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Entropy based solid transportation problems with discounted unit costs under fuzzy random environment

OPSEARCH 2014年第4期51卷 479-532页

作者： Giri, Pravash Kumar Maiti, Manas Kumar Maiti, Manoranjan Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Medinipur W Bengal India Mahishadal Raj Coll Dept Math Medinipur 721628 W Bengal India

This paper investigates some multi-objective fixed charge solid transportation problems (FCSTPs) under fuzzy-random environment with fuzzy-random fixed charges and transportation times. Here objectives are total transportation cost and time which are minimized. Unit transportation costs are crisp and three types of discount i.e. All Unit Discount (AUD), Incremental Quantity Discount (IQD), IQD within AUD are applied upon these costs. In the first model (Model-I) supplies, demands and capacities of conveyances are assumed to be fuzzy-random in nature. These quantities are fuzzy in the second model (ModelII). Imprecise objectives of the models are transformed into equivalent crisp ones using random expectation and fuzzy possibility/necessity measures on fuzzy event. Imprecise constraints are reduced to equivalent crisp constraints using two different fuzzy-random chance constraint methods. Similarly constraints of Model-II are reduced to equivalent crisp constraints with the use of possibility and necessity measures on fuzzy events. Models are also formulated with and without entropy function. Finally transformed constrained multi-objective deterministic problems are solved using a multi-objective genetic algorithm (MOGA) based on arithmetic crossover and boundary mutation. Moreover, Model-IA1 is converted to a single objective non-linear programming (SONLP) problem following fuzzy non-linear programming (FNLP) technique and the reduced ModelIA1S is solved using generalized reduced gradient (GRG) method (using LINGO software). Numerical examples are used for illustration and comparison of the models.

关键词： Solid transportation problem Fuzzy random variable All unit discount Incremental quantity discount Entropy Fuzzy programming technique multi-objective genetic algorithm

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MOGA-UDTG: Automated Uniformly Distributed Testing Approach

MOGA-UDTG: Automated Uniformly Distributed Testing Approach

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Conference on Recent Advances in Engineering and Computational Sciences (RAECS)

作者： Choudhary, Kavita Purohit, G. N. ITM Univ Dept CSE & IT Gurgaon India Banasthali Vidyapeeth Dept AIM & ACT Banasthali India

ISBN: (纸本)9781479922918

Software Testing is the critical task for any software which helps in building trust and confidence in the product. Usually, the emphasis is on Manual Testing but to reduce cost there is a need of automation in software testing process. This paper proposes a method to generate automated uniformly distributed test cases based on the platform of multiobjective genetic algorithm (MOGA). The proposed method is analyzed on the domain of Pareto-Optimality, Dominance and multi-objective genetic algorithm.

关键词： multi-objective genetic algorithm MOGA UDTG Pareto Optimal Non-dominated Sorting Fitness function etc

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Determination of optimal location and capacity of detention facilities

Determination of optimal location and capacity of detention ...

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12th International Conference on Computing-and-Control-for-the-Water-Industry (CCWI)

作者： Lim, S. Ho, V. H. Lee, S. Y. Yoo, D. G. Kim, J. H. Korea Univ Coll Engn Sch Civil Environm & Architecture Engn Seoul 136713 South Korea

The importance of the effective distribution of runoff in sewer network throughout a detention facility has recently been recognized. This study aims to determine the location and the capacity of detention facilities in urban sewer network. The optimal location and capacity of the detention facilities can be determined by numerous iterations of an optimization model. In this paper, an interfacing Visual Basic (VB) programming tool with a SWMM (Storm Water Management Model) DLL for hydraulic and hydrological computation in sewer network completes the optimization routine. A multi-objective genetic algorithm (MOGA) is adopted to minimize both the total overflow and the installing cost. The developed model is applied to S city in South Korea. The goal in prevention performance of urban disaster is used as rainfall. The model will help engineers find effective alternatives to achieve the goal of the urban disaster prevention performance. (C) 2013 The Authors. Published by Elsevier Ltd.

关键词： optimal location and capacity detention facility goal in prevention performance of urban disaster multi-objective genetic algorithm

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Optimal Test Points Selection Based on multi-objective genetic algorithm

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Journal of Electronic Science and Technology of China 2009年第4期7卷 317-321页

作者： Yong Zhang Xi-Xiang Chen Guan-Jun Liu Jing Qiu Shu-Ming Yang College of Mechatronical Engineering and Automation National University of Defense Technology Changsha 410073 China

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.

关键词： Design for testability multi-objective genetic algorithm system testing test points selection.

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Optimization of Square and Circular Ammonia-cooled Microchannel Heat Sink with genetic algorithm

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Energy Procedia 2014年 61卷 55-58页

作者： Normah Mohd-Ghazali Oh Jong-Taek Nguyen Ba Chien Choi Kwang-Il Nor Atiqah Zolpakar Robiah Ahmad Faculty of Mech. Eng. Universiti Teknologi Malaysia81300 SkudaiJohor Malaysia Department of Refrigeration & Air-Cond. Eng. Chonnam National University San 96-IDunduk-DongYeosuRepublic of Korea UTM Razak School of Engineering and Advanced Technology UTM Kuala Lumpur Malaysia

Minimization of the thermal resistance and pressure drop of a microchannel heat sink is desirable for effective heat removal which is becoming a serious challenge due to the continuous miniaturization of such cooling systems with increasing high heat generation rate. Optimization of the hydraulic diameter and wall width to channel width ratio of square and circular microchannels to minimize the two objective functions was completed with the multi-objective genetic algorithm (MOGA). Environmentally friendly liquid ammonia was used as the coolant and the thermophysical properties have been obtained based on the average experimental saturation temperatures measured along an ammonia-cooled 3.0 mm internal diameter horizontal microchannel rig. The optimized results showed that with the same hydraulic diameter and pumping power, circular microchannels have lower thermal resistance. Based on the same number of microchannels per square cm, the thermal resistance for the circular channels is lower by 21% at the lowest pumping power and lower by 35% at the highest pumping power than the thermal resistance for the square microchannels. Results obtained at 10 °C and 5 °C showed no significant difference probably due to the slight difference in properties at these temperatures.

关键词： Optimization Microchannel heat sink multi-objective genetic algorithm ammonia thermal resistance

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Determination of Optimal Location and Capacity of Detention Facilities

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Procedia Engineering 2014年 70卷 1037-1045页

作者： S. Lim V.H. Ho S.Y. Lee D.G. Yoo J.H. Kim Sch. Of Civil. Environmental & Architecture Engineering Coll. of Engineering Korea Univ Anamdpng Seongbuk-gu Seoul 136-713 Korea

关键词： optimal location and capacity detention facility goal in prevention performance of urban disaster multi-objective genetic algorithm

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multiobjective optimization of MPLS-IP networks with a variable neighborhood genetic algorithm

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APPLIED SOFT COMPUTING 2013年第11期13卷 4403-4412页

作者： Onety, Renata E. Tadei, Roberto Neto, Oriane M. Takahashi, Ricardo H. C. Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Politecn Torino Dept Control & Comp Engn Turin Italy FUCAPI Anal Res & Technol Innovat Ctr Manaus Amazonas Brazil Univ Fed Minas Gerais Dept Math Belo Horizonte MG Brazil

This paper presents a genetic algorithm for the optimization of multiple indices of Quality of Service of multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the NSGA-II, with the particular feature that solutions are encoded defining two different kinds of neighborhoods. The first neighborhood is defined by considering as decision variables the edges that form the routes to be followed by each request, whilst the second part of solution is kept constant. The second neighborhood is defined by considering the request sequence as decision variable, with the first part kept constant. Comparisons are performed with: (i) a VNS algorithm that performs a switch between the same two neighborhoods that are used in VN-MGA;and (ii) the results obtained with an integer linear programming solver, running a scalarized version of the multiobjective problem. The results indicate that the proposed VN-MGA outperforms the pure VNS algorithm, and provides a good approximation of the exact Pareto fronts obtained with Integer Linear Programming (ILP) approach, at a much smaller computational cost. Besides potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Routing on IP networks Variable Neighborhood Search multi-objective genetic algorithm

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Contractor Selection in Gas Well-drilling Projects with Quality Evaluation Using Neuro-fuzzy Networks

Contractor Selection in Gas Well-drilling Projects with Qual...

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International Conference on Future Information Engineering(FIE 2014)

作者： Roya M.Ahari S.T.A.Niaki Dept. of Industrial Engineerin Sharif University of Technology

Contractor selection for a project is an important decision, one for the project time and cost, next for the quality obtained by the project. Although the project managers can easily determine the project time and cost, the quality is usually undefined especially for un-experienced managers. With a learnable property, an approach is first introduced in this paper to quantify the quality obtained for a gas well drilling project. Then, based on these three objectives（time, cost, and quality）, a contractor selection problem is converted to an optimization problem. Next, the NSGA-II algorithm is utilized for solution. At the end, a sensitivity analysis is performed to select the parameters of the algorithm.

关键词： Contractor selection Quality evaluation multi-objective optimization Gas well drilling project multi-objective genetic algorithm

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A multi-drive Data Mining algorithm Based on Classification Method and Its Application in Investment Decision

A Multi-drive Data Mining Algorithm Based on Classification ...

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第七届模糊信息与工程国际会议(ICFIE'2014)、第一届运筹与管理国际会议 (ICORG'2014)暨中国运筹学会模糊信息与工程分会第七届年会、广东省运筹学会第二届年会、粤港澳运筹学会首届年会

作者： Yubin Zhong Li Tao Jialiang Xuan Yifan Zhang College of Mathematics and Information Science Guangzhou University College of Civil Engineering Guangzhou University UQ Business School the University of Queensland

This paper discusses the development of strategies in big data context,proposing a multi-drive data mining algorithm based on classification method and solving the investment decision problem when educational charity Goodgrant is planning to invest in 7900 colleges in the U.S. This paper first classifies all the colleges into to-be funded and to-be eliminated based on cluster analysis. Then, run the Fisher discriminant function parameters of the to-be funded colleges. After determining 52 indices which can influence investment return, select 39 effective indices according to Bayesian discriminant and calculate index weights. In the end,build a multi-objective optimal model based on the maximum of Fisher discriminant function and investment return, apply multi-objective genetic algorithm to compute the optimal investment weights of all the colleges and produce investment priority ranking according to the investment weight. The method in this paper can also be used in other investment area among big data context.

关键词： investment decision cluster analysis Bayesian discriminant Fisher discriminant multi-objective genetic algorithm

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Hybrid genetic-Fuzzy algorithm for Variable Selection in Spectroscopy

Hybrid Genetic-Fuzzy Algorithm for Variable Selection in Spe...

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12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013

作者： de Lima, Telma Woerle Soares, Anderson da Silva Coelho, Clarimar Jose Salvini, Rogerio Lopes Laureano, Gustavo Teodoro Univ Fed Goias Inst Informat Goiania Go Brazil Pontifical University Catholic Goias Dept Comp Sci Goiania Go Brazil

ISBN: (纸本)9783642386091

This paper presents a hybrid multi-objective genetic fuzzy algorithm for the variable-selection problem in spectroscopy. The problem formulation considers three fitness functions related to linear equations system stability. These fitness functions are models with fuzzy sets that evaluate the fitness solution for pick out the best to crossover. The population diversity is obtained applying the crowding distance method. The study shows that the selection by a fuzzy decision has better results than the selection by non-domination in problems where the fitness weighing is more proper than no-domination solutions.

关键词： multi-objective genetic algorithm Fuzzy logic Variable Selection

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