A Heuristic Program for Locating Warehouses

Kuehn, Alfred A.; Hamburger, Michael

doi:10.1007/978-3-642-51565-1_123

Cited by 282 publications

(143 citation statements)

References 18 publications

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“…The earliest works on the UFL problem date back to the 50's and 60's (see Koopmans and Beckmann [1957], Kuehn and Hamburger [1963], Manne [1964], Efroymson and Ray [1966]). Since then, several books were published on the UFL and other variants of location problems, see Love et al [1988], Mirchandani and Francis [1990], Drezner [1995], Daskin [1995], Farahani and Hekmatfar [2009], Eiselt and Marianov [2011].…”

Section: On Logistics Problemsmentioning

confidence: 99%

Exact algorithms for different classes of vehicle routing problems

Roberti¹

2012

4OR-Q J Oper Res

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Section: On Logistics Problemsmentioning

confidence: 99%

Exact algorithms for different classes of vehicle routing problems

Roberti¹

2012

4OR-Q J Oper Res

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“…Many methods have been proposed for tackling this problem, such as Lagrangian relaxation (e.g. [7,11,12]), polyhedral approach [1], branch and bound ( [2,13]), local search [16].…”

Section: A Local Search Heuristicmentioning

confidence: 99%

“…For deciding which facilities to open we will use a local search heuristic based on the procedure developed by Kuehn and Hamburger in [16].…”

Section: A Local Search Heuristicmentioning

confidence: 99%

Locating repair shops in a stochastic environment

Ommeren

Bumb

Sleptchenko

2006

Computers & Operations Research

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In this paper we consider a repair shop location problem with uncertainties in demand. New local repair shops have to be opened at a number of locations. At these local repair shops, customers arrive with broken, but repairable, items. Customers go to the nearest open repair shop. Since they want to leave as soon as possible, a (small) inventory of working items is kept at the repair shops. A customer immediately receives a working item from stock, provided that the stock is not empty. If a stockout occurs, the customer has to wait for a working item. The broken items are repaired in the shop and then put in stock. Sometimes, however, a broken item cannot be fixed at the local repair shop, and it has to be sent to a central repair shop. At the central repair shop the same policy with inventory and repair is used.The problem that we focus on, is not only finding locations for the local repair shops, but also minimizing the stock levels at the shops, such that the fraction of customers that can leave the local shops without waiting (the so called fill rate), is above a prespecified level. We assume that the central repair shop is already opened, but that the repair capacity still has to be set. The local repair shops can be opened at a number of locations, which may have different repair capacities.The goal is to minimize the total cost, that is the total cost for keeping the local shops operational, for the transport of items and for the inventory. For this minimizing problem, a local search heuristic with respect to the open locations, repair capacities and inventory levels is presented.

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“…The objective is to choose, from a set of potential facility-locations on a network, which ones to open to minimize the sum of opening (or fixed) costs and service (or variable) costs to satisfy the known demands from a set of customers. Although the origins of the plant-location problem go back to the pioneering work of Weber (1909), the actual formulation of SPLP may be attributed to Stollsteimer (1963), Kuehn and Hamburger (1963), and Balinski (1965). Since that time numerous articles have been published that deal with the properties and solution of the mathematical model, e.g., see the surveys in Krarup and Pruzan (1983), Labbé et al (1995), Labbé and Louveaux (1997), and the books by Mirchandani and Francis (1990), Francis et al (1992), and Daskin (1995).…”

Section: Introductionmentioning

confidence: 99%

“…Earlier heuristics typically involve a local search on the primal problem using add, drop, and interchange moves (Kuehn and Hamburger 1963, Feldman et al 1966, Teitz and Bart 1968, Manne 1964. See Cornuéjols et al (1977) for an analysis of the worst case behavior of such methods.…”

Section: Introductionmentioning

confidence: 99%

Primal-Dual Variable Neighborhood Search for the Simple Plant-Location Problem

Hansen

Brimberg

Urošević

et al. 2007

INFORMS Journal on Computing

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T he variable neighborhood search metaheuristic is applied to the primal simple plant-location problem and to a reduced dual obtained by exploiting the complementary slackness conditions. This leads to (i) heuristic resolution of (metric) instances with uniform fixed costs, up to n = 15 000 users, and m = n potential locations for facilities with an error not exceeding 0.04%; (ii) exact solution of such instances with up to m = n = 7 000; and (iii) exact solutions of instances with variable fixed costs and up to m = n = 15 000.

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A Heuristic Program for Locating Warehouses

Cited by 282 publications

References 18 publications

Exact algorithms for different classes of vehicle routing problems

Exact algorithms for different classes of vehicle routing problems

Locating repair shops in a stochastic environment

Primal-Dual Variable Neighborhood Search for the Simple Plant-Location Problem

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