Introducing capacities in the location of unreliable facilities

Albareda-Sambola, Maria; Landete, Mercedes; Monge, Juan F.; Sainz-Pardo, José L.

doi:10.1016/j.ejor.2016.10.013

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…Li et al [16] applied a "fortification" strategy in reliable facility location problems, which enables the decision maker to invest in a facility to make it totally reliable. Albareda-Sambola et al [26] presented a reliable fixed-charged location problem considering the disruption of facility capacities. Azad and Hassini [27] studied recovery strategies for facility disruptions in a network, using scenarios to model uncertainty regarding partial capacity loss over a time horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

A two-stage robust model for a reliable p-center facility location problem

Zhou

Leus

2020

Applied Mathematical Modelling

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We propose a two-stage robust model for reliable facility location when some facilities can be disrupted, for instance by a natural disaster. A reliable network is designed in a "proactive" planning phase, and when a facility is disrupted, its original clients can be reallocated to another available facility in a "reactive" phase. When demand and cost are uncertain, the initial design is also robust against the realizations (scenarios) of these parameters, which will only be revealed post-disruption. Based on the p-center location model, which attempts to optimize the worst-case performance of the network, our model is concerned with the reliability for every client. Three solution methods have been implemented and tested to solve the model, namely, a linear reformulation, a Benders dual cutting plane method, and a column-and-constraint generation method. We present an extensive numerical study to compare the performance of these methods. We find that, depending on the size of the instance (as given by the number of client sites and scenarios), either the Benders dual cutting plane method or column-and-constraint generation performs best. The effectiveness of our model is also examined in comparison with alternative facility location models.

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Section: Literature Reviewmentioning

confidence: 99%

A two-stage robust model for a reliable p-center facility location problem

Zhou

Leus

2020

Applied Mathematical Modelling

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“…In their paper, fixed intervals instead of probability distributions are used to represent uncertain travel times. Albareda-Sambola et al [12] introduced the facility capacity into a reliable facility location problem with unsplittable demands. Yahyaei and Bozorgi-Amiri [13] studied the reliable network design problem for humanitarian relief, and they proposed a robust linear programming model to deal with the uncertainty in this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(7)-(11) are the classical constraints for the maximal flow problem. Constraints (12) and (13) confirm that each demand point is allocated only one backup and one primary facility. Constraint (14) confirms that the primary facility and the backup facility must be different.…”

Section: Model Representationmentioning

confidence: 99%

“…To investigate the impact of the links to be reinforced, we delved into the solutions for G � 500. For a given ω, let us keep the primary facility and the (11)(12) 0.93 -- (11)(12)(13)(14) 0.05 0.79 76 backup facility that is allocated to each demand point unchanged and then compare the reachability guarantee before and after reinforcing a link. Table 5 lists these comparisons of the reachability guarantee when ω � 0.1, 0.5, 0.7.…”

Section: Effects Of Reinforced Linksmentioning

confidence: 99%

“…3, 0.5] 260 9.57 176 168 (1-2),(4)(5),(6)(7)(8),(9)(10),(3)(4)(5)(6)(7)(8)(9)(10)(11)(12),(773 131 269 (1-2), (4-5), (6-8), (10-11), (3-12), (12-13), (7-18), (16-18), (21-22) 1, 0.2] 260 12.47 176 324 (1-2), (4-5), (6-8), (8-9), (10-11), (3-12), (8-16), (7For G � 500 and ω � 0.5, the primary facility and the backup facility that is allocated to each demand point and the entire reinforcement links are shown inFigure 6. Here, the black arrow line indicates the primary facility of the demand point, the blue dotted arrow line indicates the backup facility of the demand point, and the red line along the link indicates the link to be reinforced.…”

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confidence: 99%

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Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Liu

2020

Journal of Advanced Transportation

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The reasonable location of emergency facilities plays an important role in both predisaster service and postdisaster relief. Moreover, damage to the transportation network often affects the accessibility of demand points, which can seriously hamper timely rescue operations. Reasonable location of emergency facilities and reinforcement of fragile roads are two important strategies to improve the reachability of demand points. In this paper, we proposed a biobjective optimization model to determine locations of emergency facilities and links to be reinforced given a limited budget. Each demand point is allocated a primary facility and a backup facility, the former can provides normal service, and the latter is prepared for postdisaster relief. One goal of the model is to minimize the operating cost of normal services, and another goal is to maximize the reachability guarantee of demand points. The novelty and contribution of this paper are that we defined the reachability by introducing damage tolerance instead of link failure probability. Based on this, we defined the reachability guarantee to deal with the worst scenario of disasters. By embedding the max-flow problem of the reachability guarantee into the emergency facility location problem, the locations of emergency facilities and links to be reinforced can be determined simultaneously. The methodology is applied to a simplified Sioux Falls transportation network. Results such as the trade-off curve of two goals, budget efficiency, and the effect of reinforcement demonstrated the effectiveness of the model.

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Fixed-Charge Facility Location Problems

Fernández¹,

Landete²

2019

Location Science

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Introducing capacities in the location of unreliable facilities

Cited by 12 publications

References 34 publications

A two-stage robust model for a reliable p-center facility location problem

A two-stage robust model for a reliable p-center facility location problem

Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

Fixed-Charge Facility Location Problems

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