallel network simplex algorithm for the minimum cost flow problem

Kara

Özturan

2021

Concurrency and Computation

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In this work, we contribute a parallel implementation of the network simplex algorithm that is used for the solution of minimum cost flow problem. In the network simplex algorithm, finding an entering arc requires searching through many arcs to decide which one should be included in the spanning tree solution on the next iteration. We propose finding the entering arc in parallel as it often takes the majority of the execution time. A usual strategy is to pick the arc violating the optimality the most out of all possible candidates. Scanning all arcs can take quite some time, so it is common to consider only a fixed number of arcs which is referred as the block search pivoting rule. Arc scans can easily be done in parallel to find the best candidate as the calculations are independent of each other. We used shared memory parallelism using OpenMP along with vectorization using AVX instructions. We also tried adjusting block sizes to increase the parallel portion of the algorithm.Our dataset consists of various natural and synthetic graphs with sizes up to a billion arc. Our experiments show speedups up to four are possible, though they are typically lower.

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Section: Related Workmentioning

confidence: 99%

Parallel network simplex algorithm for the minimum cost flow problem

Kara

Özturan

2021

Concurrency and Computation

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“…Emergency logistics is referred to the logistics service that responds to disasters (e.g., H1N1 influenza, SARS virus, Ebola virus, earthquake, and terrorist attack) [1]. To optimize the distribution of the supplies within the required time-frame in the emergency logistics, various technologies are used, for example, system dynamics [2], heuristic algorithm [3][4][5][6][7], and Internet of ings technology [8]. In the existing works, the optimization of emergency logistics is mainly focused on time [9][10][11] and cost [6,[12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…To optimize the distribution of the supplies within the required time-frame in the emergency logistics, various technologies are used, for example, system dynamics [2], heuristic algorithm [3][4][5][6][7], and Internet of ings technology [8]. In the existing works, the optimization of emergency logistics is mainly focused on time [9][10][11] and cost [6,[12][13][14][15]. However, the distribution of supplies for COVID-19 is different from the traditional emergency logistics and has unique features [16].…”

Section: Introductionmentioning

confidence: 99%

Distribution of the Emergency Supplies in the COVID-19 Pandemic: A Cloud Computing Based Approach

Wang

2021

Mathematical Problems in Engineering

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The containment of the COVID-19 pandemic was significantly affected by the unbalanced distribution of emergency supplies, low coordinated transport efficiency, high costs, and the inability of nonprofit organizations to handle the emergency supplies efficiently. Based on the COVID-19 experience, in this paper, we build a cloud platform for emergency supplies distribution to reduce the asymmetry of emergency logistics information, reduce the costs, and improve the efficiency of emergency supplies distribution. Our proposed method uses a genetic algorithm with the monarch scheme to optimize the urban emergency supplies distribution. The numerical results and sensitivity analysis for a sample network indicate that using the proposed platform the integrated cost in different cities are reduced by 29.01%, 28.67%, and 22.73%, the required time in different cities are reduced by 22.98%, 26.59%, and 36.65%. The results suggest that the proposed method reduces the integrated cost and transportation time and finds the optimal distribution path.

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Multi-granularity hybrid parallel network simplex algorithm for minimum-cost flow problems

Cited by 2 publications

References 47 publications

Parallel network simplex algorithm for the minimum cost flow problem

Parallel network simplex algorithm for the minimum cost flow problem

Distribution of the Emergency Supplies in the COVID-19 Pandemic: A Cloud Computing Based Approach

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