42,099 research outputs found
Redesign of Three-Echelon Multi-Commodity Distribution Network
This research studies the distribution network redesign of an actual electronics company. The problems are formulated based on multi-echelon capacitated Location Routing Problem (LRP) with two commodities: home products and service items. The objective function consists of three components: facility cost, closing cost of facility and transportation cost. We propose solution method based on clustering technique. The problem is decomposed into the Facility Location Allocation Problem (FLAP) and the Multi-Depot Vehicle Routing Problem (MDVRP). MDVRP is solved by clustering method and feed the results to the modified FLAP to allocate the demand nodes to facilities and configure all distribution networks, for the 2nd and 3rd echelon. The distribution is divided into five region zones. Previously, each region was operated independently but this research compares the solutions from solving each region independently and solving all five zones simultaneously. The results indicate that the proposed solution method can achieve computation time and total cost that are comparable to ones obtained from solving the problem to optimality. Exact approach can only solve small and medium problems, whereas the proposed solution method provides the acceptable solution of real-life largest problem in limit of computation time. Finally, we perform sensitivity analysis on the results
Deep Metric Learning via Facility Location
Learning the representation and the similarity metric in an end-to-end
fashion with deep networks have demonstrated outstanding results for clustering
and retrieval. However, these recent approaches still suffer from the
performance degradation stemming from the local metric training procedure which
is unaware of the global structure of the embedding space.
We propose a global metric learning scheme for optimizing the deep metric
embedding with the learnable clustering function and the clustering metric
(NMI) in a novel structured prediction framework.
Our experiments on CUB200-2011, Cars196, and Stanford online products
datasets show state of the art performance both on the clustering and retrieval
tasks measured in the NMI and Recall@K evaluation metrics.Comment: Submission accepted at CVPR 201
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Improvements and comparison of heuristics for solving the uncapacitated multisource Weber problem
Copyright @ 2000 INFORMSThe multisource Weber problem is to locate simultaneously m facilities in the Euclidean plane to minimize the total transportation cost for satisfying the demand of n fixed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial solutions for exact algorithms. We compare various heuristics, i.e., alternative location-allocation (Cooper 1964), projection (Bongartz et al. 1994), Tabu search (Brimberg and Mladenovic 1996a), p-Median plus Weber (Hansen ct al. 1996), Genetic search and several versions of Variable Neighbourhood search. Based on empirical tests that are reported, it is found that most traditional and some recent heuristics give poor results when the number of facilities to locate is large and that Variable Neighbourhood search gives consistently best results, on average, in moderate computing time.This study was supported by the Department
of National Defence (Canada) Academic Research; Office of Naval Research Grant N00014-92-J-1194, Natural Sciences and Engineering Research Council of Canada Grant GPO 105574 and Fonds pour la Formation des Chercheurs et lâAide a la Recherche Grant 32EQ 1048; and by an International Postdoctoral Fellowship of the Natural Sciences and Engineering Research Council
of Canada, Grant OGPOO 39682
An oil pipeline design problem
Copyright @ 2003 INFORMSWe consider a given set of offshore platforms and onshore wells producing known (or estimated) amounts of oil to be connected to a port. Connections may take place directly between platforms, well sites, and the port, or may go through connection points at given locations. The configuration of the network and sizes of pipes used must be chosen to minimize construction costs. This problem is expressed as a mixed-integer program, and solved both heuristically by Tabu Search and Variable Neighborhood Search methods and exactly by a branch-and-bound method. Two new types of valid inequalities are introduced. Tests are made with data from the South Gabon oil field and randomly generated problems.The work of the first author was supported by NSERC grant #OGP205041. The work of the second author was supported by FCAR (Fonds pour la Formation des Chercheurs et lâAide Ă la Recherche) grant #95-ER-1048, and NSERC grant #GP0105574
The Rise of Innovation Districts: A New Geography of Innovation in America
As the United States slowly emerges from the great recession, a remarkable shify is occurring in the spatial geogrpahy of innovation. For the past 50 years, the landscape of innovation has been dominated by places like Silicon Valley - suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. A new complementary urban model is now emerging, giving rise to what we and others are calling "innovation districts." These districts, by our definition, are geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. They are also physically compact, transit-accessible, and technicall
PyFR: An Open Source Framework for Solving Advection-Diffusion Type Problems on Streaming Architectures using the Flux Reconstruction Approach
High-order numerical methods for unstructured grids combine the superior
accuracy of high-order spectral or finite difference methods with the geometric
flexibility of low-order finite volume or finite element schemes. The Flux
Reconstruction (FR) approach unifies various high-order schemes for
unstructured grids within a single framework. Additionally, the FR approach
exhibits a significant degree of element locality, and is thus able to run
efficiently on modern streaming architectures, such as Graphical Processing
Units (GPUs). The aforementioned properties of FR mean it offers a promising
route to performing affordable, and hence industrially relevant,
scale-resolving simulations of hitherto intractable unsteady flows within the
vicinity of real-world engineering geometries. In this paper we present PyFR,
an open-source Python based framework for solving advection-diffusion type
problems on streaming architectures using the FR approach. The framework is
designed to solve a range of governing systems on mixed unstructured grids
containing various element types. It is also designed to target a range of
hardware platforms via use of an in-built domain specific language based on the
Mako templating engine. The current release of PyFR is able to solve the
compressible Euler and Navier-Stokes equations on grids of quadrilateral and
triangular elements in two dimensions, and hexahedral elements in three
dimensions, targeting clusters of CPUs, and NVIDIA GPUs. Results are presented
for various benchmark flow problems, single-node performance is discussed, and
scalability of the code is demonstrated on up to 104 NVIDIA M2090 GPUs. The
software is freely available under a 3-Clause New Style BSD license (see
www.pyfr.org)
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