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Facility Location under Uncertainty: A Review
 IIE Transactions
, 2004
"... Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made th ..."
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Cited by 77 (7 self)
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Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made the development of models for facility location under uncertainty a high priority for researchers in both the logistics and stochastic/robust optimization communities. Indeed, a large number of the approaches that have been proposed for optimization under uncertainty have been applied to facility location problems. This paper reviews the literature...
Robustness and efficiency: A study of the relationship and an algorithm for the bicriteria discrete optimization problem
, 2002
"... We study various definitions of robustness in a discrete scenario discrete optimization setting. We show that a generalized definition of robustness into which scenario weights are introduced can be used to identify the efficient solutions of multiple objective discrete optimization problems. We sho ..."
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Cited by 1 (0 self)
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We study various definitions of robustness in a discrete scenario discrete optimization setting. We show that a generalized definition of robustness into which scenario weights are introduced can be used to identify the efficient solutions of multiple objective discrete optimization problems. We show that the solution of a pair of optimization problems, with the first of them being a robust optimization one, is always an efficient solution. Moreover, any efficient solution can be obtained as an optimal solution to a pair of such problems. Based on this fact and some other results pertaining to the characteristics of the weights associated with efficient solutions, we propose an algorithm that relies on a parametric search to identify all of the efficient solutions of a bicriteria discrete optimization problem. We also propose a modification of the algorithm that generates a sample of efficient solutions within the coverage error specified by the Decision Maker. Our computational results show that the algorithm might become computationally demanding for large problems when all of the efficient solutions are sought. However, its performance for generating samples with prespecified quality restrictions is promising and can easily be applied to large problems as well.
Theory and Methodology A heuristic to minimax absolute regret for linear programs with interval objective function coecients
"... Abstract Decision makers faced with uncertain information often experience regret upon learning that an alternative action would have been preferable to the one actually selected. Models that minimize the maximum regret can be useful in such situations, especially when decisions are subject to ex p ..."
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Abstract Decision makers faced with uncertain information often experience regret upon learning that an alternative action would have been preferable to the one actually selected. Models that minimize the maximum regret can be useful in such situations, especially when decisions are subject to ex post review. Of particular interest are those decision problems that can be modeled as linear programs with interval objective function coecients. The minimax regret solution for these formulations can be found using an algorithm that, at each iteration, solves ®rst a linear program to obtain a candidate solution and then a mixed integer program (MIP) to maximize the corresponding regret. The exact solution of the MIP is computationally expensive and becomes impractical as the problem size increases. In this paper, we develop a heuristic for the MIP and investigate its performance both alone and in combination with exact procedures. The heuristic is shown to be eective for problems that are signi®cantly larger than those previously reported in the literature. Ó
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, 2009
"... Complex systems and enterprises, such as those typical in the aerospace industry, are subject to uncertainties that may lead to suboptimal performance or even catastrophic failures if unmanaged. This work focuses on flexibility as an important means of managing uncertainties and leverages real optio ..."
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Complex systems and enterprises, such as those typical in the aerospace industry, are subject to uncertainties that may lead to suboptimal performance or even catastrophic failures if unmanaged. This work focuses on flexibility as an important means of managing uncertainties and leverages real options analysis that provides a theoretical foundation for quantifying the value of flexibility. Real options analysis has traditionally been applied to the valuation of capital investment decisions by considering managerial flexibility. More recently, real options have been applied to the
ON ROBUST SOLUTIONS TO MULTIOBJECTIVE LINEAR PROGRAMS
"... In multiple criteria linear programming (MOLP) any efficient solution can be found by the weighting approach with some positive weights allocated to several criteria. The weights settings represent preferences model thus involving impreciseness and uncertainties. The resulting weighted average perf ..."
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In multiple criteria linear programming (MOLP) any efficient solution can be found by the weighting approach with some positive weights allocated to several criteria. The weights settings represent preferences model thus involving impreciseness and uncertainties. The resulting weighted average performance may be lower than expected. Several approaches have been developed to deal with uncertain or imprecise data. In this paper we focus on robust approaches to the weighted averages of criteria where the weights are varying. Assume that the weights may be affected by perturbations varying within given intervals. Note that the weights are normalized and although varying independently they must total to 1. We are interested in the optimization of the worst case weighted average outcome with respect to the weights perturbation set. For the case of unlimited perturbations the worst case weighted average becomes the worst outcome (maxmin solution). For the special case of proportional perturbation limits this becomes the conditional average. In general case, the worst case weighted average is a generalization of the conditional average. Nevertheless, it can be effectively reformulated as an LP expansion of the original problem.
SUPPLY CHAIN DESIGN AND DISTRIBUTION PLANNING UNDER SUPPLY UNCERTAINTY  APPLICATION TO BULK LIQUID GAS DISTRIBUTION
, 2013
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Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich
, 2007
"... Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle ..."