The stochastic -hub center problem with service-level constraints

Sim, Thaddeus; Lowe, Timothy J.; Thomas, Barrett W.

doi:10.1016/j.cor.2008.11.020

Cited by 145 publications

(47 citation statements)

References 23 publications

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“…For example, Mariano and Serra [11] suggested a queuing model for hub location problems in airline industry where the arrivals of planes follow a Poisson process (see also [12] as an extension of this study). In another study, Sim et al [13] developed a stochastic model for p-hub median problem in package delivery networks and assumed that the travel time between the nodes follow normal distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Karimi

Davoudpour

et al. 2014

Int J Adv Manuf Technol

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The stochastic location-allocation p-hub median problems are related to long-term decisions made in risky situations. Due to the importance of this type of problems in real-world applications, the authors were motivated to propose an approach to obtain more reliable policies in stochastic environments considering the decision makers' preferences. Therefore, a systematic approach to make robust decisions for the single location-allocation p-hub median problem based on mean-variance theory and twostage stochastic programming was developed. The approach involves three main phases, namely location modeling, risk modeling, and decision making, each including several steps. In the first phase, the pertinent location-allocation model of the problem is developed in the form of a two-stage stochastic model based on its deterministic version. A risk measure, based on total cost function and mean-variance theory, is derived in the second phase. Furthermore, two heterogeneous terms of the risk measure have been normalized and an innovative procedure has been proposed to significantly improve the calculation efficiency. In the third phase, the Pareto solution is obtained, the frontier curve is depicted to determine the decision maker's risk aversion coefficient, and a robust policy is obtained through optimization based on decision makers' preferences. Finally, a case study of an automobile part distribution system with stochastic demand is described to further illustrate our risk management and analysis approach.

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Section: Literature Reviewmentioning

confidence: 99%

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

Karimi

Davoudpour

et al. 2014

Int J Adv Manuf Technol

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show abstract

“…Campbell (2009) minimizes the total transportation cost while ensuring the timedefinite transportation by considering feasible assignments only. Sim et al (2009) consider the p-hub center problem and allow stochasticity on the travel times. Other examples are Yaman et al (2007), where stopovers are incorporated; Yaman (2009), where a hierarchical hub network is designed; and and , where incomplete hub networks are allowed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chen et al (2008) design tree structures over the already established network while minimizing the violations of the service levels. Campbell (2009) and Sim et al (2009) consider the hub network design decisions together with the time-definite transportation. Campbell (2009) minimizes the total transportation cost while ensuring the timedefinite transportation by considering feasible assignments only.…”

Section: Literature Reviewmentioning

confidence: 99%

Release Time Scheduling and Hub Location for Next-Day Delivery

Yaman

Karaşan

Kara

2012

Operations Research

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Inspired by a real-life problem faced by one of the largest ground-based cargo companies of Turkey, the current study introduces a new facet to the hub location literature. The release time scheduling and hub location problem aims to select a specified number of hubs from a fixed set of demand centers, to allocate each demand center to a hub, and to decide on the release times of trucks from each demand center in such a way that the total amount of cargo guaranteed to be delivered to every potential destination by the next day is not below a threshold and the total routing cost is minimized. The paper introduces integer programming models to solve this problem in the special cases when the cargo uniformly arrives to each demand center during the day and the more realistic pattern of when the cargo arrivals exhibit a piecewise linear form. Several classes of valid inequalities are proposed to strengthen the formulations. Extensions with multiple service levels and discrete sets for release times are also discussed. Computational results show the computational viability of the models under realistic scenarios as well as the validity of the proposed problems in answering several interesting questions from the cargo sector's perspective.

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“…In both of these researches, a probabilistic constraint controls the congestion in the hub facilities. Sim et al (2009) consider p-center hub problem with service level constraints. They consider a stochastic travel time for each arc.…”

Section: Introductionmentioning

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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The stochastic -hub center problem with service-level constraints

Cited by 145 publications

References 23 publications

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

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

Release Time Scheduling and Hub Location for Next-Day Delivery

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

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