1,403 research outputs found
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
Several metaheuristics use decomposition and pruning strategies to solve
large-scale instances of the vehicle routing problem (VRP). Those complexity
reduction techniques often rely on simple, problem-specific rules. However, the
growth in available data and advances in computer hardware enable data-based
approaches that use machine learning (ML) to improve scalability of solution
algorithms. We propose a decompose-route-improve (DRI) framework that groups
customers using clustering. Its similarity metric incorporates customers'
spatial, temporal, and demand data and is formulated to reflect the problem's
objective function and constraints. The resulting sub-routing problems can
independently be solved using any suitable algorithm. We apply pruned local
search (LS) between solved subproblems to improve the overall solution. Pruning
is based on customers' similarity information obtained in the decomposition
phase. In a computational study, we parameterize and compare existing
clustering algorithms and benchmark the DRI against the Hybrid Genetic Search
(HGS) of Vidal et al. (2013). Results show that our data-based approach
outperforms classic cluster-first, route-second approaches solely based on
customers' spatial information. The newly introduced similarity metric forms
separate sub-VRPs and improves the selection of LS moves in the improvement
phase. Thus, the DRI scales existing metaheuristics to achieve high-quality
solutions faster for large-scale VRPs by efficiently reducing complexity.
Further, the DRI can be easily adapted to various solution methods and VRP
characteristics, such as distribution of customer locations and demands, depot
location, and different time window scenarios, making it a generalizable
approach to solving routing problems
Parallel ACO with a Ring Neighborhood for Dynamic TSP
The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc
Clustered coverage orienteering problem of unmanned surface vehicles for water sampling
202105 bchyNot applicableOthersNSFC projectsPublished12 month
Clustered coverage orienteering problem of unmanned surface vehicles for water sampling
This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two-stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two-stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.</p
On the Energy Efficiency and Performance of Irregular Application Executions on Multicore, NUMA and Manycore Platforms
International audienceUntil the last decade, performance of HPC architectures has been almost exclusively quantifiedby their processing power. However, energy efficiency is being recently considered as importantas raw performance and has become a critical aspect to the development of scalablesystems. These strict energy constraints guided the development of a new class of so-calledlight-weight manycore processors. This study evaluates the computing and energy performanceof two well-known irregular NP-hard problems — the Traveling-Salesman Problem (TSP) andK-Means clustering—and a numerical seismic wave propagation simulation kernel—Ondes3D—on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task ofadapting these applications to a manycore, specifically the novel MPPA-256 manycore processor.Then, we analyze their performance and energy consumption on those di↵erent machines.Our results show that applications able to fully use the resources of a manycore can have betterperformance and may consume from 3.8x to 13x less energy when compared to low-power andgeneral-purpose multicore processors, respectivel
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