Self-organization via competition, cooperation and categorization applied to extended vehicle routing problems

Matsuyama, Yasuo

doi:10.1109/ijcnn.1991.155208

Cited by 19 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although neural network models can handle spatial relationships among vertices, they cannot easily handle side constraints, such as capacity constraints or time windows, that prevent a pure spatial interpretation of the problem. Therefore, the literature on NNs applications to vehicle routing problems is scant, except for a special class of models, known as deformable templates, like the elastic net and the self-organizing map [28,29,50,55,75,86]. The work of Ghaziri [28,29] will be used in the following to illustrate how a deformable template, here a self-organizing map, can be applied to the standard VRP with capacity constraints.…”

Section: Neural Networkmentioning

confidence: 99%

A Review of Bio-inspired Algorithms for Vehicle Routing

Potvin

2009

Bio-Inspired Algorithms for the Vehicle Routing Problem

View full text Add to dashboard Cite

Summary. This chapter reviews biologically inspired algorithms for solving a class of difficult combinatorial optimization problems known as vehicle routing problems, where least-cost collection or delivery routes are designed to serve a set of customers in a transportation network. From a methodological standpoint, the review includes evolutionary algorithms, ant colony optimization, particle swarm optimization, neural networks, artificial immune systems and hybrids. From an application standpoint, the most popular vehicle routing variants are considered, starting with the classical vehicle routing problem with capacity constraints.

show abstract

Section: Neural Networkmentioning

confidence: 99%

A Review of Bio-inspired Algorithms for Vehicle Routing

Potvin

2009

Bio-Inspired Algorithms for the Vehicle Routing Problem

View full text Add to dashboard Cite

show abstract

“…We consider the three SOM based approaches of Ghaziri (1996), Modares et al (1999), and Schwardt and Dethloff (2005) which provide comparative studies, and report results for the well known Christofides et al (1979) benchmark problems, denoted CMT problems below. Other neural network versions (Gomes and Von Zuben 2002;Matsuyama 1991;Schumann and Retzko 1995;Vakhutinsky and Golden 1994) are quite algorithmically similar or clearly worse performing. Only Ghaziri (1996) addresses the timeduration version of VRP and solves almost all the corresponding CMT test cases.…”

Section: Description Of the Experimental Contextmentioning

confidence: 95%

“…Few works were carried out trying to extend SOM, or elastic nets, to the VRP. As far as we know, the most recent approaches are Ghaziri (1996), Gomes and Von Zuben (2002), Matsuyama (1991), Modares et al (1999), Schumann and Retzko (1995), Schwardt and Dethloff (2005) and Vakhutinsky and Golden (1994). They are generally based on a complex modification of the internal learning law, altered by problem dependant penalties.…”

Section: Introductionmentioning

confidence: 97%

The memetic self-organizing map approach to the vehicle routing problem

Créput

Koukam

2008

Soft Comput

View full text Add to dashboard Cite

The paper presents an extension of the selforganizing map (SOM) by embedding it into an evolutionary algorithm to solve the Vehicle Routing Problem (VRP). We call it the memetic SOM. The approach is based on the standard SOM algorithm used as a main operator in a population based search. This operator is combined with other derived operators specifically dedicated for greedy insertion moves, a fitness evaluation and a selection operator. The main operators have a similar structure based on the closest point findings and local moves performed in the plane. They can be interpreted as performing parallels and massive insertions, simulating the behavior of agents which interact continuously, having localized and limited abilities. This self-organizing process is intended to allow adaptation to noisy data as well as to confer robustness according to demand fluctuation. Selection is intended to guide the population based search toward useful solution compromises. We show that the approach performs better, with respect to solution quality and/or computation time, than other neural network applications to the VRP presented in the literature. As well, it substantially reduces the gap to classical Operations Research heuristics, specifically on the large VRP instances with time duration constraint.

show abstract

“…Few works were carried out trying to extend SOM, or elastic nets, to address the VRP. From our knowing, the most recent approaches are [12], [13], [21], [24], [29], [31]. They are generally based on a complex modification of the internal learning law and winner selection, altered by problem dependant penalties.…”

Section: Experiments Overviewmentioning

confidence: 99%

“…As far as we known, only [12], [24] have made significant use of publicly available test problems which are the Christofides, Mingozzi and Toth (CMT) [4] benchmark test set. Other approaches as [13], [21], [29], [31] are referred as less performing or are hard to evaluate since they have typically used just a few and specific test problems. All these approaches are based on a complex modification of the SOM internal learning law.…”

Section: Introductionmentioning

confidence: 99%

Transport clustering and routing as a visual meshing process

Créput

Koukam

2007

Journal of Information and Optimization Sciences

View full text Add to dashboard Cite

We propose an extension of the self-organizing map by embedding it into an evolutionary algorithm to deal with a combination of the Euclidean vehicle routing problem (VRP) and clustering k-median problem. Vehicle routes are deformable visual patterns adapting shapes to distributed demands, considering capacity and time duration constraints. Here, routes are defined among bus stops which represent clusters of customers. First, we show that the approach outperforms on accuracy other neural networks applications to the VRP. Then, we apply it to a real life case of combined clustering and routing wit capacity constraint for the transportation of the 780 employees of an enterprise, by solving the problem in a unified way, as well as sequentially by clustering first and routing second.

show abstract

Self-organization via competition, cooperation and categorization applied to extended vehicle routing problems

Cited by 19 publications

References 7 publications

A Review of Bio-inspired Algorithms for Vehicle Routing

A Review of Bio-inspired Algorithms for Vehicle Routing

The memetic self-organizing map approach to the vehicle routing problem

Transport clustering and routing as a visual meshing process

Contact Info

Product

Resources

About