Study on multiobjective transportation problem with fuzzy numbers

Ammar, E. E.; Youness, Ebrahim A.

doi:10.1016/j.amc.2004.04.103

Cited by 58 publications

(27 citation statements)

References 9 publications

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“…However, many real-world problems often involves uncertain information such as fuzzy and stochastic demands and capacities, and a vast amount of researches have been done on the fuzzy/stochastic optimization methods for transportation problems [54][55][56][57][58]. We are currently introducing fuzzy and stochastic characteristics into our problem model and extending our HCMPSO algorithm to support fuzzy and stochastic optimization, in order to greatly increase the applicability of the model and algorithm.…”

Section: Resultsmentioning

confidence: 96%

Cooperative particle swarm optimization for multiobjective transportation planning

Zheng

Chen

2013

Appl Intell

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The paper presents a multiobjective optimization problem that considers distributing multiple kinds of products from multiple sources to multiple targets. The problem is of high complexity and is difficult to solve using classical heuristics. We propose for the problem a hierarchical cooperative optimization approach that decomposes the problem into low-dimensional subcomponents, and applies Pareto-based particle swarm optimization (PSO) method to the main problem and the subproblems alternately. In particular, our approach uses multiple sub-swarms to evolve the sub-solutions concurrently, controls the detrimental effect of variable correlation by reducing the subproblem objectives, and brings together the results of the sub-swarms to construct effective solutions of the original problem. Computational experiment demonstrates that the proposed algorithm is robust and scalable, and outperforms some state-of-the-art constrained multiobjective optimization algorithms on a set of test problems.

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Section: Resultsmentioning

confidence: 96%

Cooperative particle swarm optimization for multiobjective transportation planning

Zheng

Chen

2013

Appl Intell

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“…Obviously the physical distances can be considered as constant values in a given relation, but transit times are subject to external factors [12]. Furthermore the actual costs are rarely constant and predictable, so fuzzy cost coefficient can be applied in order to represent the uncertainty [1,19]. In modern logistics systems uncertainty and inaccuracy are not tolerated due to the widespread just-in-time approach.…”

Section: Introductionmentioning

confidence: 99%

Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm

Földesi

Botzheim

2010

Memetic Comp.

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The aim of the traveling salesman problem (TSP) is to find the cheapest way of visiting all elements in a given set of cities and returning to the starting point. In solutions presented in the literature costs of travel between nodes (cities) are based on Euclidean distances, the problem is symmetric and the costs are constant and crisp values. Practical application in road transportation and supply chains are often fuzzy. The risk attitude depends on the features of the given operation. The model presented in this paper handles the fuzzy, time dependent nature of the TSP and also gives solution for the asymmetric loss aversion by embedding the risk attitude into the fitness function of the bacterial memetic algorithm. Computational results are presented as well.

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“…Li and Lai (2000) proposed a fuzzy compromise programming approach to a multiobjective linear transportation problem. For further references in this direction, we refer the reader to Chanas et al (1993), Kikuchi (2000), Abd El-Wahed (2001), Ammar and Youness (2005), Chiang (2005).…”

mentioning

confidence: 98%

An algorithm for a fuzzy transportation problem to select a new type of coal for a steel manufacturing unit

Gupta

Mehlawat

2007

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Study on multiobjective transportation problem with fuzzy numbers

Cited by 58 publications

References 9 publications

Cooperative particle swarm optimization for multiobjective transportation planning

Cooperative particle swarm optimization for multiobjective transportation planning

Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm

An algorithm for a fuzzy transportation problem to select a new type of coal for a steel manufacturing unit

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