An ant colony system approach for fuzzy traveling salesman problem with time windows

Sarhadi, Hassan; Ghoseiri, Keivan

doi:10.1007/s00170-010-2562-1

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…Furthermore, TSP variations treated with the aid of ACO approaches include (a) TSP with time windows (Sarhadi and Ghoseiri 2010), (b) asymmetric TSP (Bai et al 2013), multi-optimum TSP (Chen and Liu 2009), convergence analysis of TSP (Chen et al 2012), symmetric TSP (Valdez and Chaparro 2013), multi-depot multiple TSP (Ghafurian and Ja-vadian 2011), generalized TSP (Jun-man and Yi 2012) and (Lian-Ming 2011), dynamic TSP (Mavrovouniotis and Yang 2013).…”

Section: Literature Survey Findingsmentioning

confidence: 99%

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Dounias

Vassiliadis

2014

International Journal of Natural Computing Research

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The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

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Section: Literature Survey Findingsmentioning

confidence: 99%

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Dounias

Vassiliadis

2014

International Journal of Natural Computing Research

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“…The general form of the TSP appears to have been first studied by mathematicians during the 1930s in Vienna and at Harvard.Hassler Whitney [6]at Princeton University introduced the name travelling salesman problem. Sarhadi and Ghoseiri [7] extended Travelling salesman problem with time windows to model fuzzy travelling salesman problem with time windows. basic advantage of this new modelling is its flexibility which enables us to optimize the delivery process in the network of customers with different satisfaction patterns and different priorities.…”

Section: Introductionmentioning

confidence: 99%

Hungarian Method to Solve Travelling Salesman Problem with Fuzzy Cost

Nayak¹,

Nanda²

2017

IJMTT

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The Travelling Salesman problem is one of the most common problem in O.R. But in real life situation the problem is often not useful due to its crisp nature. Use of fuzzy can make the problem more useful in day to day life. We consider a Travelling Salesman Problem with costs of travelling as trapezoidal fuzzy numbers. Fuzzy ranking function is used to handel trapezoidal fuzzy numbers. Hungering Algorithm is used to solve the problem. An example illustrates the use of the proposed method.

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“…Like other heuristic algorithms, it searches optimal solution through group evolution process, composed by candidate solution. This method achieved good results when it is first applied to the famous traveling salesman problem (TSP) [2,3]. The features of ant colony algorithm's positive feedback, distributed and greed type heuristic search etc.…”

Section: Introductionmentioning

confidence: 99%

Expert System Aided Multi-Agent Intelligent Ant Colony Optimization System

Zhao

Lin

2011

AMM

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In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper designs an expert system aided multi-agent intelligent ant colony algorithm system to solve the production scheduling optimization model. Where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. And then it builds a system using JESS and JADE to confirm this algorithm applied in a mass customization supply chain scheduling model

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An ant colony system approach for fuzzy traveling salesman problem with time windows

Cited by 16 publications

References 29 publications

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems

Hungarian Method to Solve Travelling Salesman Problem with Fuzzy Cost

Expert System Aided Multi-Agent Intelligent Ant Colony Optimization System

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