Facility Location Problems with Stochastic Demands and Congestion

Berman, Oded; Krass, Dmitry

doi:10.1007/978-3-642-56082-8_11

Cited by 91 publications

(74 citation statements)

References 19 publications

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“…Comprehensive surveys are given by Aikens (1985), Brandreau and Chiu (1989), Drezner (1995), Owen and Daskin (1998), and recently Melo et al (2009). Reviews exclusively concerning stochastic location problems are Louveaux (1986), Krass (2002), andSnyder (2006). Of particular interest for this work are publications dealing with multiple products, such as Geoffrion and Graves (1974), and nonlinear facility costs, such as Wollenweber (2008).…”

Section: Productsmentioning

confidence: 99%

Process Location and Product Distribution with Uncertain Yields

Caro

Rajaram

Wollenweber

2012

Operations Research

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We present a framework to analyze the process location and product distribution problem with uncertain yields for a large multinational food processing company. This problem consists of selecting the location of processes, the assignment of products, and the distribution of production quantities to markets in order to minimize total expected costs. It differs from the traditional facility location problem due to characteristics that are inherent to process industry sectors. These include significant economies of scale at high volumes, large switchover times, and production yield uncertainty. We model the problem as a nonlinear mixed-integer program. A challenging aspect of this problem is that the objective function is neither convex nor concave. We develop an exact approach to linearize the objective function. We present heuristics to solve the problem and also construct lower bounds based on a reduction of the constraint set to evaluate the quality of the solutions. This framework has been used to make process choice and product allocation decisions at the food processing company, and the estimated annual cost savings are around 10%, or $50 million. In addition, the insights from the model have had a significant strategic and organizational impact at this company. Our framework and conclusions are relevant to other industrial sectors with similar characteristics, such as pharmaceuticals and specialty chemical manufacturers.Subject classifications: processed food industry; facility location; uncertain yields; model linearization. Area of review: OR Practice.

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Section: Productsmentioning

confidence: 99%

Process Location and Product Distribution with Uncertain Yields

Caro

Rajaram

Wollenweber

2012

Operations Research

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“…The next proposition considers the case of independent gamma random variables used, for instance, to model waiting and processing times in servers locations problems (Berman and Krass, 2004).…”

Section: Non Identically Distributed Variablesmentioning

confidence: 99%

Models and algorithms for network design problems

Poss¹

2011

4OR-Q J Oper Res

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“…The arrival rate parameter for the nodes in the city center is sampled from the uniform distribution on the interval [0. The maximum number of vehicles that can be allocated to each facility j ∈ J, U j , is sampled from the uniform distribution on the interval [2,4] or [6,10]. Scenario probabilities p s , s ∈ S , are set to be equal or sampled from the uniform distribution on the interval [0.2, 0.7] and then normalized.…”

Section: Generation Of Problem Instancesmentioning

confidence: 99%

Alternate risk measures for emergency medical service system design

Noyan

2010

Ann Oper Res

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Abstract. The stochastic nature of emergency service requests and the unavailability of emergency vehicles when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. We consider an EMS system design problem with stochastic demand and locate the emergency response facilities and vehicles in order to ensure target levels of coverage, which are quantified using risk measures on random unmet demand. The target service levels for each demand site and also for the entire service area are specified. In order to increase the possibility of representing a wider range of risk preferences we develop two stochastic optimization models involving alternate risk measures. Our first model includes integrated chance constraints (ICCs), whereas the second one incorporates ICCs and a second order dominance constraint. We propose solution methods for our stochastic optimization problems and present extensive numerical results demonstrating their computational effectiveness.

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Facility Location Problems with Stochastic Demands and Congestion

Cited by 91 publications

References 19 publications

Process Location and Product Distribution with Uncertain Yields

Process Location and Product Distribution with Uncertain Yields

Models and algorithms for network design problems

Alternate risk measures for emergency medical service system design

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