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Castro, Carlos. "Multiple criteria optimization in injection molding." Connect to this title online, 2004. http://hdl.handle.net/1811/322.
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Thesis (Honors)--Ohio State University, 2004.Title from first page of PDF file. Document formattted into pages: contains vi, 49 p.; also includes graphics. Includes bibliographical references (p. 46). Available online via Ohio State University's Knowledge Bank.
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Singh, Vijay K. "Equitable efficiency in multiple criteria optimization." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1181669435/.
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Pissarides, Savvas. "Interactive multiple criteria optimization for capital budgeting." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7723.
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This thesis presents a capital budgeting problem faced by a major telecommunications company. The purpose of this thesis is to address the capital budgeting problem in order to establish a framework for the measurement and evaluation of alternative capital allocation decisions which are compatible with the mission of the company. The solution method follows three major avenues of optimization: multiple criteria, multiple constraints and interactivity. The problem is solved using the Analytic Hierarchy Process to obtain an initial solution which is then improved by an interactive method allowing users to direct the search for an acceptable allocation. The method is implemented by a decision support system hinging on a graphic user interface. The support system has been used by practitioners to evaluate alternatives of a real problem. Results and enhancements are discussed.
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Soylu, Banu. "An Evolutionary Algorithm For Multiple Criteria Problems." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608134/index.pdf.
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In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights according to the Tchebycheff distance function. Some seed solutions at initial population and a crowding measure also help to achieve satisfactory results.
In order to test the performance of our evolutionary algorithm, we use some continuous and combinatorial problems. The continuous test problems taken from the literature have special difficulties that an evolutionary algorithm has to deal with. Experimental results of our algorithm on these problems are provided.
One of the combinatorial problems we address is the multi-objective knapsack problem. We carry out experiments on test data for this problem given in the literature.
We work on two bi-criteria p-hub location problems and propose an evolutionary algorithm to approximate the Pareto frontiers of these problems. We test the performance of our algorithm on Turkish Postal System (PTT) data set (TPDS), AP (Australian Post) and CAB (US Civil Aeronautics Board) data sets.
The main contribution of this thesis is in the field of developing a multi-objective evolutionary algorithm and applying it to a number of multi-objective continuous and combinatorial optimization problems.
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Filatovas, Ernestas. "Solving Multiple Criteria Optimization Problems in an Interactive Way." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120402_093953-80981.
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In practice, optimization problems are often multiple criteria. The criteria are usually contradictory, so the final decision depends on a decision maker. When the problem is solved interactively, the decision maker can change his/her preferences in decision process. Moreover, it is important to obtain solutions from the whole Pareto front. A decision support system adapted to the specific of the problem is essential for solving multiple criteria optimization problems interactively. The objects of research are multiple criteria optimization problems, interactive methods for solving these problems, interactive decision support systems, and application of parallel computing in decision support systems.
Multiple criteria optimization methods are analyzed in the dissertation. The focus of attention is the methods for a uniform distribution of solutions on the Pareto front as well as the interactive methods. An interactive way for solving multicriteria optimization problems, which finds alternative solutions uniformly distributed on the Pareto front is proposed and investigated in this dissertation. An interactive decision support system which integrates the created interactive solving way, the decision process visualization and parallelization for multiple criteria optimization is developed. The solving strategies, when a multiple criteria optimization problem is solved interactively, using a computer cluster are developed and compared experimentally. The time required for a... [to full text]Praktikoje dažnai tenka spręsti sudėtingus daugiakriterinius optimizavimo uždavinius, kai kriterijai būna prieštaringi, o galutinis apsisprendimas priklauso nuo sprendimų priėmėjo. Kai sprendimų priėmėjas dalyvauja sprendimo procese interaktyviai, tai jis gali koreguoti prioritetus ir siekiamus tikslus uždavinio sprendimo eigoje, kas įgalina spęsti uždavinius, turinčius daug kriterijų ir apribojimų. Be to, sprendimo priėmėjui svarbu gauti sprendinius iš visos Pareto aibės. Interaktyviam uždavinių sprendimui būtina sprendimų paramos sistema, kurios grafinė sąsaja yra pritaikyta sprendžiamam uždaviniui. Šio darbo tyrimų sritis yra interaktyvus daugiakriterinių optimizavimo uždavinių sprendimas bei sprendimų paramos sistemos. Disertacijoje nagrinėjant daugiakriterinio optimizavimo metodus, didesnis dėmesys skirtas metodams, užtikrinantiems gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje bei interaktyviems metodams. Pasiūlytas ir ištirtas daugiakriterinių optimizavimo uždavinių sprendimo būdas, leidžiantis spręsti daugiakriterinius optimizavimo uždavinius interaktyviai ir užtikrinantis gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje. Sukurta ir ištirta interaktyvi daugiakriterinių optimizavimo uždavinių sprendimų paramos sistemą, apjungianti pasiūlytą optimizavimo uždavinių sprendimo būdą, sprendimo proceso vizualizavimą ir jo lygiagretinimą. Taip pat pasiūlyta sprendimo strategija, pagal kurią sprendžiant daugiakriterinį optimizavimo uždavinį pasitelkiamas... [toliau žr. visą tekstą]
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Cabrera, Rios Mauricio. "MULTIPLE CRITERIA OPTIMIZATION STUDIES IN REACTIVE IN-MOLD COATING." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1022105843.
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Villanueva, Jaquez Delia. "Multiple objective optimization of performance based logistics." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Villarreal-Marroquin, Maria G. "A Metamodel based Multiple Criteria Optimization via Simulation Method for Polymer Processing." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1356518813.
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Nenėnaitė, Rita. "Tarpinių sprendinių panaudojimo tyrimas daugiakriterinių uždavinių sprendimui kompiuterių tinkle." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2004. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040611_155428-81819.
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The study analyses various methods to solve multiple criteria optimization problems of different kinds and defines principles of parallel computing. A multiple criteria optimization problem has been solved applying a computer network and a new strategy that analyses and uses intermediate results in the calculation process has been suggested. The optimization problem has been solved applying a computer network and parallel computing software MPI (Message Passage Interface). Numerous experimental trials have been carried out to investigate efficiency of the designed strategy in the solution of multiple criteria optimization problems. A computer network with different number of computers solved a single problem of different duration and final results of various strategies have been compared. The experiments have proved the designed strategy to be more precise in results and more economical in computing time.
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Bouchery, Yann. "Supply Chain optimization with sustainability criteria : A focus on inventory models." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00784197.
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Sustainability concerns are increasingly shaping customers' behavior as well as companies' strategy. In this context, optimizing the supply chain with sustainability considerations is becoming a critical issue. However, work with quantitative models is still scarce. Our research contributes by revisiting classical inventory models taking sustainability concerns into account. We believe that reducing all aspects of sustainable development to a single objective is not desirable. We thus reformulate single and multi-echelon economic order quantity models as multi-objective problems. These models are then used to study several options such as buyer-supplier coordination or green technology investment. We also consider that firms are becoming increasingly proactive with respect to sustainability. We thus propose to apply multiple criteria decision aid techniques instead of considering sustainability as a constraint. In this sense, the firm may provide preference information about economic, environmental and social tradeoffs and quickly identify a satisfactory solution.
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Garret, Aaron Dozier Gerry V. "Neural enhancement for multiobjective optimization." Auburn, Ala., 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Dissertation/Garrett_Aaron_55.pdf.
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Siraj, Sajid. "Preference elicitation from pairwise comparisons in multi-criteria decision making." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/preference-elicitation-from-pairwise-comparisons-in-multicriteria-decision-making(bf9c4efe-28b3-4e5b-807d-76df5b858aa5).html.
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Decision making is an essential activity for humans and often becomes complex in the presence of uncertainty or insufficient knowledge. This research aims at estimating preferences using pairwise comparisons. A decision maker uses pairwise comparison when he/she is unable to directly assign criteria weights or scores to the available options. The judgments provided in pairwise comparisons may not always be consistent for several reasons. Experimentation has been used to obtain statistical evidence related to the widely-used consistency measures. The results highlight the need to propose new consistency measures. Two new consistency measures - termed congruence and dissonance - are proposed to aid the decision maker in the process of elicitation. Inconsistencies in pairwise comparisons are of two types i.e. cardinal and ordinal. It is shown that both cardinal and ordinal consistency can be improved with the help of these two measures. A heuristic method is then devised to detect and remove intransitive judgments. The results suggest that the devised method is feasible for improving ordinal consistency and is computationally more efficient than the optimization-based methods. There exist situations when revision of judgments is not allowed and prioritization is required without attempting to remove inconsistency. A new prioritization method has been proposed using the graph-theoretic approach. Although the performance of the proposed prioritization method was found to be comparable to other approaches, it has practical limitation in terms of computation time. As a consequence, the problem of prioritization is explored as an optimization problem. A new method based on multi-objective optimization is formulated that offers multiple non-dominated solutions and outperforms all other relevant methods for inconsistent set of judgments. A priority estimation tool (PriEsT) has been developed that implements the proposed consistency measures and prioritization methods. In order to show the benefits of PriEsT, a case study involving Telecom infrastructure selection is presented.
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Lokman, Banu. "Approaches For Multi-objective Combinatorial Optimization Problems." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608443/index.pdf.
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In this thesis, we develop two exact algorithms and a heuristic procedure for Multiobjective
Combinatorial Optimization Problems (MOCO). Our exact algorithms
guarantee to generate all nondominated solutions of any MOCO problem. We test the
performance of the algorithms on randomly generated problems including the Multiobjective
Knapsack Problem, Multi-objective Shortest Path Problem and Multi-objective
Spanning Tree Problem. Although we showed the algorithms work much better than the
previous ones, we also proposed a fast heuristic method to approximate efficient frontier
since it will also be applicable for real-sized problems. Our heuristic approach is based
on fitting a surface to approximate the efficient frontier. We experiment our heuristic on
randomly generated problems to test how well the heuristic procedure approximates the
efficient frontier. Our results showed the heuristic method works well.
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El-Lahham, Christine. "Multiple-criteria optimization of a cold heading process using finite element analysis and a taguchi approach." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80009.
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The current work aims at modeling a three-stage cold heading process of an industrial bolt, and presents a methodology for the optimization of preform die geometries.The process is modeled using finite element simulations and potential defects in the heading process are analyzed using external and internal crack criteria. The preform geometries are then optimized with respect to external cracking in the blank and forming die load. The conventional Taguchi approach is first applied on each criterion separately. Three optimal solutions are generated. It is found that some parameters have conflicting optimal solutions.
The single criterion approach is therefore extended to multiple-criteria approaches by the use of overall evaluation criteria within the Taguchi method. Two methodologies are proposed, namely, the additive utility function method and the TOPSIS decision-making model. It is found that the performances of the two methods are comparable.
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Černevič, Anna. "Daugiakriterinių optimizavimo uždavinių sprendimo strategijų tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2004. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040611_155011-78398.
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Wang, Shuo. "Optimization Models for Network-Level Transportation Asset Preservation Strategies." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1416578565.
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Azevedo, Carlos Renato Belo 1984. "Anticipation in multiple criteria decision-making under uncertainty = Antecipação na tomada de decisão com múltiplos critérios sob incerteza." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260775.
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Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-26T06:49:07Z (GMT). No. of bitstreams: 1 Azevedo_CarlosRenatoBelo_D.pdf: 3449858 bytes, checksum: 7a1811aa772f1ae996e8851c60627b7c (MD5) Previous issue date: 2012
Resumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treino
Abstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolume
Doutorado
Engenharia de Computação
Doutor em Engenharia Elétrica
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Rohling, Gregory Allen. "Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4835.
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This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space.
Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive
complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual.
The original contributions of this thesis are:
1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description.
2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization.
3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals.
4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations.
5. Application of MOEAs to the computationally
expensive flare pattern design domain.
6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms.
7. Development of a new method of using MOEAs for
automatic design of pattern recognition systems.
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Ruderman, Alex Michael. "A framework for simulation-based multi-attribute optimum design with improved conjoint analysis." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31811.
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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010.Committee Chair: Choi, Seung-Kyum; Committee Member: Allen, Janet K.; Committee Member: Paredis, Chris. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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NAIK, AMIT R. "TRADEOFF ANALYSIS FOR HELICAL GEAR REDUCTION UNITS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1129591522.
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Bui, Lam Thu Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "The role of communication messages and explicit niching in distributed evolutionary multi-objective optimization." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2007. http://handle.unsw.edu.au/1959.4/38739.
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Dealing with optimization problems with more than one objective has been an important research area in evolutionary computation. The class of multi-objective problems (MOPs) is an important one because multi-objectivity exists in almost all aspects of human life; whereby there usually exist several compromises in each problem. Multi-objective evolutionary algorithms (MOEAs) have been applied widely in many real-world problems. This is because (1) they work with a population during the course of action, which hence offer more flexible control to find a set of efficient solutions, and (2) real-world problems are usually black-box where an explicit mathematical representation is unknown. However, MOEAs usually require a large amount of computational effort. This is a sub- stantial challenge in bringing MOEAs to practice. This thesis primarily aims to address this challenge through an investigation into issues of scalability and the balance between exploration and exploitation. These have been outstanding research challenges, not only for MOEAs, but also for evolutionary algorithms in general. A distributed framework of local models using explicit niching is introduced as an overarching umbrella to solve multi-objective optimization problems. This framework is used to address the two-part question about first, the role of communication messages and second, the role of explicit niching in distributed evolutionary multi-objective optimization. The concept behind the framework of local models is for the search to be conducted locally in different areas of the decision search space, which allows the local models to be distributed on different processing nodes. During the optimization process, local models interact (exchange messages) with each other using rules inspired from Particle Swarm Optimization (PSO). Hence, the hypothesis of this work is that running simultaneously several search engines in different local areas is better for exploiting local information, while exchanging messages among those diverse engines can provide a better exploration strategy. For this framework, as the models work locally, they gain access to some global knowledge of each other. In order to validate the proposed framework, a series of experiments on a wide range of test problems was conducted. These experiments were motivated by the following studies which in their totality contribute to the verification of our hypothesis: (1) studying the performance of the framework under different aspects such as initialization, convergence, diversity, scalability, and sensitivity to the framework's parameters, (2) investigating interleaving guidance in both the decision and objective spaces, (3) applying local models using estimation of distributions, (4) evaluating local models in noisy environments and (5) the role of communication messages and explicit niching in distributed computing. The experimental results showed that: (1) the use of local models increases the chance of MOEAs to improve their performance in finding the Pareto optimal front, (2) interaction strategies using PSO rules are suitable for controlling local models, and that they also can be coupled with specialization in order to refine the obtained non-dominated set, (3) estimation of distribution improves when coupled with local models, (4) local models work well in noisy environments, and (5) the communication cost in distributed systems with local models can be reduced significantly by using summary information (such as the direction information naturally determined by local models) as the communication messages, in comparison with conventional approaches using descriptive information of individuals. In summary, the proposed framework is a successful step towards efficient distributed MOEAs.
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Payan, Alexia Paule Marie-Renee. "Enabling methods for the design and optimization of detection architectures." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47688.
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The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.
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Häckel, Sascha. "Hybride Ansätze basierend auf Dynamic Programming und Ant Colony Optimization zur mehrkriteriellen Optimierung Kürzester-Wege-Probleme in gerichteten Graphen am Beispiel von Angebotsnetzen im Extended Value Chain Management." Master's thesis, Universitätsbibliothek Chemnitz, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-61086.
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In einer von Vernetzung und Globalisierung geprägten Umwelt wächst der Wettbewerbsdruck auf die Unternehmen am Markt stetig. Die effektive Nutzung der Ressourcen einerseits und die enge Zusammenarbeit mit Lieferanten und Kunden andererseits führen für nicht wenige Unternehmen des industriellen Sektors zu entscheidenden Wettbewerbsvorteilen, die das Fortbestehen jener Unternehmen am Markt sichern. Viele Unternehmen verstehen sich aus diesem Grund als Bestandteil so genannter Supply Chains. Die unternehmensübergreifende Steuerung und Optimierung des Wertschöpfungsprozesses stellt ein charakteristisches Problem des Supply Chain Managements dar und besitzt zur Erzielung von Wettbewerbsvorteilen hohes Potential. Produktionsnetzwerke sind ein wesentlicher Forschungsschwerpunkt der Professur für Produktionswirtschaft und Industriebetriebslehre an der TU Chemnitz. Das Extended Value Chain Management (EVCM) stellt ein kompetenzorientiertes Konzept für die Bildung und zum Betrieb hierarchieloser temporärer regionaler Produktionsnetzwerke im Sinne virtueller Unternehmen dar. Gegenstand dieser Arbeit ist ein diskretes Optimierungsproblem, dass einen mehrstufigen Entscheidungsprozesses unter Berücksichtigung mehrerer Ziele abbildet, der sich bei der Auswahl möglicher Partner in einem Produktionsnetzwerk nach dem Betreiberkonzept des EVCM ergibt. Da mehrere Zielstellungen bestehen, werden grundlegende Methoden der mehrkriteriellen Optimierung und Entscheidung erörtert. Neben der Vorstellung des Problems sollen mehrzielorientierte Ansätze im Sinne einer Pareto-Optimierung auf Basis des Dynamic Programmings als Verfahren zur Bestimmung von Optimallösungen sowie Ant Colony Optimization zur näherungsweisen Lösung vorgestellt werden. Darauf aufbauend werden verschiedene Möglichkeiten der Hybridisierung beider Methoden diskutiert. Die entwickelten Ansätze werden auf ihre Eignung im Rahmen der informationstechnischen Umsetzung des EVCM-Konzepts untersucht und einer Evaluierung unterzogen. Hierzu werden verschiedene Kennzahlen zur Beurteilung der Verfahren entwickelt. Die modellierten Algorithmen und entwickelten Konzepte beschränken sich nicht ausschließlich auf das betrachtete Problem, sondern können leicht auf Probleme mit ähnlichen Eigenschaften übertragen werden. Insbesondere das NP-vollständige mehrkriterielle Kürzeste-Wege-Problem stellt einen Spezialfall des behandelten Optimierungsproblems dar.
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Daskilewicz, Matthew John. "Methods for parameterizing and exploring Pareto frontiers using barycentric coordinates." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47658.
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The research objective of this dissertation is to create and demonstrate methods for parameterizing the Pareto frontiers of continuous multi-attribute design problems using barycentric coordinates, and in doing so, to enable intuitive exploration of optimal trade spaces. This work is enabled by two observations about Pareto frontiers that have not been previously addressed in the engineering design literature. First, the observation that the mapping between non-dominated designs and Pareto efficient response vectors is a bijection almost everywhere suggests that points on the Pareto frontier can be inverted to find their corresponding design variable vectors. Second, the observation that certain common classes of Pareto frontiers are topologically equivalent to simplices suggests that a barycentric coordinate system will be more useful for parameterizing the frontier than the Cartesian coordinate systems typically used to parameterize the design and objective spaces.
By defining such a coordinate system, the design problem may be reformulated from y = f(x) to (y,x) = g(p) where x is a vector of design variables, y is a vector of attributes and p is a vector of barycentric coordinates. Exploration of the design problem using p as the independent variables has the following desirable properties: 1) Every vector p corresponds to a particular Pareto efficient design, and every Pareto efficient design corresponds to a particular vector p. 2) The number of p-coordinates is equal to the number of attributes regardless of the number of design variables. 3) Each attribute y_i has a corresponding coordinate p_i such that increasing the value of p_i corresponds to a motion along the Pareto frontier that improves y_i monotonically.
The primary contribution of this work is the development of three methods for forming a barycentric coordinate system on the Pareto frontier, two of which are entirely original. The first method, named "non-domination level coordinates," constructs a coordinate system based on the (k-1)-attribute non-domination levels of a discretely sampled Pareto frontier. The second method is based on a modification to an existing "normal boundary intersection" multi-objective optimizer that adaptively redistributes its search basepoints in order to sample from the entire frontier uniformly. The weights associated with each basepoint can then serve as a coordinate system on the frontier. The third method, named "Pareto simplex self-organizing maps" uses a modified a self-organizing map training algorithm with a barycentric-grid node topology to iteratively conform a coordinate grid to the sampled Pareto frontier.
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Mynařík, Petr. "Finanční optimalizace." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-10536.
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The thesis describes a way of searching a better financial opportunities. The first part is about the multiple criteria decision making. I focus on application methods of multicriterial evaluation of alternatives on different possibilities of solving retirement. The target is to compare different possibilities and then suggest a solution. The second part is about the linear programming. The main objective of the diploma thesis is to suggest a create mathematical model, which I will use in my profession. This mathematical model will display results, which tell us how we can solve the question of money and finance.
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Miranda, Ackerman Marco Augusto. "Multi-objective optimization for Green Supply Chain Management and Design : Application to the orange juice agrofood cluster." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/15550/1/Miranda_Ackerman.pdf.
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Supply chain and operations management has matured from a field that addressed only operational and economic concerns to one that comprehensively considers the broader environmental and social issues that face industrial organizations of today. Adding the term “green” to supply chain activities seeks to incorporate environmentally conscious thinking in all processes in the supply chain. The aim of this work is to develop a Green Supply Chain (GrSC) framework based on a multi-objective optimization approach, with specific emphasis on agrofood supply chain design, planning and operations through the implementation of appropriate green supply chain management and logistics principles. The case study is the orange juice cluster. The research objective is the minimization of the environmental burden and the maximization of economic profitability of the selected product categories. This work focuses on the application of GrSCM to two fundamental strategic issues targeting agro food supply chains. The former is related to the Green Supplier Selection (GSS) problem devoted to the farming production systems and the way they are integrated into the global supply chain network. The latter focuses on the global Green Supply Chain Network Design (GSCND) as a whole. These two complementary and ultimately integrated strategic topics are framed in order to evaluate and exploit the unique characteristics of agro food supply chains in relation to eco-labeling. The methodology is based on the use of Life Cycle Assessment, Multi-objective Optimization via Genetic Algorithms and Multiple-criteria Decision Making tools (TOPSIS type). The approach is illustrated and validated through the development and analysis of an Orange Juice Supply Chain case study modelled as a three echelon GrSC composed of the supplier, manufacturing and market levels that in turn are decomposed into more detailed subcomponents. Methodologically, the work has shown the development of the modelling and optimization GrSCM framework is useful in the context of eco-labeled agro food supply chain and feasible in particular for the orange juice cluster. The proposed framework can help decision makers handle the complexity that characterizes agro food supply chain design decision and that is brought on by the multi-objective and multi-period nature of the problem as well as by the multiple stakeholders, thus preventing to make the decision in a segmented empirical manner. Experimentally, under the assumptions used in the case study, the work highlights that by focusing only on the “organic” eco-label to improve the agricultural aspect, low to no improvement on overall supply chain environmental performance is reached in relative terms. In contrast, the environmental criteria resulting from a full lifecycle approach is a better option for future public and private policies to reach more sustainable agro food supply chains.
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Filatovas, Ernestas. "Daugiakriterinių optimizavimo uždavinių sprendimas interaktyviuoju būdu." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120402_093941-01357.
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Praktikoje dažnai tenka spręsti sudėtingus daugiakriterinius optimizavimo uždavinius, kai kriterijai būna prieštaringi, o galutinis apsisprendimas priklauso nuo sprendimų priėmėjo. Kai sprendimų priėmėjas dalyvauja sprendimo procese interaktyviai, tai jis gali koreguoti prioritetus ir siekiamus tikslus uždavinio sprendimo eigoje, kas įgalina spęsti uždavinius, turinčius daug kriterijų ir apribojimų. Be to, sprendimo priėmėjui svarbu gauti sprendinius iš visos Pareto aibės. Interaktyviam uždavinių sprendimui būtina sprendimų paramos sistema, kurios grafinė sąsaja yra pritaikyta sprendžiamam uždaviniui. Šio darbo tyrimų sritis yra interaktyvus daugiakriterinių optimizavimo uždavinių sprendimas bei sprendimų paramos sistemos.
Disertacijoje nagrinėjant daugiakriterinio optimizavimo metodus, didesnis dėmesys skirtas metodams, užtikrinantiems gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje bei interaktyviems metodams. Pasiūlytas ir ištirtas daugiakriterinių optimizavimo uždavinių sprendimo būdas, leidžiantis spręsti daugiakriterinius optimizavimo uždavinius interaktyviai ir užtikrinantis gaunamų sprendinių tolygų pasiskirstymą Pareto aibėje. Sukurta ir ištirta interaktyvi daugiakriterinių optimizavimo uždavinių sprendimų paramos sistemą, apjungianti pasiūlytą optimizavimo uždavinių sprendimo būdą, sprendimo proceso vizualizavimą ir jo lygiagretinimą. Taip pat pasiūlyta sprendimo strategija, pagal kurią sprendžiant daugiakriterinį optimizavimo uždavinį pasitelkiamas... [toliau žr. visą tekstą]In practice, optimization problems are often multiple criteria. The criteria are usually contradictory, so the final decision depends on a decision maker. When the problem is solved interactively, the decision maker can change his/her preferences in decision process. Moreover, it is important to obtain solutions from the whole Pareto front. A decision support system adapted to the specific of the problem is essential for solving multiple criteria optimization problems interactively. The objects of research are multiple criteria optimization problems, interactive methods for solving these problems, interactive decision support systems, and application of parallel computing in decision support systems. Multiple criteria optimization methods are analyzed in the dissertation. The focus of attention is the methods for a uniform distribution of solutions on the Pareto front as well as the interactive methods. An interactive way for solving multicriteria optimization problems, which finds alternative solutions uniformly distributed on the Pareto front is proposed and investigated in this dissertation. An interactive decision support system which integrates the created interactive solving way, the decision process visualization and parallelization for multiple criteria optimization is developed. The solving strategies, when a multiple criteria optimization problem is solved interactively, using a computer cluster are developed and compared experimentally. The time required for a... [to full text]
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Almeida, Carolina Paula de. "Transgenética computacional aplicada a problemas de otimização combinatória com múltiplos objetivos." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/510.
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CNPqA Transgenética Computacional é uma metáfora para o desenvolvimento de algoritmos evolucionários com base na teoria de evolução endossimbiótica e em outras interações do fluxo intracelular. Diversos algoritmos foram desenvolvidos com base nesta metáfora para problemas de Otimização Combinatória, em sua maioria com um único objetivo, obtendo bons resultados. Uma vez que a consideração de mais de um objetivo leva, em geral, a representações mais realistas de problemas práticos complexos, neste trabalho investiga-se o desenvolvimento de Algoritmos Transgenéticos para problemas multiobjetivo. Tais algoritmos são examinados em versões que utilizam elementos de outros algoritmos evolucionários multiobjetivo sendo eles o NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) e o MOEA/D (Multi-objective Evolutionary Algorithm based on Decomposition). Diante disso, este trabalho propõe duas novas metodologias utilizando a Transgenética Computacional acoplada ao NSGA-II e ao MOEA/D, denominadas NSTA (Non-Dominated Sorting Transgenetic Algorithm) e MOTA/D (Multi-objective Transgenetic Algorithm based on Decomposition), respectivamente. Para avaliar o desempenho das técnicas propostas, os algoritmos desenvolvidos foram aplicados a dois problemas de Otimização Combinatória, NP-difíceis,em versões com mais de um objetivo. O primeiro problema é o Caixeiro Comprador Biobjetivo e o segundo o Quadrático de Alocação multiobjetivo. Foram realizados experimentos com casos de teste disponíveis em bancos utilizados comumente por outros trabalhos da literatura. Os resultados dos algoritmos propostos foram comparados com os resultados obtidos com os algoritmos evolucionários multiobjetivo que os inspiraram. A análise dos dados obtidos com os experimentos computacionais mostram que a versão MOTA/D é a mais eficiente dentre os algoritmos do experimento com relação a qualidade da aproximação da fronteira de Pareto.
The Computational Transgenetic is a metaphor for the development of evolutionary algorithms based on the theory of evolution endosymbiotic and other intracellular interactions flow. Several algorithms have been developed based on this metaphor for combinatorial optimization problems, mostly with a single objective, obtaining good results. Once the account of more than one objective provides, in general, more realistic representations of complex practical problems, this work investigates the development of Transgenetic Algorithms for multiobjective problems. Such algorithms are examined in versions that use elements of other multiobjective evolutionary algorithms such as the NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) and the MOEA/D (Multi-objective Evolutionary Algorithm based on Decomposition). Therefore, this work proposes two new methods using Computational Transgenetic attached to NSGA-II and MOEA/D, named NSTA (Non-Dominated Sorting Transgenetic Algorithm) and MOTA/D (Multi-objective Transgenetic Algorithm based on Decomposition), respectively. To evaluate the proposed techniques performance, the experiments consider two NP-hard combinatorial optimization problems, in versions with more than one objective. The first problem is the Traveling Purchaser Problem and the second the Quadratic Assignment Problem. Experiments were performed with test cases available in benchmarks commonly used by other studies in the literature. The proposed algorithms' results were compared with those obtained by the multiobjetive evolutionary algorithms that inspired them. The analysis of data obtained by the computational experiment shows that the version MOTA/D is among the most efficient algorithms of the experiment with respect to the quality of the Pareto front approximation.
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Perez, Gallardo Jorge Raúl. "Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10505/1/perez_gallardo_partie_1_sur_2.pdf.
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Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.
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Song, Qiang. "Non-euler-lagrangian pareto-optimality conditions for dynamic multiple-criterion decision problems." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/24920.
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Krčil, Jakub. "Moderní metody a nástroje pro podporu manažerského rozhodování." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-194192.
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This master's thesis is focused on modern methods and tools to support managerial decision-making. The first part of this thesis introduces the basic characteristics related to the management and managerial decision-making that are subsequently extended to the area of modeling, simulation, optimization and multi-criteria decision making. It also outlines the relationship between the managerial decision-making tasks. The second part introduces practical examples which show the connection of these areas. Specifically, they are a colony of ants, traveling salesman problem, a tool AnyLogic, analytic hierarchy process and simulation HealthBound. The thesis is further supplemented by an appropriate software tools to support multi-criteria decision making.
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Ait, Haddadene Syrine Roufaida. "Modèles et méthodes pour la gestion logistique optimisée dans le domaine des services et de la santé." Thesis, Troyes, 2016. http://www.theses.fr/2016TROY0027/document.
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Cette thèse aborde le problème de tournées de véhicules (VRP) intégrant des contraintes temporelles : fenêtres de temps (TW), synchronisation (S) et précédence (P), appliqué au secteur de soins à domicile, donnant le VRPTW-SP. Il s’agit d’établir un plan de visite journalier des soignants, aux domiciles des patients ayant besoin d’un ou plusieurs services. Tout d’abord, nous avons abordé ce problème sous angle mono-objectif. Ensuite, le cas bi-objectif est considéré. Pour la version mono-objectif, un Programme Linéaire à Variables Mixtes Entières (PLME), deux heuristiques constructives, deux procédures de recherches locales et trois métaheuristiques à base de voisinages sont proposés : une procédure de recherche constructive adaptative randomisée (GRASP), une recherche locale itérée (ILS) et une approche hybride (GRASP × ILS). Concernant le cas bi-objectif, différentes versions de métaheuristiques évolutionnaires multi-objectifs sont proposées, intégrant différentes recherches locales : l’algorithme génétique avec tri par non-dominance version 2 (NSGAII), une version généralisée de ce dernier avec démarrages multiples (MS-NSGAII) et une recherche locale itérée avec tri par non-dominance (NSILS). Ces algorithmes ont été testés et validés sur des instances adaptées de la littérature. Enfin, nous avons étendu le VRPTW-SP sur un horizon de planification, donnant le VRPTW-SP multi-période. Pour résoudre cette extension, un PLME ainsi qu’une matheuristique sont proposésThis work addresses the vehicle routing problem (VRP) including timing constraints: time windows (TW), synchronization (S) and precedence (P), applied in Home Health Care sector; giving the VRPTW-SP. This problem consists in establishing a daily caregivers planning to patients' homes asking for one or several services. We have started by considering the problem as a single objective case. Then, a bi-objective version of the problem is introduced. For solving the single-objective problem, a Mixed Integer Linear Program (MILP), two constructive heuristics, local search procedures and three local search based metaheuristics are proposed : a Greedy Randomized Adaptive Search procedure (GRASP), an Iterated Local Search (ILS) and a hybrid approach (GRASP × ILS). Regarding the bi-objective VRPTW-SP, different versions of multi-objective evolutionary algorithm, including various local research strategies are proposed: the Non-dominated Sorting Genetic Algorithm version 2 (NSGAII), a generalized version of this latter with multiple restarts (MS-NSGAII) and an Iterated Local Search combined with the Non-dominated Sorting concept (NSILS). All these algorithms have been tested and validated on appropriate instances adapted from the literature. Finally, we extended the VRPTW-SP on a multi-period planning horizon and then proposed a MILP and a matheuristic approach
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Pavelski, Lucas Marcondes. "Otimização evolutiva multiobjetivo baseada em decomposição e assistida por máquinas de aprendizado extremo." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1254.
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Muitos problemas de otimização reais apresentam mais de uma função-objetivo. Quando os objetivos são conflitantes, estratégias especializadas são necessárias, como é o caso dos algoritmos evolutivos multiobjetivo (MOEAs, do inglês Multi-objective Optimization Evolutionary Algorithms). Entretanto, se a avaliação das funções-objetivo é custosa (alto custo computacional ou econômico) muitos MOEAs propostos são impraticáveis. Uma alternativa pode ser a utilização de um modelo de aprendizado de máquina que aproxima o cálculo do fitness (surrogate) no algoritmo de otimização. Este trabalho propõe e investiga uma plataforma chamada ELMOEA/D que agrega MOEAs do estado da arte baseados em decomposição de objetivos (MOEA/D) e máquinas de aprendizado extremo (ELMs, do inglês Extreme Learning Machines) como modelos surrogate. A plataforma proposta é testada com diferentes variantes do algoritmo MOEA/D e apresenta bons resultados em problemas benchmark, comparada a um algoritmo da literatura que também utiliza MOEA/D mas modelos surrogates baseados em redes com função de base radial. A plataforma ELMOEA/D também é testada no Problema de Predição de Estrutura de Proteínas (PPEP). Apesar dos resultados alcançados pela proposta não serem tão animadores quanto aqueles obtidos nos benchmarks (quando comparados os algoritmos com e sem surrogates), diversos aspectos da proposta e do problema são explorados. Por fim, a plataforma ELMOEA/D é aplicada a uma formulação alternativa do PPEP com sete objetivos e, com estes resultados, várias direções para trabalhos futuros são apontadas.Many real optimization problems have more than one objective function. When the objectives are in conflict, there is a need for specialized strategies, as is the case of the Multi-objective Optimization Evolutionary Algorithms (MOEAs). However, if the functions evaluation is expensive (high computational or economical costs) many proposed MOEAs are impractical. An alternative might be the use of a machine learning model to approximate the fitness function (surrogates) in the optimization algorithm. This work proposes and investigates a framework called ELMOEA/D that aggregates state-of-the-art MOEAs based on decomposition of objectives (MOEA/D) and extreme learning machines as surrogate models. The proposed framework is tested with different MOEA/D variants and show good results in benchmark problems, compared to a literature algorithm that also encompasses MOEA/D but uses surrogate models based on radial basis function networks. The ELMOEA/D framework is also applied to the protein structure prediction problem (PSPP). Despite the fact that the results achieved by the proposed approach were not as encouraging as the ones achieved in the benchmarks (when the algorithms with and without surrogates are compared), many aspects of both algorithm and problem are explored. Finally, the ELMOEA/D framework is applied to an alternative formulation of the PSPP and the results lead to various directions for future works.
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Lamboia, Fabiany. "Modelo de otimização multiobjetivo baseado em algoritmo Shuffled Frog Leaping para transporte de produtos em redes de dutos." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/2029.
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ANP; FINEP; MCTA modelagem de sistemas envolvidos no gerenciamento das operações de uma rede de dutos é um problema de otimização que envolve complexas restrições operacionais. O transporte por meio de dutos mostra-se confiável e econômico, principalmente para grandes volumes. Porém, a elevada taxa de ocupação das redes de distribuição e a quantidade de diferentes produtos que devem ser transportados sob condições operacionais diferenciadas levam a cenários operacionais complexos. Uma melhoria na eficiência do transporte de produtos através de redes de dutos pode ser obtida por uma melhor alocação dos recursos disponíveis, contudo além de ser este um problema combinatório de difícil solução, é também um problema de otimização multiobjetivo. Para resolver este tipo de problema, as técnicas baseadas em metaheurísticas populacionais, em especial os algoritmos evolucionários parecem adequados pois tratam simultaneamente com um conjunto de soluções possíveis que permite encontrar um conjunto de soluções ótimas de Pareto com a simples execução do algoritmo. Neste contexto, este trabalho tem como objetivo o desenvolvimento de modelos de otimização multiobjetivo aplicados ao escalonamento de operações em rede de dutos existente na indústria P & G, investigando técnicas baseadas em metaheurísticas que auxiliem na tomada de decisões deste cenário específico, em especial, técnicas baseadas em algoritmos evolucionários multiobjetivos. Assim, usa-se uma abordagem que propõe o uso de um algoritmo evolucionário multiobjetivo inspirado a partir da evolução memética de um grupo de sapos que procuram por comida: o SFLA (Shuffled Frog Leaping Algorithm). Os resultados obtidos a partir das simulações realizadas serão comparados com um algoritmo muito conhecido e usado na literatura, o algoritmo genético (AG). Além disso, como este trabalho utiliza um modelo de otimização multiobjetivo e nestes casos procura-se um conjunto de soluções Pareto-ótimas, uma nova abordagem é proposta para o algoritmo SFLA: o Modified Shuffled Frog-leaping Pareto Approach (MSFLPA). Esta nova abordagem combina o uso de uma pequena população e uma estratégia de arquivamento com um processo de reinicialização da população usando duas memórias auxiliares para armazenar soluções não-dominadas~(Conjunto de Pareto) encontradas durante a evolução da população. Para validar o desempenho e a eficiência do algoritmo MSFLPA proposto, cinco funções Zitzler-Deb-Thiele são utilizadas para comparar com dois algoritmos genéticos multi-objetivos bem conhecidos da literatura: NSGA-II e SPEA2. Os experimentos numéricos indicam que MSFLPA produz soluções bem espalhadas~(diversidade) e converge para a verdadeira fronteira de Pareto e verifica-se ser eficiente e competitivo para resolver problemas multiobjetivos. Após essa validação, o MSFLPA é usado para otimizar a alocação dos recursos e para resolver o problema de programação de uma rede de dutos e quando comparado com o NSGA-II e microAG, MSFLPA tem se mostrado uma nova alternativa eficaz para a solução de problemas multiobjetivos com mais de dois objetivos, como é o caso dos problemas de escalonamento de redes de dutos.
The development of model to support pipeline network operation management is an optimization problem which involves complex operational constraints. The product transport through pipelines proves reliable and economical, especially for large volumes. However, the high occupancy rate of the distribution networks and the amount of different products should be transported under different operating conditions lead to complex operational scenarios. An efficiency improvement of products transport through pipeline networks can be obtained by a better allocation of available resources. However that is a hard solution combinatorial problem with multiobjective optimization characteristics. An alternative to efficient solve this type of problem is the use of metaheuristics such Multiobjective Evolutionary Algorithms~(MOEA). MOEA uses a population of solutions in its search, and multiple Pareto-optimal solutions can, in principle, be found in one single run. This work aims to develop a model of multi-criterion optimization applied to scheduling operations in a real-world pipeline network in the oil industry. We use a metaheuristic optimization method inspired from the memetic evolution of a group of frogs when seeking for food: SFLA~(Shuffled Frog Leaping Algorithm). The results obtained from the simulations are compared to an algorithm well known in the literature: genetic algorithm~(GA). Moreover, this works then introduces a new approach of the original shuffled frog leaping algorithm to create a modified form of the algorithm: the Modified Shuffled frog-leaping Pareto Approach~(MSFLPA). The main goal of MSFLPA is to represent and recover the entire Pareto front to a modeled problem, moreover an efficient and competitive algorithm to solve multi-objective scheduling problems with more than two conflicting objectives. This new approach combines the use of a small population and an archiving strategy with a procedure to restart the population using two auxiliary memories to store nondominated solutions (Pareto set) found during population evolution. To validate the performance and efficiency of the proposed MSFLPA in spread Pareto front, five Zitzler-Deb-Thiele functions are examined and compared against two well-known multi-objective genetic algorithms: NSGA-II and SPEA2. The numerical experiments indicate that MSFLPA yields spread solutions and converges to the true Pareto front and it is verified to be efficient and competitive for solving multi-objective problem. After this validation, the MSFLPA is used to optimize the allocation of the resources and to solve the scheduling problem of a real world pipeline network and if compared with NSGA-II and microGA, MSFLPA is verified to be a new effective alternative for solving of multi-objective problems with more than two objectives as it is the case of the pipeline scheduling problems.
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Morales, Mendoza Luis Fernando. "Écoconception de procédés : approche systémique couplant modélisation globale, analyse du cycle de vie et optimisation multiobjectif." Thesis, Toulouse, INPT, 2013. http://www.theses.fr/2013INPT0106/document.
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L’objectif de ce travail est de développer un cadre méthodologique et générique d’éco-conception de procédés chimiques couplant des outils de modélisation et de simulation traditionnels de procédés (HYSYS, COCO, ProSimPlus et Ariane), d’Analyse du Cycle de Vie (ACV), d’optimisation multiobjectif basée sur des Algorithmes Génétiques et enfin des outils d’aide à la décision multicritère (ELECTRE, PROMETHEE, M-TOPSIS). Il s’agit de généraliser, d’automatiser et d’optimiser l’évaluation des impacts environnementaux au stade préliminaire de la conception d’un procédé chimique. L’approche comprend trois étapes principales. Les deux premières correspondent d’une part aux phases d’analyse de l’inventaire par calcul des bilans de matière et d’énergie et d’autre part à l’évaluation environnementale par ACV. Le problème du manque d’information ou de l’imprécision dans les bases de données classiques en ACV pour la production d’énergie notamment sous forme de vapeur largement utilisée dans les procédés a reçu une attention particulière. Une solution proposée consiste à utiliser un simulateur de procédés de production d’utilités (Ariane, ProSim SA) pour contribuer à alimenter la base de données environnementale en tenant compte de variations sur les conditions opératoires ou sur les technologies utilisées. Des sous-modules « énergie » sont ainsi proposés pour calculer les émissions relatives aux impacts liés à l’utilisation de l’énergie dans les procédés. La troisième étape réalise l’interaction entre les deux premières phases et l’optimisation multi-objectif qui met en jeu des critères économiques et environnementaux. Elle conduit à des solutions de compromis le long du front de Pareto à partir desquelles les meilleures sont choisies à l’aide de méthodes d’aide à la décision. L’approche est appliquée à des procédés de production continus : production de benzène par hydrodéalkylation du toluène HDA et production de biodiesel à partir d’huiles végétales. Une stratégie à plusieurs niveaux est mise en oeuvre pour l'analyse de l'optimisation multi-objectif. Elle est utilisée dans les deux cas d'étude afin d'analyser les comportements antagonistes des critèresThe objective of this work is to propose an integrated and generic framework for eco-design coupling traditional modelling and flowsheeting simulation tools (HYSYS, COCO, ProSimPlus and Ariane), Life Cycle Assessment, multi-objective optimization based on Genetic Algorithms and multiple criteria decision-making methods MCDM (Multiple Choice Decision Making, such as ELECTRE, PROMETHEE, M-TOPSIS) that generalizes, automates and optimizes the evaluation of the environmental criteria at earlier design stage. The approach consists of three main stages. The first two steps correspond respectively to process inventory analysis based on mass and energy balances and impact assessment phases of LCA methodology. Specific attention is paid to the main issues that can be encountered with database and impact assessment i.e. incomplete or missing information, or approximate information that does not match exactly the real situation that may introduce a bias in the environmental impact estimation. A process simulation tool dedicated to production utilities, Ariane, ProSim SA is used to fill environmental database gap, by the design of specific energy sub modules, so that the life cycle energy related emissions for any given process can be computed. The third stage of the methodology is based on the interaction of the previous steps with process simulation for environmental impact assessment and cost estimation through a computational framework. The use of multi-objective optimization methods generally leads to a set of efficient solutions, the so-called Pareto front. The next step consists in identifying the best ones through MCDM methods. The approach is applied to two processes operating in continuous mode. The capabilities of the methodology are highlighted through these case studies (benzene production by HDA process and biodiesel production from vegetable oils). A multi-level assessment for multi-objective optimization is implemented for both cases, the explored pathways depending on the analysis and antagonist behaviour of the criteria
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Hsiao, Chi-Che, and 蕭祺哲. "Fuzzy Multiple Criteria Optimization for Redundancy Allocation." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/49707362836044825442.
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碩士國立交通大學
工業工程研究所
83
It is well known that redundancy is one of the effective methods to increase the reliability of system. However, cost, weight and volumn of system are also the important criteria for the redundancy allocation problem. In this study,we provide a fuzzy multiple criteria mathematical model for the redundancy allocation problem. The reliability and cost are represent as fuzzy numbers. This redundancy allocation mathematical model is nonlinear and integer programming. We also provide an interactive approach to solve the model. Finally,a numerical example is given to illustrate the approach.
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Hung, J. C., and 洪榮志. "An Efficient Approach for Multiple Criteria Redundancy Optimization Problems." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/57058752353379836075.
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碩士國立交通大學
工業工程研究所
83
Due to the increasing complexity of modern engineering systems, the concept of reliability has become a very important factor in the overall system design. Redundancy allocation is a design to improve reliability for systems. In many practical situations, redundancy allocation is complicated because of mutually conflicting goals. Besides, the decision variables are all integer values and the objective functions are nonlinear; that is, the problem belongs to the class of the NP-hard problems. The volume of computation required for an optimal solution increasing exponentially with the number of decision variables and constraints. In this study, we propose a model for multiple criteric redundancy allocation model with maximum reliability, minimum cost and weight. The method is based on branch & bound by considering the monotinicity in the objective functions and in the constraints to solve efficiently all feasible solutions, and solutions, and then we use the concept of Pareto optimal solutions and TOPSIS technique to solve the final compromise solution.
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yang, chung-huei, and 楊崇揮. "studies on multiple criteria optimization model of construction process." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/19279702063250475892.
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碩士國立台灣工業技術學院
營建工程技術研究所
85
Studies on Multiple Criteria Optimization Model of Construction ProcessesThesis Advisor: Sou-Sen LeuGraduate student: Chung-Huei YangABSTRACT The resources of activities are limited in the real construction world. To avoid the waste and shortage of resource, Scheduling must include resource allocation. Because of the limitation of resources, the orders of activities will change except the predecessor and successor relationships. The levels of resources reflect the cost and duration of activities. When we combine the schedule and resources, the scheduling problem will locate at the scope of resource allocation problem. To build an optimization model of bridge construction process, we construct it by genetic algorithms. The model adapting in any resource allocation problem must be economic, efficient and flexible. The model integrates time/ cost trade-off model, resource-limited model and resource leveling model. The nondominated solution is found by the MCDM method, TOPSIS.To find a optimal schedule solution under the minimization of completion time, the minimization of cost and the leveling of resources is the final goal of the research.
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Tarun, Prashant. "A dynamic multiple stage, multiple objective optimization model with an application to a wastewater treatment system." 2008. http://hdl.handle.net/10106/916.
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Yu-Lun, Su, and 蘇于倫. "Application of Multiple Criteria Decision Making on Number Toys Design and Evaluation Optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/95805662235246685749.
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碩士樹德科技大學
應用設計研究所
96
This research takes “the number toy” as an example and through the design and objective alternatives to create trendy number toys. First of all, this study tracks back the development of own branding and manufacturing and the design of number toys. Through quality function deployment, the research has multi level analysis on customers’ needs for products and then transfers it to the reference of the further design and characteristics of number toys. Furthermore, the series number toys design will develop with accurate design skills, creativities and factors of numbers and fashion styles. To find out if the result of the number toy projects meets the market acceptance, this research has three evaluations of multiple criteria decision making: TOPSIS, grey relation analysis and fuzzy synthetic decision. The differences of the three evaluations are discussed and AHP is used to calculate attribute value. The result indicates four points as following. 1. Orders and attribute value of customers’ needs are “unique style 9.37%”, “dust proof material 9.25%” and “style series 8.28%”; design parameter orders are “ exterior design 9.87%”, “series design 8.28%” and “institution design 7.92%”. 2. The best five designs chosen from thirteen drafts are the Lego fairy tale stationary set, the invisible number stress release stationary, the number circle stress release cup set, the tumbler business card clip and the number robot calendar. 3. Use qualitative research of grounded theory to consider market sales and build consumer-oriented system for multiple attribute decision making evaluation. The result of this research has seven perspectives and thirty five evaluation factors; the first three aspects are the whole creativity, beauty and economy. 4. The calculation outcome of these three evaluations doesn’t vary much on the entire order but for the calculation content, each has different difficulty. The research indicates that mode of fuzzy summarizing assessment is more suitable for the entire consideration factors and outcome. For product development, this mode is better to be used for pre-evaluation to know the outcome of each factor. Grey relation analysis is good for general evaluation when there are plenty of factors. TOPSIS can find out the positive and negative ideal solutions of evaluation criteria and get the distance of each project from both solutions to list out the priority orders. This research comes up with the toy design process and selection system and it focuses on consumers’ needs to develop creative design skills, and some number toy products are made for market strategy analysis and project selection. The process will be evaluated on practicality, objectivity and convenience, and expected to truly represent number toy designs. It will also help the designers understand questions, think of new projects, try to evaluate them and choose the best with limited time and resources. Hope this study will provide toy design industry with the evaluation system for new product development and raise companies’ competitiveness and brand value. Key words: number, toy design, Grounded theory, Quality Function Deployment, Multiple Criteria Decision Making
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Juan, Teng-Kuei, and 阮騰逵. "Minimum Manhattan Distance Approach to Multiple Criteria Decision Making in Multiobjective Optimization Problems." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/877hp4.
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碩士元智大學
電機工程學系
105
A minimum Manhattan distance (MMD) approach to multiple criteria decision making in multiobjective optimization problems (MOPs) is proposed. The approach selects the finial solution corresponding with a vector that has the MMD from a normalized ideal vector. This procedure is equivalent to the knee selection described by a divide and conquer approach that involves iterations of pairwise comparisons. Being able to systematically assign weighting coefficients to multiple criteria, the MMD approach is equivalent to a weighted-sum approach. Because of the equivalence, the MMD approach possesses rich geometric interpretations that are considered essential in the field of evolutionary computation. The MMD approach is elegant because all evaluations can be performed by efficient matrix calculations without iterations of comparisons. While the weighted-sum approach may encounter an indeterminate situation in which a few solutions yield almost the same weighted sum, the MMD approach is able to determine the final solution discriminately. Since existing multiobjective evolutionary algorithms aim for a posteriori decision making, i.e., determining the final solution after a set of Pareto optimal solutions is available, the proposed MMD approach can be combined with them to form a powerful solution method of solving MOPs. Furthermore, the approach enables scalable definitions of the knee and knee solutions.
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Qi, Yue. "Nondominated sets and surfaces in multiple criteria optimization and portfolio selection theory in finance." 2004. http://purl.galileo.usg.edu/uga%5Fetd/qi%5Fyue%5F200412%5Fphd.
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Tsuei, Hung-Jia, and 崔紘嘉. "An Investigation of Search Engine Optimization Based on Hybrid Modified Multiple Criteria Decision Making Models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/24ez5j.
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博士國立臺北科技大學
電子工程系
107
Search engine optimization (SEO) has been considered one of the most important techniques in internet marketing. This study establishes a decision model of search engine ranking for administrators to improve the performances of websites that satisfy users’ needs. To probe into the interrelationship and influential weights among criteria of SEO and evaluate the gaps of performance to achieve the aspiration level in real world, this research utilizes hybrid modified multiple criteria decision-making (MCDM) models, including decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based analytic network process (called DANP), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The empirical findings discover that the criteria of SEO possessed a self-effect relationship based on DEMATEL technique. External website optimization is the top priority dimension that needs to be improved when implementing SEO. Among the six criteria for evaluation, meta tags is the most significant criterion influencing search engine ranking, followed by keywords and website design.
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Chen, Ming-Hsien, and 陳明賢. "Induction of Multiple Criteria Classification Rules from Optimization Perspectives — Applied in Biology and Medicine Informatics." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/32a278.
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博士國立交通大學
資訊管理研究所
96
To induce critical classification rules from observed data is a major task in biological and medical research. A classification rule is considered to be useful if it is optimal and simultaneously satisfies three criteria: is highly accurate, has a high rate of support, and is highly compact. However, existed classification methods, such as rough set theory, neural networks, ID3, etc., may only induce feasible rules instead of optimal rules. In addition, the rules found by existed methods may only satisfy one of the three criteria. This study proposes a multi-criteria model to induce optimal classification rules with better rates of accuracy, support and compactness. A linear multiobjective programming model for inducing classification rules is formulated. Two practical data sets, one of HSV patients results and another of European barn swallows, are tested. The results illustrate that the proposed method can induce better rules than existed methods.
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"An evolutionary approach to multi-objective optimization problems." 2002. http://library.cuhk.edu.hk/record=b6073476.
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Zhong-Yao Zhu."6th August 2002."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2002.
Includes bibliographical references (p. 227-239).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
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Jiunn-ChennLu and 盧俊臣. "A combined multiple criteria decision-making method and simulation optimization for Lean production system design problem." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/43478317512245012819.
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博士國立成功大學
製造資訊與系統研究所碩博士班
98
Lean philosophy is a systematic approach for identifying and eliminating waste through continuous improvement in pursuit of perfection, using a pull control strategy derived from customers’ requirements. However, not all lean implementations have produced such desired results due to not having a clear implementation procedure and execution guide. This research proposes a lean pull system implementation procedure based on combining a supermarket supply with two constant work-in-process (CONWIP) structures, then proposed an implemetaion strategy. An aim is to implement lean continuous flow that can satisfy both a high service-level and low inventory cost. Simultaneously, we consider sophisticated variability, such as multi-products, random setup, random break-down, yield loss, and batch processes, and other contingencies. The problem can be solved by a multiple criteria decision-making (MCDM) method, using a hybrid Taguchi technique for order preference based on similarity to ideal solution (TOPSIS) method and value stream mapping (VSM) was applied to visualize what conditions would work when improvements are introduced. My research includes two parts of topic. The first objective implement of a lean, continuous flow, by determining an appropriate pacemaker location. The second objective takes customer demand uncertainty as a noise factor. This allowed identification of the most robust production control to identify an optimal scenario from alternative designs. To evidence the performance of this methodology, a real-world, thin film transistor-liquid crystal display (TFT-LCD) manufacturing case-study with complex variability factors are used to demonstrate and test findings. After comparing the current-state map and future-state map of the case-study, simulation results indicate that the average cycle time reduced from 26.9 days to 9.6 days concurrently inventory cost was reduced around 61% after implementing a lean-pull production strategy.
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Taboada, Heidi. "Multi-objective optimization algorithms considering objective preferences and solution clusters." 2007. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17110.
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ROUHOVÁ, Eliška. "Využití metod vícekriteriálního hodnocení variant pro hodnocení úrazového pojištění." Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-394684.
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Decision making is an inherent part of human life. Every day we decide on little things, but also some more complex problems may appear. In such cases, we need to choose a more complex process leading to the final decision. To do so, many multiple-criteria decision-making methods can be used. These methods will be explored in this thesis. They will be used to select accident insurance according to the preferences and requirements of several respondents. Accident insurance protects the insured person against the impairment of their life caused by the consequences of their injury. Accident insurance is a commitment for many years, so its choice needs close attention. The choice of the insurance can be assessed based on of several criteria. Designing a model of multi-criteria decision-making strategy was the main goal of this diploma thesis. The thesis is divided into two parts - the theoretical and practical part. Theoretical part defines the concepts of decision making, multi-criteria decision making and its methods using specialized literature. It also defines the concept of insurance and its specifics relating to accident insurance. The practical part focuses on the specific solution of the given problem. The first step was a survey of offers of insurance companies operating on the Czech market that have separate accident insurance in their product portfolio. Based on these offers, decision variants have been created. The second step was to determine the five criteria on whose basis the variants were evaluated. After the general procedure has been established, the model has been applied. The proposed procedure was tried on a group of respondents to select the best offer for them. The target group, for which the procedure was designed, was set for people between the ages of 40 and 50 who do not have a risky job and do not engage in any hazardous leisure activities. Their preferences were determined by a questionnaire, based on which the decision matrices and weightings of the individual criteria were compiled. The optimal option was recommended to respondents.
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Häckel, Sascha. "Hybride Ansätze basierend auf Dynamic Programming und Ant Colony Optimization zur mehrkriteriellen Optimierung Kürzester-Wege-Probleme in gerichteten Graphen am Beispiel von Angebotsnetzen im Extended Value Chain Management." Master's thesis, 2006. https://monarch.qucosa.de/id/qucosa%3A19381.
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In einer von Vernetzung und Globalisierung geprägten Umwelt wächst der Wettbewerbsdruck auf die Unternehmen am Markt stetig. Die effektive Nutzung der Ressourcen einerseits und die enge Zusammenarbeit mit Lieferanten und Kunden andererseits führen für nicht wenige Unternehmen des industriellen Sektors zu entscheidenden Wettbewerbsvorteilen, die das Fortbestehen jener Unternehmen am Markt sichern. Viele Unternehmen verstehen sich aus diesem Grund als Bestandteil so genannter Supply Chains. Die unternehmensübergreifende Steuerung und Optimierung des Wertschöpfungsprozesses stellt ein charakteristisches Problem des Supply Chain Managements dar und besitzt zur Erzielung von Wettbewerbsvorteilen hohes Potential. Produktionsnetzwerke sind ein wesentlicher Forschungsschwerpunkt der Professur für Produktionswirtschaft und Industriebetriebslehre an der TU Chemnitz. Das Extended Value Chain Management (EVCM) stellt ein kompetenzorientiertes Konzept für die Bildung und zum Betrieb hierarchieloser temporärer regionaler Produktionsnetzwerke im Sinne virtueller Unternehmen dar. Gegenstand dieser Arbeit ist ein diskretes Optimierungsproblem, dass einen mehrstufigen Entscheidungsprozesses unter Berücksichtigung mehrerer Ziele abbildet, der sich bei der Auswahl möglicher Partner in einem Produktionsnetzwerk nach dem Betreiberkonzept des EVCM ergibt. Da mehrere Zielstellungen bestehen, werden grundlegende Methoden der mehrkriteriellen Optimierung und Entscheidung erörtert. Neben der Vorstellung des Problems sollen mehrzielorientierte Ansätze im Sinne einer Pareto-Optimierung auf Basis des Dynamic Programmings als Verfahren zur Bestimmung von Optimallösungen sowie Ant Colony Optimization zur näherungsweisen Lösung vorgestellt werden. Darauf aufbauend werden verschiedene Möglichkeiten der Hybridisierung beider Methoden diskutiert. Die entwickelten Ansätze werden auf ihre Eignung im Rahmen der informationstechnischen Umsetzung des EVCM-Konzepts untersucht und einer Evaluierung unterzogen. Hierzu werden verschiedene Kennzahlen zur Beurteilung der Verfahren entwickelt. Die modellierten Algorithmen und entwickelten Konzepte beschränken sich nicht ausschließlich auf das betrachtete Problem, sondern können leicht auf Probleme mit ähnlichen Eigenschaften übertragen werden. Insbesondere das NP-vollständige mehrkriterielle Kürzeste-Wege-Problem stellt einen Spezialfall des behandelten Optimierungsproblems dar.
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Linstrom, Leslie. "A portfolio approach to capital project management." Diss., 2005. http://hdl.handle.net/2263/25354.
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The proposition of this dissertation is that superior capital budgeting solutions can be attained by not only analyzing projects individually but rather as part of a portfolio of projects that has the objective of maximizing the company’s range of multiple objectives, not only the economic benefit. The dissertation starts with a detailed study of current techniques and an assessment of flaws and shortcomings. This study concludes with the requirements that any new approach or model must address in order to improve on the current practices. Based on these requirements, a new model is developed based on the portfolio approach that integrates all the assumptions, constraints, project and variable interrelationships. An important feature of the model is that it selects its portfolio of capital projects in such a way that it optimizes support for the company’s multiple objectives, not only the economic objective. The dissertation concludes with the application of this model to a hypothetical case. It is concluded that, by developing and using this model, a company can improve the analysis required before capital budgets are finalized.Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2006.
Industrial and Systems Engineering
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