Network reliability is a performance indicator of computer/communication networks to measure the quality level. However, it is costly to improve or maximize network reliability. This study attempts to maximize network...
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Network reliability is a performance indicator of computer/communication networks to measure the quality level. However, it is costly to improve or maximize network reliability. This study attempts to maximize network reliability with minimal cost by finding the optimal transmission line assignment. These two conflicting objectives frustrate decision makers. In this study, a set of transmission lines is ready to be assigned to the computer network, and the computer network associated with any transmission line assignment is regarded as a stochastic computer network (SCN) because of the multistate transmission lines. Therefore, network reliability means the probability to transmit a specified amount of data successfully through the SCN. To solve this multipleobjectives programming problem, this study proposes an approach integrating Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-II searches for the Pareto set where network reliability is evaluated in terms of minimal paths and Recursive Sum of Disjoint Products (RSDP). Subsequently, TOPSIS determines the best compromise solution. Several real computer networks serve to demonstrate the proposed approach. (C) 2011 Elsevier B.V. All rights reserved.
Data Envelopment Analysis is used to determine the relative efficiency of Decision Making Units as the ratio of weighted sum of outputs by weighted sum of inputs. To accomplish the purpose, a DEA model calculates the ...
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Data Envelopment Analysis is used to determine the relative efficiency of Decision Making Units as the ratio of weighted sum of outputs by weighted sum of inputs. To accomplish the purpose, a DEA model calculates the weights of inputs and outputs of each DMU individually so that the highest efficiency can be estimated. Thus, the present study suggests an innovative method using a common set of weights leading to solving a linear programming problem. The method determines the efficiency score of all DMUs and rank them too.
Multiobjective multiproduct parcel distribution timetabling problem is concerned with generating effective timetables for parcel distribution companies that provide interdependent services (products) and have more tha...
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Multiobjective multiproduct parcel distribution timetabling problem is concerned with generating effective timetables for parcel distribution companies that provide interdependent services (products) and have more than one objective. A parcel distribution timetabling problem is inherently multiobjective because of the multitude of criteria that can measure the performance of a timetable. This paper provides the mathematical formulation of the problem and applies the model to a real-world case study. The application shows that without a common ground with the practitioners, it would be impossible to define the actual requirements and objectives of the company;problem definition is as important as model construction and solution method.
This work deals with the concept of satisfactory solution for Stochastic Multiobjectiveprogramming (SMP) problems. Based on previous literature, we will introduce different concepts of satisfactory solutions for SMP ...
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This work deals with the concept of satisfactory solution for Stochastic Multiobjectiveprogramming (SMP) problems. Based on previous literature, we will introduce different concepts of satisfactory solutions for SMP problems, define a new concept of solution (where the decision maker (DM) sets his/her preferences in terms of two aspiration levels for the stochastic objective and two probabilities to reach those levels), and establish some relationship between these concepts. The results will aim at featuring these concepts and determine the differences between them. Moreover, the paper proposes a new step by step procedure to exchange information between the analyst and DM prior to solving the problem. Thus, the DM will be able to choose the transformation criterion for each stochastic objective and the aspiration level. (C) 2012 Elsevier B.V. All rights reserved.
In this paper a general mathematical model for portfolio selection problem is proposed. By considering a forecasting performance according to the distributional properties of residuals, we formulate an extended mean-v...
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In this paper a general mathematical model for portfolio selection problem is proposed. By considering a forecasting performance according to the distributional properties of residuals, we formulate an extended mean-variance-skewness model with 11 objective functions. Returns and return errors for each asset obtained using different forecasting techniques, are combined in optimal proportions so as to minimize the mean absolute forecast error. These proportions are then used in constructing six criteria related to the mean, variance and skewness of return forecasts of assets in the future and forecasting errors of returns of assets in the past. The obtained multi-objective model is scalarized by using the conic scalarization method which guarantees to find all non-dominated solutions by considering investor preferences in non-convex multi-objective problems. The obtained scalar problem is solved by utilizing F-MSG algorithm. The performance of the proposed approach is tested on a real case problem generated on the data derived from Istanbul Stock Exchange. The comparison is conducted with respect to different levels of investor preferences over return, variance, and skewness and obtained results are summarized. (C) 2010 Elsevier Ltd. All rights reserved.
We develop a multi-objective stochastic programming model for supply chain design under uncertainty using a metaheuristic approach. This is a comprehensive model, which includes both the strategic and tactical levels....
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ISBN:
(纸本)9783642304330;9783642304323
We develop a multi-objective stochastic programming model for supply chain design under uncertainty using a metaheuristic approach. This is a comprehensive model, which includes both the strategic and tactical levels. The uncertainty regarding demands, supplies, processing and transportation costs is captured by generating discrete scenarios with given probabilities of occurrence. To solve the problem, we use multi-objective simulated annealing and compare the results against the goal attainment technique. Numerical results show that the proposed metaheuristic approach is a very practical solution technique.
This paper integrates positive and normative approaches to modelling. The normative approach uses assumptions associated with multiple objective programming. The positive approach uses past observations to estimate th...
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This paper integrates positive and normative approaches to modelling. The normative approach uses assumptions associated with multiple objective programming. The positive approach uses past observations to estimate the weights associated with each objective criteria. The technique encompasses both linear and non-linear objectives such as profit, cost and risk as well as quadratic calibration terms. The proposed methodology minimizes the sum of squared errors about the ideal multipleobjective function, that is one that would reproduce observed results, rather than to minimize errors between fitted and observed activity levels. The technique removes the need to rely upon the use of abstract restraints normally applied to mathematical programming methods and provides a more objective means of testing the appropriateness of a model than previously. The technique has many applications in the field of mathematical modelling such as forecasting and analysing changes in decision-making and behaviour.
In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematic...
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In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically designed to incorporate such complex preference structures, they gain more and more importance in application areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share important properties, and that many classical solution approaches have correspondences in the respective models. The analysis naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence results are proven for convex and nonconvex problems.
It is a common characteristic of many multiple objective programming problems that the efficient solution set can only be identified in approximation: as this set often contains an infinite number of points, only a di...
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It is a common characteristic of many multiple objective programming problems that the efficient solution set can only be identified in approximation: as this set often contains an infinite number of points, only a discrete representation can be computed, and due to numerical difficulties, each of these points itself might, in general, be only approximate to some efficient point. From among the various approximation concepts, this paper considers the notion of epsilon-efficient solutions and proposes several new methods for their generation. Supporting theoretical results are established and a numerical example is provided.
In this paper, we propose a simple linear multiple objective programming to deal with the fuzzy shortest path problem. The proposed approach does not need to declare 0-1 variables to solve the fuzzy shortest path prob...
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In this paper, we propose a simple linear multiple objective programming to deal with the fuzzy shortest path problem. The proposed approach does not need to declare 0-1 variables to solve the fuzzy shortest path problem because it meets the requirements of the network linear programming constraints. Therefore, the linear programming relaxation can be used to arrive at an integer solution without using the Branch and Bound technique, and the complexity of our proposed method can be reduced. A compromising non-dominated integer optimal solution, the fuzzy shortest path, can be obtained easily without adding extra constraints. This approach not only can obtain a fuzzy shortest path but also can reduce the complexity of solving the basic fuzzy shortest path problem without using 0-1 variables. Three examples with trapezoidal and triangular fuzzy numbers in arc length are used to demonstrate the proposed method in more details.
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