Results 1 - 10
of
17
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 ..."
Abstract
-
Cited by 77 (7 self)
- Add to MetaCart
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...
Min-max and min-max regret versions of combinatorial optimization problems: A survey
- European Journal of Operational Research
"... Min-max and min-max regret criteria are commonly used to define robust solutions. After motivating the use of these criteria, we present general results. Then, we survey complexity results for the min-max and min-max regret versions of some combinatorial optimization problems: shortest path, spannin ..."
Abstract
-
Cited by 21 (1 self)
- Add to MetaCart
(Show Context)
Min-max and min-max regret criteria are commonly used to define robust solutions. After motivating the use of these criteria, we present general results. Then, we survey complexity results for the min-max and min-max regret versions of some combinatorial optimization problems: shortest path, spanning tree, assignment, min cut, min s-t cut, knapsack. Since most of these problems are NP-hard, we also investigate the approximability of these problems. Furthermore, we present algorithms to solve these problems to optimality.
A Min-Max Regret Robust Optimization Approach for Interval Data Uncertainty
- Journal of Optimization Theory and Applications
"... This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty t ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty that can occur in any of the parameters, and the second stage problem is a linear programming model. Each uncertain parameter can take its value from a finite set of real numbers with unknown probability distribution independently of other parameters ’ settings. This structure of parametric uncertainty is referred to in this paper as the full-factorial scenario design of data uncertainty. The proposed algorithm is shown to be efficient for solving large-scale min-max regret robust optimization problems with this structure. The algorithm coordinates three mathematical programming formulations to solve the overall optimization problem. The main contributions of this paper are the theoretical development of the three-stage optimization algorithm, and improving its computational performance through model transformation, decomposition, and pre-processing techniques based on analysis of the problem structure. The proposed algorithm is applied to solve a number of robust facility location problems under this structure of parametric uncertainty. All results illustrate significant improvement in computation time of the proposed algorithm over existing approaches.
Planning the e-Scrap Reverse Production System Under Uncertainty in the State of Georgia: A Case Study,” Electronics Packaging Manufacturing
- IEEE Transactions on
, 2006
"... Abstract—Due to legislative requirements, environmental con-cerns, and market image, the disposition of end-of-life e-scrap is attracting tremendous attention in many parts of the world today. Effective management of returned used product flows can have a great impact on the profitability and result ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Due to legislative requirements, environmental con-cerns, and market image, the disposition of end-of-life e-scrap is attracting tremendous attention in many parts of the world today. Effective management of returned used product flows can have a great impact on the profitability and resulting financial viability of associated e-scrap reverse production systems. However, designing efficient e-scrap reverse production systems is complicated by the high degree of uncertainty surrounding several key factors. Very few examples of this complex design problem are documented in the academic literature. This paper contributes as analysis of a new, large-scale application that designs an infrastructure to process used televisions, monitors, and computer central pro-cessing units (CPUs) in the state of Georgia in the U.S. The case study employs a scenario-based robust optimization model for supporting strategic e-scrap reverse production infrastructure design decisions under uncertainty. A mixed integer linear pro-gramming (MILP) model is used to maximize the system net profit for specified deterministic parameter values in each scenario, and then a min–max robust optimization methodology finds a robust solution for all of the scenarios. Index Terms—Electronics recycling, reverse production systems, robust optimization. I.
A Robust Approach to Location-Allocation Problem under Uncertainty
, 2008
"... This paper presents a new mathematical model for location-allocation problem considering uncertain parameter. In real-world cases, demand, distance, traveling time or any parameters in classical models may change over the period of time. So, considering uncertainty yields more flexibility for the re ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
This paper presents a new mathematical model for location-allocation problem considering uncertain parameter. In real-world cases, demand, distance, traveling time or any parameters in classical models may change over the period of time. So, considering uncertainty yields more flexibility for the results and its applications. In our study, environmental uncertainty is described by discrete scenarios where probability of occurrence each of them is not known. So, we use robust optimization technique to analyze the model. Therefore, we introduce a formulation of the robust location-allocation problem in which we have budget constraint. Also, we present mean value model where each uncertain parameter is replaced by its mean to compare with robust model. Finally, some numerical examples are illustrated to show effectiveness of the robust solutions.
LE MEMBRES DUJURY:
"... Gestion des risques dans les chaînes logistiques: planification sous incertitude par la théorie des possibilités vendredi 23 septembre 2011 ..."
Abstract
- Add to MetaCart
Gestion des risques dans les chaînes logistiques: planification sous incertitude par la théorie des possibilités vendredi 23 septembre 2011
unknown title
, 2012
"... Gestion des risques dans les châınes logistiques: planification sous incertitude par la théorie des possibilités ..."
Abstract
- Add to MetaCart
Gestion des risques dans les châınes logistiques: planification sous incertitude par la théorie des possibilités
Robust Parameter Estimation of Density Functions under Fuzzy Interval Observations
"... Abstract This paper deals with the derivation of a probabilistic parametric model from interval or fuzzy data using the maximum likelihood principle. In contrast with classical techniques such as the EM algorithm, that define a precise likelihood function by averaging inside each imprecise observat ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract This paper deals with the derivation of a probabilistic parametric model from interval or fuzzy data using the maximum likelihood principle. In contrast with classical techniques such as the EM algorithm, that define a precise likelihood function by averaging inside each imprecise observations, our approach presupposes that each imprecise observation underlies a precise one, and that the uncertainty that pervades its observation is epistemic, rather than representing noise. We define an interval-valued likelihood function and apply robust optimisation methods to find a safe plausible estimate of the statistical parameters. The resulting density has a standard deviation that is large enough to cover the imprecision of the observations, making a pessimistic assumption on dispersion. This approach is extended to fuzzy data by optimizing the average of lower likelihoods over a collection of data sets obtained from cuts of the fuzzy intervals, as a trade off between optimistic and pessimistic interpretations of fuzzy data. The principles of this method are compared with those of other existing approaches to handle incompleteness of observations, especially the EM technique.
