The Reliable Facility Location Problem: Formulations, Heuristics, and Approximation Algorithms

Shen, Zuo‐Jun Max; Zhan, Roger L.; Zhang, Jiawei

doi:10.1287/ijoc.1100.0414

Cited by 184 publications

(84 citation statements)

References 23 publications

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“…Li and Ouyang [6] studied the reliable uncapacitated fixed charge location problem where facilities were subject to spatially correlated disruptions. Shen, et al [31] studied a reliable facility location problem. They formulated this problem as a two-stage stochastic program and then as a nonlinear integer program.…”

Section: Disruption Risks In Supply Chainmentioning

confidence: 99%

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

2017

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Abstract:While biomass has been recognized as an important renewable energy source which has a range of positive impacts on the economy, environment, and society, the existence of feedstock seasonality and risk of service disruptions at collection facilities potentially compromises the efficiency and reliability of the energy supply system. In this paper, we consider reliable supply chain design for biomass collection against feedstock seasonality and time-varying disruption risks. We optimize facility location, inventory, biomass quantity, and shipment decisions in a multi-period planning horizon setting. A real-world case in Hubei, China is studied to offer managerial insights. Our computational results show that: (1) the disruption risk significantly affects both the optimal facility locations and the supply chain cost; (2) no matter how the failure probability changes, setting backup facilities can significantly decrease the total cost; and (3) the feedstock seasonality does not affect locations of the collection facilities, but it affects the allocations of collection facilities and brings higher inventory cost for the biomass supply chain.

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Section: Disruption Risks In Supply Chainmentioning

confidence: 99%

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

2017

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“…The SAA approximates the true optimal value by solving generated samples of stochastic parameters, and it is used in the reliable facility location problem with a large number of scenarios (Gade and Pohl 2009;Shen et al 2011;Aydin and Murat 2013). The steps of SAA procedure are as follows (Kleywegt et al 2001).…”

Section: Sample Average Approximationmentioning

confidence: 99%

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

Yahyaei

Bashiri

2017

J Ind Eng Int

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The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of scenarios grows exponentially with the number of facilities. To alleviate this issue, two approaches are applied simultaneously. The first approach is to apply sample average approximation to approximate the two stochastic problem via sampling. Then, by applying the multiple cuts Benders decomposition approach, computational performance is enhanced. Numerical studies show the effective performance of the SAA in terms of optimality gap for small problem instances with numerous scenarios. Moreover, performance of multi-cut Benders decomposition is assessed through comparison with the classic version and the computational results reveal the superiority of the multi-cut approach regarding the computational time and number of iterations.

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“…As described in [43], it is possible to classify uncertainty in three categories: provider-side uncertainty, receiver-side uncertainty, and in-between uncertainty. The first corresponds to the uncertainty in facility capacity, facility reliability, facility availability, etc.…”

Section: A Formulation Of the Rruflmentioning

confidence: 99%

“…In [44] one can find an excellent review on Facility Location under uncertainty, describing contributions not only from the stochastic but also from the RO perspective. More recent references to Facility Location under uncertainty include [45,7,46,19,43,2,1,25,4,3,26] and [34].…”

Section: The Rrufl and Previously Proposed Problemsmentioning

confidence: 99%

The recoverable robust facility location problem

Álvarez-Miranda

Fernández

Ljubić

2015

Transportation Research Part B: Methodological

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This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters.For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm.

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The Reliable Facility Location Problem: Formulations, Heuristics, and Approximation Algorithms

Cited by 184 publications

References 23 publications

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

The recoverable robust facility location problem

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