Interactive fuzzy goal programming for multi-objective transportation problems

El-Wahed, Waiel F. Abd; Lee, Sang M.

doi:10.1016/j.omega.2004.08.006

Cited by 165 publications

(67 citation statements)

References 19 publications

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“…Fuzzy compromise programming was first suggested by Zimmerman [33][34][35][36] and has since been applied by numerous authors, including Bit [5][6][7], Chang et al [8], Coffin and Taylor [10], Adb El-Wahed and Lee [1], Li and Lai [20], and Topaloglu and Selim [28]. By obtaining a marginal evaluation of each objective Z g (a mapping which tells us to what degree the decision x [ X makes the objective Z g close to its aspiration level L g ) for g = 1,…G where G is the total number of objective functions, we can aggregate these marginal evaluations to find a compromise solution at which the global evaluation of the synthetic membership degree of optimum for all objectives is maximum [20].…”

Section: Solving the Multiobjective Cdn Modelmentioning

confidence: 99%

Designing content distribution networks for optimal cost and performance

2011

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One strategy for alleviating excess latency (delay) in the Internet is the caching of web content at multiple locations. This reduces the number of hops necessary to reach the desired content. This strategy is used for web content such as html pages, images, streaming video, and Internet radio. The network of servers which store this content, and the collections of objects stored on each server, is called a content distribution network (CDN). In order to optimally design a CDN, given a network topology with available server storage capacity at various points in the network, one must decide which object collections to place on each server in order to achieve performance or cost objectives. The placements must be within the storage limits of the servers and must reflect the request patterns for each collection of objects to be cached. Researchers have suggested formulations for the CDN problem which address performance by minimizing latency (the average number of hops is a commonly accepted measure of latency) from client to content, or formulations that focus on minimizing cost of storage and/or bandwidth. In this research, we develop a model which allows for the simultaneous treatment of performance and cost, present examples to illustrate the application of the model and perform a detailed designed experiment to gain insights into cost/hops tradeoff for a variety of network parameters.

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Section: Solving the Multiobjective Cdn Modelmentioning

confidence: 99%

Designing content distribution networks for optimal cost and performance

2011

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“…In addition, Li and Lai (2000) proved that using the min-operator does not guarantee an efficient solution [9]. In this paper, we introduce an interactive fuzzy goal programming model for regional port transportation networks optimization, which is the combination of interactive programming, fuzzy programming and goal programming, and leverages the advantages of the three approaches as well as reduces some or all of the shortcomings of each individual approach [10].…”

Section: ∑ ∑mentioning

confidence: 99%

Interactive Fuzzy Goal Programming Approach for Optimization of Extended Hub-and-Spoke Regional Port Transportation Networks

Wang

Jiang

2007

Computational Science – ICCS 2007

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Abstract. Based on interactive fuzzy goal programming, a model is introduced for extended hub-and-spoke regional port transportation network optimization problem where one of the objective functions is considered as a non-linear function. The proposed model considers the imprecise nature of the input data and assumes that each objective function has a fuzzy goal, and aims at jointly minimizing the total costs including the sailing cost and handling cost as well as the total transit times consisting of sailing time and handling time. Meanwhile it optimizes the following factors: transportation quantities via a hub port from an original port to a destination port, transportation quantities directly from an original port to a destination port. The solution procedure to the proposed model is then presented. At last, a numerical example is given to demonstrate the effectiveness of the proposed model and evaluate the performance of the solution procedure.

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“…Additionally, in practical MDPD problems, the decision maker (DM) must simultaneously handle multiple conflicting goals in term of the use of organizational resources, and achieve these conflicting objectives in a framework of fuzzy aspiration level (Masud and Hwang 1980;Li and Lai 2000;Sabri and Beamon 2000;Fung et al 2003;Kumar et al 2004;Abd El-Wahed and Lee 2006;Liang 2006Liang , 2007. Therefore, this work presents a possibilistic linear programming (PLP) method for solving the integrated MDPD problems with multiple imprecise goals in a supply chains under uncertain environments.…”

Section: Introductionmentioning

confidence: 99%