A robust decision-making approach for p-hub median location problems based on two-stage stochastic programming and mean-variance theory: a real case study

Ahmadi, Taher; Karimi, Hadi; Davoudpour, Hamid; Hosseinijou, Seyed Abbas

doi:10.1007/s00170-014-6569-x

Cited by 14 publications

(7 citation statements)

References 23 publications

(20 reference statements)

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“…In classical hub location problems, network designers try to find suitable hub locations from all the node locations and then proper allocation of non-hub nodes to minimize the total transportation costs. Extending this to real world scenarios often involves network designers considering multiple objectives [4] or uncertain parameters [5,6,7] and network incompleteness [8,9]. In addition, the disruptive effects of natural disasters play an important role in determining the overall network performance.…”

Section: Literature Reviewmentioning

confidence: 99%

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A model for a reliable single allocation hub network design under massive disruption

Yahyaei

Bashiri

Randall

2019

Applied Soft Computing

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Hub facilities may fail to operate in networks because of accidental failures such as natural disasters. In this paper, a quadratic model was presented for a reliable single allocation hub network under massive random failure of hub facilities which more than one hub may be disrupted in a route. It determines the location of hub facilities and the primal allocation of non-hub nodes. It also determines the backup allocation in case of failure of the primal hub. A lexicographic form of bi-objective quadratic model is developed where the first objective (maximization of served demand or equivalently, minimization of lost flows) must be attained before the second objective (total cost). By adding a structure-based constraint and using enhanced linearization techniques the model was converted into a classic linear zero-one mixed integer model. The applied linear technique was compared with other techniques in terms of computational time and its better performance was approved in problem instances. An iterated local search algorithm was developed to solve large sized instances in reasonable computational time and the compu-

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Section: Literature Reviewmentioning

confidence: 99%

“…This shows that the network has a maximum demand servicing policy. The second objective is related to system costs and includes the eight terms in the above model (3)(4)(5)(6)(7)(8)(9)(10).…”

Section: The Hub Problem and The Reliable Version Of Itmentioning

confidence: 99%

A model for a reliable single allocation hub network design under massive disruption

Yahyaei

Bashiri

Randall

2019

Applied Soft Computing

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“…Wu et al [25] presented a mean-variance optimization model for solving the track allocation problem that minimizes the occupation time costs in groups of turnouts at station bottlenecks. Ahmadi et al [26] developed a systematic approach to make robust decisions for the single location-allocation -hub median problem based on mean-variance theory. Li et al [27] presented a systematic approach integrated the mean-variance and scheduling to allow for schedule delay and trip time variability under uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In a risky situation, the DM exhibits three different behaviors in the forms of riskseeking, risk-neutral, and risk-averse that originated from concavity, linearity, and convexity of their utility function [26], respectively, as shown in Figure 2. Apparently, a rational DM prefers to choose a policy with a higher expected value and a lower variance of positive utilities.…”

Section: Mean-variance Theorymentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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“…In another research, the robust optimization framework proposed by Bertsimas and Sim [31] was studied to model the capacitated single and multiple allocation hub location problems with stochastic demands that may be changed according to a given interval [32]. More recently, Ahmadi et al [33] developed a systematic risk management approach to make robust decisions for the single locationallocation p-hub median problem based on mean-variance theory and two-stage stochastic programming concepts.…”

Section: Introductionmentioning

confidence: 99%

Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times

Ghaderi

Rahmaniani

2015

Int J Adv Manuf Technol

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Hub location problems appear at the core of strategic planning for many transportation and logistics problems. This class of optimization problems has received a wide gamut of research attention during the last few decades. However, the associated mathematical formulations are mainly developed with deterministic parameters to avoid the complexities and computational difficulties. In this paper, we put forward an extension to the uncapacitated single allocation p-hub median problem under uncertainty in which demands and travel times are stochastic. A robust optimization approach is used to deal with uncertainty in parameters and design a robust hub-and-spoke network. The developed model minimizes the total expected transportation costs, while bounding the relative regret in each scenario. To efficiently solve the model, a two-stage approach is investigated to design two hybrid heuristics. We firstly apply a meta-heuristic (i.e., variable neighborhood search (VNS) or particle swarm optimization (PSO)) to find the best combination for the hubs location. Then, by fixing the hubs location, the original model easily decomposes by scenario for which an efficient tabu search (TS) is designed. Encouraging results on a wide-range of test problems are reported.

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A robust decision-making approach for p-hub median location problems based on two-stage stochastic programming and mean-variance theory: a real case study

Cited by 14 publications

References 23 publications

A model for a reliable single allocation hub network design under massive disruption

A model for a reliable single allocation hub network design under massive disruption

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Meta-heuristic solution approaches for robust single allocation p-hub median problem with stochastic demands and travel times

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