Modeling congestion and service time in hub location problems

Alumur, Sibel A.; Nickel, Stefan; Rohrbeck, Brita; Saldanha-da-Gama, Francisco

doi:10.1016/j.apm.2017.10.033

Cited by 49 publications

(44 citation statements)

References 57 publications

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“…In the literature, one can find numerous extensions of the classical HLPs that arise from different practical situations. For example, a congestion has been addressed by adding a constraint to the queue length at hubs (Mohammadi et al 2011;Marianov and Serra 2003), imposing a service time limit at hubs (Alumur et al 2018), as well as by adding a nonlinear term in the objective. The HLPs with congestion have been solved either by using Lagrangian methods (Elhedhli and Wu 2010) or metaheuristics (Koksalan and Soylu 2010).…”

Section: Introductionmentioning

confidence: 99%

A single allocation hub location and pricing problem

Čvokić

Stanimirović

2019

Comp. Appl. Math.

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This study introduces a new problem for uncapacitated single allocation hub location in which pricing is taken into account. The objective is profit maximization by choosing the best hub and spoke topology and applying the optimal pricing, in the case of price-dependent demand. It is assumed that the source determining the number of hubs is endogenous. Two variants of the considered problem are addressed: deterministic and robust. For the initial non-linear model, we show how the deterministic variant can be reformulated as a mixedinteger linear program, excluding the price variables. In the robust counterpart case, the quantity of commodity flows between the pairs of customers is of stochastic nature. The goal of the robust variant is to design a hub and spoke network, together with the pricing structure, that would be immune to small perturbations of demand. Starting from the original model for the robust case, we have shown how to formulate an equivalent mixed-integer conic-quadratic program. In addition, we have proposed a 2-phase matheuristic approach for the robust variant. A computational study was conducted on the set of hub instances from the literature using the commercial state-of-the-art solver. The obtained computational results are thoroughly discussed, location patterns are analyzed and some managerial insights are provided. The experimental study also showed that the proposed matheuristic approach for the robust variant performs better compared to the commercial solver.

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Section: Introductionmentioning

confidence: 99%

A single allocation hub location and pricing problem

Čvokić

Stanimirović

2019

Comp. Appl. Math.

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“…A further step forward for the research on the multi-capacity hub location problem is to discuss the relevance with the level of congestion. Reference [41] considers a CHLP with service time and congestion for both single allocation and multiple allocation cases. They assume that service time includes travelling time through the network and handling time at hubs.…”

Section: Hub Location With Multiple Capacitymentioning

confidence: 99%

“…Afterwards, the assumptions and the parameters are depicted in the remaining subsections, respectively. Our problem is inspired by the integrated research of hub location and revenue management developed by [19], the research on the hubs with multiple capacity levels based on [41], and the reseach on the disruptions considered by [7].…”

Section: Problem Statementmentioning

confidence: 99%

An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

Hou

Huo

2019

Journal of Advanced Transportation

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This paper considers an integrated hub location and revenue management problem in which a set of capacities is available from which one can be chosen for each hub and the disruption is considered in a star-star shaped airline network. We propose a two-stage stochastic programming model to maximize the profit of the network in which the cost of installing the hubs at different levels of capacities, the transportation cost, and the revenue obtained by selling airline tickets are considered. To provide flexible solutions, a hybrid two-stage stochastic programming-robust optimization model is developed by putting relative emphasis on a weighted sum of profit maximization. Furthermore, a sample average approximation approach is used for solving the stochastic programming formulation and a genetic algorithm approach is applied for both formulations. Numerical experiments are conducted to verify the mathematical formulations and compare the performance of the used approaches.

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“…Research by Karimi and Setak (2018) showed in the problem of traffic scheduling and hub location routing, the hubs in the network are not completely interconnected, and the goal is to maximize the total traffic and minimize the total fixed cost and routing cost within the predetermined minimum arrival time. Research by Alumur, Nickel, Rohrbeck and Saldanha-da-Gama (2018)showed modeling framework for hub location issues considering hub crowded service time constraints. The service time model takes into account the running time, processing time and the delay caused by hub congestion.…”

Section: Hub-and-spoke Networkmentioning

confidence: 99%

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Huang

2019

JIEM

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Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network.

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Modeling congestion and service time in hub location problems

Cited by 49 publications

References 57 publications

A single allocation hub location and pricing problem

A single allocation hub location and pricing problem

An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

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