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International Conference on Industrial Engineering (ICIE)

作者： Davood Shishebori Abolghasem Yousefi-Babadi Ashkan Davoudi Yazd University Yazd Iran University of Tehran Tehran Iran Mazandaran university of science and technology Mazamdaran Iran

ISBN: (纸本)9781509004591

In this paper, a robust multi objective mixed integer linear programming model is proposed for a robust facility location network design problem, which takes uncertain parameters (including demand and transfer costs) into the account. The proposed model simultaneously minimizes the total operating costs and maximizes the total coverage. Moreover, an investment budget constraint is raised on facility location and link construction. Therefore, this model determines the optimal locations of new facilities, link roads that should be constructed and also demand nodes covered by these facilities and link roads. Then, the proposed model is robust by suitable approaches. An efficient multi objective approach, based on Lp-metrics- is proposed and tested for several test problems. A practical case study, based on an integrated distribution system, is presented in detail to illustrate the application of the proposed mathematical model. Finally, a sensitivity analysis is done to provide an insight into the behavior of the proposed model in response to changes of key parameters of the problem.

关键词： Robustness Mathematical model optimization Investment Uncertainty Linear programming Stochastic processes

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Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance optimization Problems

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IEEE TRANSACTIONS ON RELIABILITY 2015年第2期64卷 645-660页

作者： Compare, Michele Zio, Enrico Politecn Milan Dept Energy I-20133 Milan Italy Ecole Cent Paris & Supelec European Fdn New Energy Elect France Syst Sci & Energet Challenge Paris France

The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting;this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values;ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization;and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry.

关键词： Evidence theory genetic algorithms pareto dominance maintenance optimization

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Bi-criterion procedures to support logistics decision making: cost and uncertainty

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2015年第12期66卷 2086-2091页

作者： Rijpkema, Willem A. Hendrix, Eligius M. T. Rossi, Roberto Wageningen Univ NL-6706 KN Wageningen Netherlands Univ Malaga E-29071 Malaga Spain Univ Edinburgh Edinburgh Midlothian Scotland

In practical decision making, one often is interested in solutions that balance multiple objectives. In this study we focus on generating efficient solutions for optimization problems with two objectives and a large but finite number of feasible solutions. Two classical approaches exist, being the constraint method and the weighting method, for which a specific implementation is required for this problem class. This paper elaborates specific straightforward implementations and applies them to a practical allocation problem, in which transportation cost and risk of shortage in supplied livestock quality are balanced. The variability in delivered quality is modelled using a scenario-based model that exploits historical farmer quality delivery data. The behaviour of both implementations is illustrated on this specific case, providing insight in (i) the obtained solutions, (ii) their computational efficiency. Our results indicate how efficient trade-offs in bi-criterion problems can be found in practical problems.

关键词： multi-objective optimization allocation problems scenario-based modelling constraint method weighting method stochastic programming

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Self-Configuring Ensemble of Neural Network Classifiers for Emotion Recognition in the Intelligent Human-Machine Interaction

Self-Configuring Ensemble of Neural Network Classifiers for ...

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IEEE Symposium Series Computational Intelligence

作者： Sopov, Evgenii Ivanov, Ilia Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9781479975600

Reducing the dimensionality of datasets and configuring learning algorithms for solving particular practical tasks are the main problems in machine learning. In this work we propose multi-objective optimization approach to feature selection and base learners hyper-parameter optimization. The effectiveness of the proposed multi-objective approach is compared to the single-objective approach. We have chosen emotion recognition problem by audio-visual data as a benchmark for comparing the two mentioned approaches. We have chosen neural network as a base learning algorithm for testing the proposed approach to parameter optimization. As a result of multi-objective optimization applied to parameter configuration we get the Pareto set of neural networks with optimal parameter values. In order to get the single output, the Pareto optimal neural networks were combined into an ensemble. We have examined several ensemble model fusion techniques including voting, average class probabilities and meta-classification. According to results, multi-objective optimization approach to feature selection provides an average 2.8% better emotion classification rate on the given datasets than single-objective approach. multi-objective approach is 5.4% more effective compared to principal components analysis, and 13.9% more effective compared to not using any dimensionality reduction at all. multi-objective approach applied to neural networks parameter optimization provided on average 7.1% better classification rate than single-objective approach. The results suggest that the proposed multi-objective optimization approach is more effective at solving considered emotion recognition problem.

关键词： multiobjective optimization

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Modeling and optimization of multi objective non-linear programming problem in intuitionistic fuzzy environment

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APPLIED MATHEMATICAL MODELLING 2015年第16期39卷 4617-4629页

作者： Singh, Sujeet Kumar Yadav, Shiv Prasad Indian Inst Technol Dept Math Roorkee 247667 Uttar Pradesh India

In dealing with real world practical optimization problems, a decision maker usually faces a state of uncertainty as well as hesitation, due to various unpredictable factors. Sometimes it is necessary to optimize, several non-linear and conflicting objectives simultaneously. To deal with the uncertain parameters which arise in such situations, intuitionistic fuzzy numbers are utilized. We formulate a multiobjective non-linear programming problem in intuitionistic fuzzy environment. We propose a linear ranking function and utilize it to convert the intuitionistic fuzzy model into a crisp model. After converting the problem into equivalent crisp problem, we propose a non-linear membership function and develop various approaches for solving it by using different operators and fuzzy programming technique. We apply our methodologies for justification to a numerical problem in manufacturing systems. (C) 2015 Elsevier Inc. All rights reserved.

关键词： multiple objective programming Intuitionistic fuzzy number Accuracy function Nonlinear programming

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Sensitivity analysis of pareto solution sets of multiobjective optimization for a batch cooling crystallization process

Sensitivity analysis of pareto solution sets of multiobjecti...

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Indian Control Conference (ICC)

作者： Kilari Hemalatha Kalipatnapu Yamuna Rani Chemical Engineering Division CSIR-Indian Institute of Chemical Technology Academy of scientific and innovative research (AcSIR) CSIR-IICT Hyderabad Telangana India

An increased interest in pharmaceutical crystallization has been observed in recent years where the product quality is affected by variations in product crystal size distribution, purity, and morphology of the crystals which in turn affect the downstream processes such as filtration, milling, and eventually, formulation. Therefore, it is necessary to ascertain the optimal operation of the process in terms of optimal temperature policies in order to achieve the desired product crystal size distribution. In this work, the optimization of an unseeded batch cooling crystallization of paracetamol within a multi-objective framework is studied. The weight mean size and coefficient of variation of final crystals are considered as objectives, which give rise to a set of optimal solutions known as Pareto-optimal solutions using non-dominated sorting genetic algorithm. The present work focuses on evaluating the sensitivity of optimal solutions for induced disturbances and uncertainties in process model mismatch during practical applications through simulation and optimization. It has been observed that the selection of a cooling profile exerts a strong influence on final product crystal size distribution which reiterates the importance of optimization and control in batch cooling crystallization processes industrially at a large scale. Further, in this study, an initial attempt has been made to provide some directions regarding solution approaches to the sensitivity issues.

关键词： Crystallization Cooling optimization Solvents Trajectory

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Towards the standardization of distributed Embodied Evolution

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INFORMATION SCIENCES 2015年 312卷 55-77页

作者： Prieto, A. Bellas, F. Trueba, P. Duro, R. J. Univ A Coruna Integrated Grp Engn Res Ferrol 15403 A Coruna Spain

The Embodied Evolution (EE) paradigm arose in the early 2000s as a response to the automatic design of distributed control systems in real time for teams of autonomous robots. The interest for this type of evolutionary approach has been increasing steadily, not only in its native field of robotics, but also in other fields related to distributed optimization problems since previous works have shown its capability to outperform traditional evolutionary techniques when the scenario requires an on-line coordination of the team. Most of the activity in this research field has been eminently practical, meaning that authors have focused their efforts on developing EE algorithms and variations adapted to solve very specific practical cases. The problem that arises is that, on one hand, all these dissimilar variations of the basic EE structure produce an unclear state of the art and, on the other, that there is a high dependence between the performance obtained by the algorithms and the specific problems where they have been tested, which complicates extrapolating conclusions to different scenarios. As a consequence, this work has two main objectives, namely, designing and implementing a standard EE algorithm that captures the more general principles of this paradigm and that can be applied to any distributed optimization problem, and analyzing how its parameters influence the performance of the algorithms in a set of theoretical representative problems so that objective and reliable conclusions about the behavior of EE can be obtained. At the same time, this work presents an analysis of the evaluation criteria required for coordination tasks when using decentralized distributed approaches, which has influenced both, the definition of the algorithm and the selection of experimental set to test it. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Embodied Evolution Evolutionary computation Distributed optimization Collective intelligence multi-robot system

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multi-objective optimization coupled with dimension-wise polynomial-based approach in smart well placement under model uncertainty

Multi-objective optimization coupled with dimension-wise pol...

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SPE Reservoir Simulation Symposium 2015

作者： Chang, Yuqing Petvipusit, Kurt R. Devegowda, Deepak University of Oklahoma United States International Research Institute of Stavanger Norway Imperial College London United Kingdom

ISBN: (纸本)9781510800618

Targeting hydrocarbon-rich zones and locating smart wells optimally are critical to oil and gas field development. With model uncertainty being one of the main considerations in well placement, there are several challenges associated with the statistical inference from objective distributions which are normally unknown a priori and the estimation may become computationally intensive. To overcome these challenges, we use a robust multi-objective optimization approach to handle model uncertainty and propose an efficient polynomial-based technique to accelerate optimization process. To handle model uncertainty, we implement an evolutionary multi-objective optimization algorithm (NSGA-II) with a mean-variance approach to seek robust solutions for well placement, where the robustness is considered for the uncertainty of formation characteristics. To assist the optimization process, we apply a polynomial-based sparse grid interpolation approach coupled with the multi-objective optimization algorithm. This workflow is demonstrated on a reservoir case study where the results indicate that the optimization approach leads to improved decision making capabilities by providing a suite of well planning solutions. This work highlights the advantage of utilizing the polynomial-based approach - The computational cost from multi-objective optimization can be substantially reduced in comparison to traditional approaches, which makes the proposed workflow more practical, rapid and repeatable, thus able to assist the decision making process in large field development. Copyright © 2015, Society of Petroleum Engineers.

关键词： Polynomials

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A practical Case of the multiobjective Knapsack Problem: Design, Modelling, Tests and Analysis 9th

A Practical Case of the Multiobjective Knapsack Problem: Des...

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9th International Conference on Learning and Intelligent optimization (LION)

作者： Chabane, Brahim Basseur, Matthieu Hao, Jin-Kao Univ Angers LERIA F-49045 Angers 01 France GePI Conseil F-49000 Angers France

ISBN: (纸本)9783319190846;9783319190839

In this paper, we present a practical case of the multiobjective knapsack problem which concerns the elaboration of the optimal action plan in the social and medico-social sector. We provide a description and a formal model of the problem as well as some preliminary computational results. We perform an empirical analysis of the behavior of three metaheuristic approaches: a fast and elitist multiobjective genetic algorithm (NSGA-II), a Pareto Local Search (PLS) algorithm and an Indicator-Based multi-objective Local Search (IBMOLS).

关键词： Combinatorial optimization

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MOMM: multi-objective model merging

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JOURNAL OF SYSTEMS AND SOFTWARE 2015年 103卷 423-439页

作者： Mansoor, Usman Kessentini, Marouane Langer, Philip Wimmer, Manuel Bechikh, Slim Deb, Kalyanmoy Univ Michigan Dearborn MI 48128 USA Vienna Univ Technol A-1040 Vienna Austria

Nowadays, software systems are complex and large. To cope with this situation, teams of developers have to cooperate and work in parallel on software models. Thus, techniques to support the collaborative development of models are a must. To this end, several approaches exist to identify the change operations applied in parallel, to detect conflicts among them, as well as to construct a merged model by incorporating all non-conflicting operations. Conflicts often denote situations where the application of one operation disables the applicability of another one. Consequently, one operation has to be omitted to construct a valid merged model in such scenarios. When having to decide which operation to omit, the importance of its application has to be taken into account depending on the operation type and the application context. However, existing works treat the operations to merge with equal importance. We introduce in this paper, for the first time, a multi-objective formulation of the problem of model merging, based on NSGA-II, that aims to find the best trade-off between minimizing the number of omitted operations and maximizing the number of successfully applied important operations. We evaluated our approach using seven open source systems and compared it with different existing model merging approaches. The merging solutions obtained with our approach were found in all of the scenarios of our experiments to be comparable in terms of minimizing the number of conflicts to those suggested by existing approaches and to carry a high importance score of merged operations. Our results also revealed an interesting feature concerning the trade-off between the two conflicting objectives that demonstrates the practical value of taking the importance of operations into account in model merging tasks. In fact, the shape of the Pareto front represents an interesting guidance for developers to select best solutions based on their preferences. (C) 2014 Elsevier Inc. All rights r

关键词： Search-based software engineering Merging multi-objective optimization

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