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Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年 182卷 324-341页

作者： Ransikarbum, Kasin Mason, Scott J. Clemson Univ Dept Ind Engn Clemson SC 29634 USA Ulsan Natl Inst Sci & Technol Dept Syst Design & Control Engn Ulsan South Korea

Few published research studies focusing on supply chain disruptions and humanitarian logistics examine the response and recovery phases in post-disaster operations. We present a goal programming-based multiple-objective integrated response and recovery model to investigate strategic supply distribution and early-stage network restoration decisions. The model prescribes equity- or fairness-based compromise solutions for user-desired goals, given limited capacity, budget, and available resources. An experimental study demonstrates how different decision making strategies can be formulated to understand important dimensions of decision making. The efficient frontiers are generated to understand the trade-off between objectives and to analyze capacity-related planning strategies. Hazus-generated regional case studies for two regions, South Carolina. and California, demonstrate the applicability of our proposed model in post-disaster operations. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Humanitarian logistics Disaster relief multiple-objective optimization Goal programming

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Strongly Proper Efficient Solutions: Efficient Solutions with Bounded Trade-Offs

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JOURNAL OF optimization THEORY AND APPLICATIONS 2016年第3期168卷 864-883页

作者： Khaledian, Kazhal Khorram, Esmaile Soleimani-damaneh, Majid Amirkabir Univ Technol Fac Math & Comp Sci 424 Hafez Ave Tehran 15914 Iran Univ Tehran Sch Math Stat & Comp Sci Coll Sci Tehran Iran Inst Res Fundamental Sci IPM Sch Math POB 19395-5746 Tehran Iran

In multiple-objective optimization literature, a properly efficient solution has been interpreted as a point in which the trade-offs between all objectives are bounded. In this paper, it is shown that this boundedness does not necessarily hold for problems with three or more objective functions. It is possible that in a properly efficient solution the trade-offs between some objectives are unbounded. To overcome this, in this paper strongly proper efficient solutions are introduced, in which the trade-offs between all objectives are bounded. This notion is defined in different senses, and the relationships between them are investigated. In addition to theoretical discussions, some clarifying examples are given.

关键词： multiple-objective optimization Proper efficiency Strong proper efficiency Trade-off

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Evolutionary Algorithmic Deployment of Radio Beacons for Indoor Positioning

Evolutionary Algorithmic Deployment of Radio Beacons for Ind...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ye, Sipan Chen, Yuanzhu Hu, Ting Mem Univ Newfoundland Dept Comp Sci St John NF A1B 3X5 Canada

ISBN: (纸本)9781509006229

In mobile computing, the location awareness of a mobile device and its user enables numerous personalized and social services such as recommendation of products and sharing current locations on social networks. Extending positioning services to indoor environments augments the value of the mobile communication market vastly. Due to serious signal attenuation, navigation satellites are incapable, and a common approach is to use or deploy small-scale radio frequency transmitters. When deploying these radio beacons, it is crucial to use a small number of them to provide high-quality positioning services. Such a deployment task is a challenging optimization problem, and the system administrators would benefit from having a spectrum of solutions with varying balance between cost and quality. In this study, we propose an Evolutionary Algorithm (EA) to tackle the problem. Using a cost-quality adjustment parameter, our EA framework is able to provide a set of solution options to meet varying requirements balancing cost and quality. This property is a result of the parallel population-based search of EAs, and can be very useful in real-world engineering applications.

关键词： Mobile computing Evolutionary computation Location-aware service Indoor positioning Radio beacons Genetic algorithm multiple-objective optimization

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A new approach to approximate the bounded Pareto front

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2015年第2期82卷 211-228页

作者： Khaledian, Kazhal Soleimani-damaneh, Majid Amirkabir Univ Technol Fac Math & Comp Sci Tehran 15914 Iran Univ Tehran Sch Math Stat & Comp Sci Coll Sci Tehran Iran Inst Res Fundamental Sci IPM Sch Math Tehran Iran

In this paper, a sequential approach, recently addressed by Mueller-Gritschneder et al. [SIAM J Optim 20(2):915-934, 2009] is studied. After a brief review, some weaknesses of the mentioned approach are pointed out. These weaknesses are due to a redundant assumption and the inability to generate the entire Pareto front. We present two improvements for the mentioned approach, firstly by eliminating the assumption of equality between the Pareto front and the weak Pareto front and secondly by presenting a new definition of the boundary of the Pareto front and establishing some theoretical results about it.

关键词： multiple-objective optimization Pareto front Boundary of the Pareto front Indifferent regions NBI method

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Generalized Convexity and Characterization of (Weak) Pareto-Optimality in Nonsmooth Multiobjective optimization Problems

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2015年第4期14卷 877-899页

作者： Soleimani-Damaneh, Majid Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran

Efforts to characterize optimality in nonsmooth and/or nonconvex optimization problems have made rapid progress in the past four decades. Nonsmooth analysis, which refers to differential analysis in the absence of differentiability, has grown rapidly in recent years, and plays a vital role in functional analysis, information technology, optimization, mechanics, differential equations, decision making, etc. Furthermore, convexity has been increasingly important nowadays in the study of many pure and applied mathematical problems. In this paper, some new connections between three major fields, nonsmooth analysis, convex analysis, and optimization, are provided that will help to make these fields accessible to a wider audience. In this paper, at first, we address some newly reported and interesting applications of multiobjective optimization in Management Science and Biology. Afterwards, some sufficient conditions for characterizing the feasible and improving directions of nonsmooth multiobjective optimization problems are given, and using these results a necessary optimality condition is proved. The sufficient optimality conditions are given utilizing a generalized convexity notion. Establishing necessary and sufficient optimality conditions for nonsmooth fractional programming problems is the next aim of the paper. We follow the paper by studying (strictly) prequasiinvexity and pseudoinvexity. Finally, some connections between these notions as well as some applications of these concepts in optimization are given.

关键词： (Weak) Pareto-optimality multiple-objective optimization decision making invexity functional analysis Clarke's gradient

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On optimality and duality for multiple-objective optimization under generalized type I univexity

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2009年第8期86卷 1345-1354页

作者： Soleimani-damaneh, M. Univ Tehran Coll Sci Sch Math Stat & Comp Sci Tehran Iran

In this paper, we deal with the multiple-objective optimization problems, considering an improved definition of generalized type I univex function. Some optimality conditions as well as some duality relations are esta... 详细信息

关键词： optimality duality multiple-objective optimization generalized type I univexity Pareto optimality

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Nonsmooth multiple-objective optimization in separable Hilbert spaces

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2009年第10期71卷 4553-4558页

作者： Soleimani-damaneh, M. Nieto, J. J. Inst Res Fundamental Sci IPM Sch Math Tehran Iran Univ Tehran Sch Math Stat & Comp Sci Univ Coll Sci Tehran Iran Univ Santiago de Compostela Fac Matemat Dept Anal Matemat Santiago De Compostela Spain

关键词： Limiting subdifferential Generalized differentiation multiple-objective optimization Separable Hilbert space

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The performance and locality tradeoff in bittorrent-like file sharing systems

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2014年第4期7卷 469-484页

作者： Huang, Wei Wu, Chuan Li, Zongpeng Lau, Francis C. M. Univ Toronto Dept Comp Engn Toronto ON Canada Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Calgary Calgary AB T2N 1N4 Canada

In recent years, the surge of large-scale peer-to-peer (P2P) applications has brought huge amounts of P2P traffic, which has significantly changed the Internet traffic pattern and increased the traffic-relay cost at the Internet Service Providers (ISPs). To alleviate the stress on networks, methods of localized peer selection have been proposed that advocate neighbor selection within the same network (AS or ISP) to reduce the cross-ISP traffic. Nevertheless, localized peer selection may potentially lead to the downgrade of download speed at the peers, rendering a non-negligible tradeoff between the download performance and traffic localization in the P2P system. Aiming at effective peer selection strategies that achieve any desired Pareto optimum in face of the tradeoff, our contributions in this paper are three-fold: (1) We characterize the performance and locality tradeoff as a multi-objective -matching optimization problem. In particular, we first present a generic weighted -matching model that characterizes the tit-for-tat in BitTorrent-like peer selection. We then introduce multiple optimization objectives into the model, which effectively characterize the performance and locality tradeoff using simultaneous objectives to optimize. (2) We design fully distributed peer selection algorithms that can effectively approximate any desired Pareto optimum of the global multi-objective optimization problem, which represents a desired tradeoff point between performance and locality in the entire system. (3) Taking network dynamics into consideration, we further propose practical protocols that allow each peer to dynamically adjust its peer selection preference on download performance or traffic locality, in order to adapt to the current quality of peering connections, while guaranteeing that the desired tradeoff is still achieved over its entire download process. To support our models and protocols, we have conducted rigorous analysis, extensive simulations, and prototyp

关键词： Peer-to-peer File sharing Traffic locality multiple-objective optimization

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Solving multiple-objective optimization problems using GISMOO algorithm

Solving multiple-objective optimization problems using GISMO...

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World Congress on Nature and Biologically Inspired Computing

作者： Zinflou, Arnaud Gagne, Caroline Gravel, Marc Univ Quebec Chicoutimi Dept Math & Informat Chicoutimi PQ Canada

ISBN: (纸本)9781424450534

In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimization (MOKP). Results show that our approach is able to obtain better performance than two state of the art approaches: NSGAII and PMSMO.

关键词： evolutionnary algorithm artificial immune systems multiple-objective optimization hybridization Pareto front MOKP

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Optimal Allocation of Distributed Generation in Distribution System Considering Time Sequence Data and Low-carbon Economy

Optimal Allocation of Distributed Generation in Distribution...

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IEEE PES General Meeting

作者： Liu, Ke-yan Sheng, Wanxing Liu, Yuan China Elect Power Res Inst Power Distribut Dept Beijing Peoples R China

ISBN: (纸本)9781479964154

With the consideration of time sequence characteristics of load and distributed generation (DG), a novel method is presented for optimal sitting and sizing of DG in distribution system. multiple-objective functions have been formed with the consideration of minimum investment and operational cost of DG, minimum voltage deviation and maximal voltage stability margin. To solve the multiple-objective optimization problem, an Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) has been proposed. Several experiments have been made on the modified PG&E 69-bus and actual 292-bus test systems. The result and comparisons indicate the proposed method for optimal placement and sizing of DG units is feasible and effective.

关键词： Optimal allocation distribution generation distribution system planning multiple-objective optimization

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