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multiple weighted objectives heuristic for the redundancy allocation problem

IEEE TRANSACTIONS ON RELIABILITY 2006年第3期55卷 551-558页

作者： Coit, David W. Konak, Abdulldh Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA Penn State Univ Dept Informat Sci & Technol Reading PA USA

A new heuristic is proposed and tested for system reliability optimization. The multiple weighted objective heuristic is based on a transformation of the problem into a multiple objective optimization problem, and then ultimately, transformation into a different single objective problem. The multiple objectives are to simultaneously maximize the reliability of each individual subsystem. This is a logical approach because system reliability is the product of the subsystem reliabilities, so if they are maximized, the system reliability will also be high. This new formulation and associated heuristic are then based on solving a sequence of linear programming problems. It is one of the very few optimization approaches that allow for linear programming algorithms and software to be used for the redundancy allocation problem when mixing of functionally equivalent components is allowed. Thus, it represents an efficient solution method that relies on readily available optimization tools. The heuristic is tested on many example problems, and compared to competing solution approaches. Overall, the heuristic performance is observed to be very good on the tested problem, and superior to the max-min heuristic regarding both efficiency, and performance.

关键词： multiple objective optimization redundancy allocation system reliability

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Comprehensive Sensitivity Analysis and Multiobjective optimization Research of Permanent Magnet Flux-Intensifying Motors

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第4期66卷 2613-2627页

作者： Zhu, Xiaoyong Huang, Juan Quan, Li Xiang, Zixuan Shi, Bing Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

In this paper, two permanent magnet flux-intensifying motors are designed and optimized to realize the characteristic of L-d > L-q and the flux-intensifying effect. Compared with the conventional interior permanent magnet motor with the characteristic of L-d < L-q, the advantages of a low irreversible demagnetization risk and a wide speed range can be obtained in the motors. To meet multiple design requirements effectively, a comprehensive sensitivity analysis method is first implemented to evaluate the influence of each design variable on the selected optimized objectives of output torque, reverse saliency ratio, and torque ripple. Second, a sequential nonlinear programming algorithm is used for realizing the multiobjective optimizations of strong-sensitive design variables. Then, the performances of the two optimal motors are analyzed and compared with the initial permanent magnet motor in detail. Finally, two prototype motors are manufactured and tested. Both the simulation and experimental results verify the validity of the new flux-intensifying motors and the optimization method.

关键词： Demagnetization electric vehicle flux-intensifying effect finite-element method multiple objective optimization permanent magnet (PM) motor speed range sensitivity analysis

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A tolerance interval based approach to address uncertainty for RAMS plus C optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2007年第4期92卷 408-422页

作者： Martorell, S. Sanchez, A. Carlos, S. Univ Politecn Valencia Dept Chem & Nucl Engn Valencia 46022 Spain Univ Politecn Valencia Dept Stat & Operat Res Valencia 46022 Spain

This paper proposes an approach based on tolerance intervals to address uncertainty for RAMS +C informed optimization of design and maintenance of safety-related systems using a combined Monte Carlo (MC) (simulation) and Genetic Algorithm (search) procedure. This approach is intended to keep control of the uncertainty effects on the decision criteria and reduce the computational effort in simulating RAMS+C using a MC procedure with simple random sampling. It exploits the advantages of order statistics to provide distribution free tolerance intervals for the RAMS+C estimation, which is based on the minimum number of runs necessary to guarantee a probability content or coverage with a confidence level. This approach has been implemented into a customization of the Multi-objective Genetic Algorithm introduced by the authors in a previous work. For validation purposes, a simple application example regarding the testing and maintenance optimization of the High-Pressure Injection System of a nuclear power plant is also provided, which considers the effect of the epistemic uncertainty associated with the equipment reliability characteristics on the optimal testing and maintenance policy. This example proves that the new approach can provide a robust, fast and powerful tool for RAMS+C informed multi-objective optimization of testing and maintenance under uncertainty in objective and constraints. It is shown that the approach proposed performs very favourably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional non-linear space in a tiny fraction of the time required for enumeration of the decision space. In addition, a sensitivity study on the number of generations versus the number of trials (i.e. simulation runs) shows that overall computational resources must be assigned preferably to evolving a larger number of generations instead of being more precise in the quantification of the RAMS +C attribute

关键词： testing and maintenance multiple objective optimization multiple criteria genetic algorithm uncertainty Monte Carlo simulation order statistics

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Multiobjective optimization and marginal pollution abatement cost in the electricity sector - An Israeli case study

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2002年第3期140卷 571-583页

作者： Soloveitchik, D Ben-Aderet, N Grinman, M Lotov, A Minist Natl Infrastruct Dept Econ & Planning IL-91130 Jerusalem Israel Russian Acad Sci Moscow Russia

The multiple objective optimization models for capacity expansion problem of power generation system in the long run as a base for setting up the marginal abatement cost were examined. In the optimization model the objective function is considered as the weighted sum of several objective functions. Air pollutants are taken into account in both the objective function and the constraints. Different scenarios of pollutant reduction were analyzed. The periods of the years 2003-2013 were taken into account and the results are based on the real data of the Israel electricity sector. Several environmental policies were considered by using the CAPEX system to evaluate the environmental and economic deficiencies in different abatement cost scenarios. The following are obtained: abatement cost for each pollutant, amount of emissions and additional cost connected with the pollutants. Modern decision tools are implemented, such as data envelopment analysis (DEA) and reasonable goal method/interactive decision maps (RGM/IDM) technique as a base for decision-makers to make decisions on energy and environmental policy. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： multiple objective optimization marginal abatement cost air pollutants power generation system capacity expansion problem

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Optimum stage design in axial-flow gas turbines

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2002年第A6期216卷 433-445页

作者： Rao, KVJ Kolla, S Penchalayya, C Rao, MA Srinivas, J RVR & JCCE Dept Mech Engn Guntur 522019 India

This paper proposes the formulation and solution procedures in the stage optimization of the effective dimensions of an axial-flow gas turbine. Increasing the stage efficiency and minimizing the overall mass of components per stage are the common objectives in gas turbine design. This multiple objective function, with important constraints like natural frequency limits, root stress values, and tip deflection in blades, constitutes the overall optimization problem. The problem is solved by using a modified nonlinear simplex method with a built-in user interactive program that helps in on-line modifications of parameters other than variables in the problem. Results are presented with single objective and multiple objective criteria, including sensitivity analyses about the optimum point.

关键词： axial flow turbine stage rotor blade design nonlinear simplex method multiple objective optimization

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Multi-objective optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2017年第5期26卷 1760016-1760016页

作者： Shil, Shubhashis Kumar Sadaoui, Samira Univ Regina Dept Comp Sci Regina SK Canada

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer's revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it's significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.

关键词： Combinatorial reverse auctions multiple attributes multiple objective optimization evolutionary algorithms winner determination genetic algorithms diversity and elitism buyer's revenue e-commerce

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Application of a Genetic Algorithm for the optimization of a Complex Reservoir System in Tunisia

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WATER RESOURCES MANAGEMENT 2011年第10期25卷 2387-2404页

作者： Louati, Mohamed Hedi Benabdallah, Sihem Lebdi, Fethi Milutin, Darko Ctr Rech & Technol Eaux Soliman 8020 Tunisia Minist Agr & Ressources Hydraul Direct Gen Barrages & Grands Travaux Hydraul Tunis 1002 Tunisia Inst Natl Agron Tunisie Tunis Tunisia AHT Grp AG D-45128 Essen Germany

A genetic algorithm (GA) model is developed and used for optimizing the allocation of water resources within a complex multiple reservoir system located in Tunisia. The GA model considers two objectives: the water allocation to demand centers and the salinity level of the water supply to end users. These two objectives are combined into a single objective function using a weighting factor approach. Five different cases (representing five different weighting factor combinations) were analyzed by the GA model to produce the "optimum" allocation of water resources for each case. The generated solutions exhibited low variability. The results are then compared using a range of system performance indicators to measure reliability, resilience, and vulnerability. The evaluation of the system performance is an essential step to help system managers identify the preferred allocation strategy and it provides a comprehensive insight into system behavior.

关键词： Genetic algorithms Resource allocation multiple objective optimization

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A new complexity result on multiobjective linear integer programming using short rational generating functions

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optimization LETTERS 2012年第3期6卷 537-543页

作者： Blanco, Victor Puerto, Justo Univ Granada Dept Algebra E-18071 Granada Spain Univ Seville Dept Estadist Invest & Operat E-41012 Seville Spain

This paper presents a new complexity result for solving multiobjective integer programming problems. We prove that encoding the entire set of nondominated solutions of the problem in a short sum of rational functions is polynomially doable, when the dimension of the decision space is fixed. This result extends a previous result presented in De Loera et al. (INFORMS J. Comput. 21(1):39-48, 2009) in that there the number of the objective functions is assumed to be fixed whereas ours allows this number to vary.

关键词： multiple objective optimization Integer programming Short rational generating functions

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The risk element transmission theory research of multi-objective risk-time-cost trade-off

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1792-1799页

作者： Li, Cunbin Wang, Kecheng N China Elect Power Univ Inst Management Beijing 102206 Peoples R China

Risk management project is an important aspect of general project risk element transmission theory. To solve the multi-objective time-cost trade-off problem considering the risk elements effectively, this paper establishes an analytical model for multi-objective risk-time-cost trade-off problem based on general project risk element transmission theory. We divide risk elements into discrete model and continuous model to be discussed separately, and the two models for multi-objective risk-time-cost trade-off problem are established by taking Markov dynamic PERT network into classical PERT network. Thus, we combine Radial Basis Function (RBF) neural network to solve the discrete model of the problem. Finally, a practical example illustrates the effectiveness of the algorithm. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

关键词： multiple objective optimization Markov processes RBF neural network Risk element Transmission

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Fuzzy convexity and multiobjective convex optimization problems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2006年第3-4期52卷 351-362页

作者： Syau, Yu-Ru Lee, E. Stanley Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA Natl Formosa Univ Dept Informat Management Yunlin 63201 Taiwan

In this paper, based on the more restrictive definition of fuzzy convexity due to Ammar and Metz [1], several useful composition rules are developed. The advantages in using the more restrictive definition of fuzzy convexity are that local optimality implies global optimality, and that any convex combination of such convex fuzzy sets is also a convex fuzzy set. As shown in this paper, these properties are laking in the usual convex fuzzy sets. In addition, to illustrate the applications in fuzzy convex optimization, two examples in multiple objective programming are considered. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： fuzzy convexity fuzzy criterion sets pareto-optimal decision multiple objective optimization

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