Results 1  10
of
32
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...
How to pay, come what may: Approximation algorithms for demandrobust covering problems
 In FOCS
, 2005
"... Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among the various uncertain scenarios in the model. While these approaches may be thought of defining data or costrobust prob ..."
Abstract

Cited by 31 (9 self)
 Add to MetaCart
(Show Context)
Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among the various uncertain scenarios in the model. While these approaches may be thought of defining data or costrobust problems, we formulate a new “demandrobust” model motivated by recent work on twostage stochastic optimization problems. We propose this in the framework of general covering problems and prove a general structural lemma about special types of firststage solutions for such problems: there exists a firststage solution that is a minimal feasible solution for the union of the demands for some subset of the scenarios and its objective function value is no more than twice the optimal. We then provide approximation algorithms for a variety of standard discrete covering problems in this setting, including minimum cut, minimum multicut, shortest paths, Steiner trees, vertex cover and uncapacitated facility location. While many of our results draw from rounding approaches recently developed for stochastic programming problems, we also show new applications of old metric rounding techniques for cut problems in this demandrobust setting.
Pay Today for a Rainy Day: Improved Approximation Algorithms for DemandRobust MinCut and Shortest Path Problems
 STACS
, 2006
"... Abstract. Demandrobust versions of common optimization problems were recently introduced by Dhamdhere et al. [4] motivated by the worstcase considerations of twostage stochastic optimization models. We study the demand robust mincut and shortest path problems, and exploit the nature of the robus ..."
Abstract

Cited by 14 (5 self)
 Add to MetaCart
(Show Context)
Abstract. Demandrobust versions of common optimization problems were recently introduced by Dhamdhere et al. [4] motivated by the worstcase considerations of twostage stochastic optimization models. We study the demand robust mincut and shortest path problems, and exploit the nature of the robust objective to give improved approximation factors. Specifically, we give a (1 + √ 2) approximation for robust mincut and a 7.1 approximation for robust shortest path. Previously, the best approximation factors were O(log n) for robust mincut and 16 for robust shortest paths, both due to Dhamdhere et al. [4]. Our main technique can be summarized as follows: We investigate each of the second stage scenarios individually, checking if it can be independently serviced in the second stage within an acceptable cost (namely, a guess of the optimal second stage costs). For the costly scenarios that cannot be serviced in this way (“rainy days”), we show that they can be fully taken care of in a nearoptimal first stage solution (i.e., by ”paying today”). We also consider “hittingset ” extensions of the robust mincut and shortest path problems and show that our techniques can be combined with algorithms for Steiner multicut and group Steiner tree problems to give similar approximation guarantees for the hittingset versions of robust mincut and shortest path problems respectively. 1
Random Keys Genetic Algorithm with Adaptive Penalty Function for Optimization of Constrained Facility Layout Problems
, 1997
"... This paper presents an extended formulation of the unequal area facilities block layout problem which explicitly considers uncertainty in material handling costs by use of expected value and standard deviations of product forecasts. This formulation is solved using a random keys genetic algorith ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
This paper presents an extended formulation of the unequal area facilities block layout problem which explicitly considers uncertainty in material handling costs by use of expected value and standard deviations of product forecasts. This formulation is solved using a random keys genetic algorithm (RKGA) to circumvent the need for repair operators after crossover and mutation. Because this problem can be highly constrained depending on the maximum allowable aspect ratios of the facility departments, an adaptive penalty function is used to guide the search to feasible, but not suboptimal, regions. The RKGA is shown to be a robust optimizor which allows a user to make an explicit characterization of the cost and uncertainty tradeoffs involved in a particular block layout problem.
Next generation factory layouts: Research challenges and recent progress. Interfaces
, 2002
"... This paper was refereed. Recent trends in industry suggest that existing layout configurations do not meet the needs of multiproduct enterprises and that there is a need for a new generation of factory layouts that are flexible, modular, and easy to reconfigure. Although most of the academic literat ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
This paper was refereed. Recent trends in industry suggest that existing layout configurations do not meet the needs of multiproduct enterprises and that there is a need for a new generation of factory layouts that are flexible, modular, and easy to reconfigure. Although most of the academic literature on layout design is based on a deterministic paradigm that assumes production requirements are known far in advance or change very little over time, a growing body of research focuses on designing layouts for dynamic and uncertain environments. An example is the research being carried out by the newly formed Consortium on Next Generation Factory Layouts (NGFL). The consortium, which involves multiple universities and several companies, is developing alternative layouts, new performance metrics, and new methods for designing flexible and reconfigurable factories. (Facilitiesequipment planning: layout. Manufacturing: performanceproductivity, strategy.) There is an emerging consensus that existing layout configurations do not meet the needs of multiproduct enterprises and there is a need for a new generation of factory layouts that are more flexible, modular, and easy to reconfigure (Askin et al. 1997,
Design of Flexible Plant Layouts
 IIE Transactions
, 2000
"... In this paper, we address the problem of designing flexible plant layouts for manufacturing facilities where product demands are subject to variability. A flexible layout is one that maintains low material handling costs despite fluctuations in the product demand levels. We extend existing procedure ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
In this paper, we address the problem of designing flexible plant layouts for manufacturing facilities where product demands are subject to variability. A flexible layout is one that maintains low material handling costs despite fluctuations in the product demand levels. We extend existing procedures for design of flexible layouts by (1) explicitly capturing the stochastic nature of product demands and the resulting variability in material flows between different processing departments, (2) allowing for the possibility of multiple processing departments of the same type to exist in the same facility, and (3) letting material flows between pairs of individual departments be determined simultaneously with the layout and as a function of demand scenarios. Optimal and heuristic methods are presented for generating flexible layouts and determining flow allocations under various design and operation assumptions. 2 1. Introduction The ability to design and operate manufacturing facilitie...
A plant location guide for the unsure
, 2008
"... This paper studies an extension of the kmedian problem where we are given a metric space (V, d) and not just one but m client sets {Si ⊆ V} m i=1, and the goal is to open k facilities F to minimize: maxi∈[m] j∈Si d(j, F) �, i.e., the worstcase cost over all the client sets. This is a “minmax ” or ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
This paper studies an extension of the kmedian problem where we are given a metric space (V, d) and not just one but m client sets {Si ⊆ V} m i=1, and the goal is to open k facilities F to minimize: maxi∈[m] j∈Si d(j, F) �, i.e., the worstcase cost over all the client sets. This is a “minmax ” or “robust ” version of the kmedian problem; however, note that in contrast to previous papers on robust/stochastic problems, we have only one stage of decisionmaking—where should we place the facilities? We present an O(log n+log m) approximation for robust kmedian: The algorithm is combinatorial and very simple, and is based on reweighting/Lagrangeanrelaxation ideas. In fact, we give a general framework for (minimization) facility location problems where there is a bound on the number of open facilities. For robust and stochastic versions of such location problems, we show that if the problem satisfies a certain “projection” property, essentially the same algorithm gives a logarithmic approximation ratio in both versions. We use our framework to give the first approximation algorithms for robust/stochastic versions of ktree, capacitated kmedian, and faulttolerant kmedian.
NEXT GENERATION FACTORY LAYOUTS: RESEARCH CHALLENGES AND RECENT PROGRESS
, 2000
"... There is an emerging consensus that existing layout configurations do not meet the needs of the multiproduct enterprise and that there is a need for a new generation of factory layouts that are more flexible, modular, and more easily reconfigurable. In this article, we offer a review of state of th ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
There is an emerging consensus that existing layout configurations do not meet the needs of the multiproduct enterprise and that there is a need for a new generation of factory layouts that are more flexible, modular, and more easily reconfigurable. In this article, we offer a review of state of the art in the area of design of factory layouts for dynamic environments. We report on emerging efforts in both academia and industry in developing alternative layout configurations, new performance metrics, and solution methods for designing the “next generation ” of factory layouts. In particular, we focus on describing efforts by the Consortium on Next Generation Factory Layouts (NGFL) to address some of these challenges. The consortium, supported by the National Science Foundation, involves multiple universities and several manufacturing companies. The goal of the consortium is to explore alternative layout configurations and alternative performance metrics for designing flexible and reconfigurable factories.
Considering Production Uncertainty In Block Layout Design
, 1999
"... This paper presents a formulation of the facilities block layout problem which explicitly considers uncertainty in material handling costs by use of expected values and standard deviations of product forecasts. This formulation is solved using a genetic algorithm metaheuristic with a flexible ba ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
This paper presents a formulation of the facilities block layout problem which explicitly considers uncertainty in material handling costs by use of expected values and standard deviations of product forecasts. This formulation is solved using a genetic algorithm metaheuristic with a flexible bay construct of the departments and total facility area. It is shown that depending on the attitude of the decisionmaker towards uncertainty, the optimal layout can change significantly. Furthermore, designs can be optimized directly for robustness over a range of uncertainty that is prespecified by the user. Keywords  facilities, heuristics, optimisation, genetic algorithms, block layout, production uncertainty 1. Introduction Facility design problems generally involve the partition of a planar region into departments (work centers or cells) along with an aisle structure and a material handling system to link the departments. The primary objective of the design problem is to minimi...