Kohonen maps for solving a class of location-allocation problems

Lozano, Sebastián; Guerrero, Fernando; Onieva, Luis; Larrañeta, Juan

doi:10.1016/s0377-2217(97)00046-5

Cited by 41 publications

(14 citation statements)

References 27 publications

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“…(For a more detailed discussion of the presentation and adaptation steps in a cycle of a SOM procedure, see Kohonen 1982 and2001. ) The presentation of the input data takes place in stochastic order to enable the procedure to leave local "valleys" (Lozano et al (1998)). When applying SOM procedures to optimisation problems, usually complete presentation cycles are done in order to make sure that all input data are taken into account.…”

Section: Neural Network Approaches Self-organising Maps and Applicatmentioning

confidence: 99%

“…Further problems that have been solved using self-organising map approaches are the M-TSP (e.g., Goldstein 1990, and the (Euclidean) Capacitated Vehicle Routing Problem ((E)CVRP) (e.g., Ghaziri 1991); location-allocation problems are solved using SOMs by Lozano et al (1998).…”

Section: Neural Network Approaches Self-organising Maps and Applicatmentioning

confidence: 99%

“…Therefore, on the one hand, it might be worthwhile after solving such a problem in the plane to try and find additional potential locations close to the optimal point. On the other hand, in a subsequent refined network or discrete analysis, only very few locations would need to be considered, i.e., the problem dimension is reduced considerably (Lozano et al 1998 also mention this option). As combined location-routing problems are in general NP-hard (Laporte 1988), this is an important contribution to and a facilitation in solving such problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined location-routing problems—a neural network approach

Schwardt¹,

Fischer

2008

Ann Oper Res

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While in location planning it is often assumed that deliveries are made on a directtrip basis, in fact deliveries, e.g., to the different supermarkets belonging to a specific chain or to retail outlets of any kind, usually are performed as round-trips. Therefore, it is often necessary to combine the two issues of locating a depot and of planning tours in one problem formulation.In this paper, a neural network approach based on a self-organizing map is proposed for solving such single-depot location-routing problems in the plane. The results derived by this approach are compared with those which can be found by different well-known heuristics, and it is shown that the self-organising map approach competes well with these concepts. Moreover, some modifications which rely on ideas from Tabu Search can be shown to be especially useful for increasing the number of feasible solutions found by the selforganising map approach. Finally, the implementation of the Weiszfeld procedure for a final improvement of the optimal depot location proves to be a useful device.

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Section: Neural Network Approaches Self-organising Maps and Applicatmentioning

confidence: 99%

Section: Neural Network Approaches Self-organising Maps and Applicatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combined location-routing problems—a neural network approach

Schwardt¹,

Fischer

2008

Ann Oper Res

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show abstract

“…Some popular ones include tree search (Christofides and Beasley 1982;Bartezzaghi and Colorni 1981), Lagrangian relaxation with branch and bound (Narula, Ogbu and Samuelsson, 1977;Khumawala, 1972;Galvao, 1980;Erlenkotter, 1978;ReVelle and Swain, 1970), tabu search (Ohlemüller, 1997;Rolland et al, 1996), and other heuristic and decision techniques (Hribara and Daskin, 1997;Hansen, Mladenovic and Taillard, 1998;Drezner and Guyse, 1999), as well as Kohonen maps (Lozano, Guerrero, Onieva and Larrañeta, 1998). A summary of past results is given in Table 1.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A summary of past results is given in Table 1. Khumawala (1972) Branch and Bound Galvao (1980) Erlenkotter (1978 ReVelle and Swain (1970) Tabu Search Rolland et al (1996) Kohonen maps Lozano et al (1998) Hribara and Daskin (1997) Other heuristics Hansen et al (1998) Drezner andGuyse (1999) …”

Section: Problem Formulationmentioning

confidence: 99%

Comparative analysis of modern optimization tools for the p-median problem

Alba

Domínguez

2006

Stat Comput

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This paper develops a study on different modern optimization techniques to solve the p-median problem. We analyze the behavior of a class of evolutionary algorithm (EA) known as cellular EA (cEA), and compare it against a tailored neural network model and against a canonical genetic algorithm for optimization of the p-median problem. We also compare against existing approaches including variable neighborhood search and parallel scatter search, and show their relative performances on a large set of problem instances. Our conclusions state the advantages of using a cEA: wide applicability, low implementation effort and high accuracy. In addition, the neural network model shows up as being the more accurate tool at the price of a narrow applicability and larger customization effort.

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Applying Bio-inspired Techniques to the p-Median Problem

Domínguez

Naranjo

2005

Computational Intelligence and Bioinspired Systems

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Kohonen maps for solving a class of location-allocation problems

Cited by 41 publications

References 27 publications

Combined location-routing problems—a neural network approach

Combined location-routing problems—a neural network approach

Comparative analysis of modern optimization tools for the p-median problem

Applying Bio-inspired Techniques to the p-Median Problem

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