Solving the p-hub Median Problem Under Intentional Disruptions Using Simulated Annealing

Parvaresh, F.; Golpayegany, S. A. Hashemi; Husseini, S. M. Moattar; Karimi, Behrooz

doi:10.1007/s11067-013-9189-3

Cited by 46 publications

(19 citation statements)

References 70 publications

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“…Several solution approaches have been studied in the literature, including both heuristic techniques and exact methods. Examples of heuristic approaches can be found in , Aksen et al ( , 2014, Parvaresh et al (2013). Exact methods can be broadly classified into reformulation, enumeration and decomposition methods (Saharidis and Ierapetritou, 2009).…”

Section: Solution Methodologymentioning

confidence: 99%

Optimizing dynamic investment decisions for railway systems protection

Starita

Scaparra

2016

European Journal of Operational Research

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Protection investments are instrumental in reducing economic losses and preserving public safety. A systematic approach to plan security investments is paramount to guarantee that limited protection resources are utilized in the most efficient manner.In this article, we present an optimization model to identify the railway assets which should be protected to minimize the impact of worst case disruptions on passenger flows. We consider a dynamic investment problem where protection resources become available over a planning horizon. The problem is formulated as a bilevel mixed-integer model and solved using two different decomposition approaches. Random instances of different sizes are generated to compare the solution algorithms. The model is then tested on the Kent railway network to demonstrate how the results can be used to support efficient protection decisions.

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Section: Solution Methodologymentioning

confidence: 99%

Optimizing dynamic investment decisions for railway systems protection

Starita

Scaparra

2016

European Journal of Operational Research

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“…Next, they proceed to present a nonlinear fuzzy mathematical model in order to decrease costs. Parvaresh et al (2013) study imperative disruption of hubs. They model their study as Steklberg game that includes a leader and follower as two steps with consideration of bi-objective model.…”

Section: Literaturementioning

confidence: 99%

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

Chaharsooghi¹,

Momayezi²,

Ghaffarinasab³

2017

10.5267/j.ijiec

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The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.

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“…However, in a real-world situation, disruptions, failures, damages, and unexpected events may lead to unavailability of some facilities [22]. Hence, design of survivable and robust systems subjected to disruptions has been one of the important research topics in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

2016

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Abstract. The critical and undeniable role of hubs in telecommunication and networking brings about some precautions to be taken to protect networks against any disruption. In this paper, a multi-objective model in a group of hub location problems, referred to as protection models for survivable network design, is developed. In fact, this model is a hub location problem that aims to maximize the potential ow between the origin and destination node pairs in the network, which has the minimum potential ow among all O-D pairs, including multiple assignment and back-up routes. In addition, it concerns the xed cost of installation of the hub facilities. A fuzzy goal programming method is applied to solve the model. Di erent scenarios are implemented using Turkish data set and also numerical experiments are presented to illustrate the advantages of the proposed model in some aspects, compared to previous models. The results can give useful insights into telecommunication network design.

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Solving the p-hub Median Problem Under Intentional Disruptions Using Simulated Annealing

Cited by 46 publications

References 70 publications

Optimizing dynamic investment decisions for railway systems protection

Optimizing dynamic investment decisions for railway systems protection

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

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