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By
Pelegrín, Blas; Fernández, Pascual; García Pérez, María Dolores
4 Citations
We consider the facility location problem for an expanding chain which competes with other chains offering the same goods or service in a geographical area. Customers are supposed to select the facility with maximum utility to be served and facilities in the expanding chain may have different owners. We first use the weighted method to develop an integer linear programming model to obtain Pareto optimal locations related to the inner competition between the owners of the old facilities and the owners of the new facilities. This model is applied to maximizing the profit of the expanding chain taking into account the loss in market share of its old facilities caused by the entering of new facilities (cannibalization effect). A study with data of Spanish municipalities shows that the cannibalization effect can be significantly reduced by sacrificing a small portion of profit.
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By
Margaliot, N.
4 Citations
The information‐economics approach to assessing the value of information is different from the statistical approach. The statistical approach focuses on determining the probabilities of type I and II errors, while the information‐economics approach focuses on maximizing the expected monetary value of the whole process. This attitude is the basis for the models of sequential decision processes, especially Markov decision processes (MDP) or partially observed Markov decision processes (POMDP). However, as in traditional single‐sampling models, the sample size and sampling costs are not treated as decision variables in a cost‐effective manner. This paper uses a well‐known information‐economics model ‐ the Information Structure Model ‐ to determine the optimal sample size and decision rule in QC single‐sampling problems. The method uses rough information about the costs of types I and II errors and other parameters of the sampling problem. That method can be applied by decision makers to decide whether to use a QC sample and to determine the optimal QC plan in order to maximize the long‐range expected monetary value of sampling gained by the firm. An algorithm for single‐sampling plan determination is presented toward the end of the paper. Applications to double‐sampling or sequential‐sampling problems need further research.
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By
Kulikowski, Roman
1 Citations
The paper deals with optimization of allocation of human resources among different activities. It is assumed that an individual is characterized by a “risk averse” and “constant return to scale” utility function of two variables: motivation to perform and reward following the performance. The individual is trying to maximize the utility by the best allocation of his time resources among the activities and by selecting the best portfolio of activities. Motivations are regarded, generally, as the product of the individual's preferences (i.e. subjective choice probabilities), productivities of time, output prices, performance and access probabilities, etc., while the rewards are profits or salaries connected with each activity. Satisfaction is defined as the maximum of utility attained for the optimum allocation and selection strategies. It is shown that for the given “equitable reward rate”, the optimum allocation and portfolio selection strategies can be derived explicitly and the derivation does not require the explicit knowledge of the individual's utility function.
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By
Wakker, Peter P.
2 Citations
It is shown that assumptions about risk aversion, usually studied under the presupposition of expected utility maximization, have a surprising extra merit at an earlier stage of the measurement work: together with the surething principle, these assumptions imply subjective expected utility maximization for monotonic continuous weak orders.
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By
Deák, István
3 Citations
A heuristic procedure, called successive regression approximations (SRA) has been developed for solving stochastic programming problems. They range from equation solving to probabilistic constrained and twostage models through a combined model of Prékopa. We show here, that due to enhancements in the computer program, SRA can be used to solve largescale twostage problems with 100 first stage decision variables and a 120 dimensional normally distributed random right hand side vector in the second stage problem. A FORTRAN source program and computational results for 124 problems are presented at
www.unicorvinus.hu/~ideak1
.
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By
Hochreiter, Ronald; Pflug, Georg Ch.
95 Citations
The quality of multistage stochastic optimization models as they appear in asset liability management, energy planning, transportation, supply chain management, and other applications depends heavily on the quality of the underlying scenario model, describing the uncertain processes influencing the profit/cost function, such as asset prices and liabilities, the energy demand process, demand for transportation, and the like. A common approach to generate scenarios is based on estimating an unknown distribution and matching its moments with moments of a discrete scenario model. This paper demonstrates that the problem of finding valuable scenario approximations can be viewed as the problem of optimally approximating a given distribution with some distance function. We show that for Lipschitz continuous cost/profit functions it is best to employ the Wasserstein distance. The resulting optimization problem can be viewed as a multidimensional facility location problem, for which at least good heuristic algorithms exist. For multistage problems, a scenario tree is constructed as a nested facility location problem. Numerical convergence results for financial meanrisk portfolio selection conclude the paper.
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By
Datta, Manjira; Mirman, Leonard J.; Morand, Olivier F.; Reffett, Kevin L.
Show all (4)
10 Citations
In this paper, we provide an overview of an emerging class of “monotone map methods” in analyzing distorted equilibrium in dynamic economies. In particular, we focus on proving the existence and characterization of competitive equilibrium in nonoptimal versions of the optimal growth models. We suggest two alternative methods: an Euler equation method for a smooth, strongly concave environment, and a value function method for a nonsmooth supermodular environment. We are able to extend this analysis to study models that allow for unbounded growth or a labor–leisure choice.
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By
Vallée, T.; Deissenberg, Ch.; Basar, T.
8 Citations
The distinctive characteristic of a “Reversed Stackelberg Game” is that the leader playstwice, first by announcing his future action, second by implementing a possibly differentaction given the follower's reaction to his announcement. In such a game, if the leader usesthe normal Stackelberg solution to find (and announce) his optimal strategy, there is a strongtemptation for him to cheat, that is, to implement another action than the one announced. Inthis paper, within the framework of a standard discrete time Linear‐Quadratic DynamicReversed Stackelberg game, we discuss and derive the best possible open‐loop cheatingstrategy for an unscrupulous leader.
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By
Carneiro, João; Conceição, Luís; Martinho, Diogo; Marreiros, Goreti; Novais, Paulo
Show all (5)
4 Citations
Many multiple criteria decision analysis (MCDA) methods have been proposed over the last decades. Some of the most known methods share some similarities in the way they are used and configured. However, we live in a time of change and nowadays the decisionmaking process (especially when done in group) is even more demanding and dynamic. In this work, we propose a MCDA method that includes cognitive aspects (cognitive analytic process, CAP). By taking advantage of aspects such as expertise level, credibility and behaviour style of the decisionmakers, we propose a method that relates these aspects with problem configurations (alternatives and criteria preferences) done by each decisionmaker. In this work, we evaluated the CAP in terms of configuration costs and the capability to enhance the quality of the decision. We have used the satisfaction level as a metric to compare our method with other known MCDA methods in literature (utility function, AHP and TOPSIS). Our method proved to be capable to achieve higher satisfaction levels compared to other MCDA methods, especially when the decision suggested by CAP is different from the one proposed by those methods.
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By
Campêlo, Manoel; Figueiredo, Tatiane; Silva, Ana
Based on the sociometric analysis of social networks, we introduce the sociotechnical teams formation problem (STFP). Given a group of individuals with different skillsets and a social network that captures the mutual affinity between them, the problem consists in finding a set of pairwise disjoint teams, as harmonious as possible, with a minimum specified number of individuals per team per skill. We prove that STFP is $$\mathcal {NP}$$hard and propose an integer linear programming formulation. We show several classes of facetinducing inequalities for the corresponding polytope. Computational experiments performed on a set of 120 test instances attest the efficiency of a solution method based on the formulation strengthened by valid inequalities and on a simulated annealing algorithm used to provide good initial feasible solutions.
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