Addressing congestion on single allocation hub-and-spoke networks

Camargo, Ricardo Saraiva de; Miranda, Gilberto de

doi:10.1590/s0101-74382012005000024

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…With the help of Benders Decomposition Algorithm, the standard data set problem of 81 nodes was successfully solved, which is of vital significance in effectively balancing the scale economic benefits and congestion cost loss. Camargo and Miranda [17] pointed out the problems in calculating congestion cost with the power exponential function and analyzed the cost components from the perspective of network owner and network user. Moreover, the generalized Benders Decomposition Algorithm and the external approximation method were used for model optimized solution.…”

Section: Related Workmentioning

confidence: 99%

Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion

Huang

Qiu

2021

Journal of Advanced Transportation

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The design of the hub-and-spoke network has wide applications in the freight transportation system. This design involves the location of a group of hubs as well as the allocation between nonhub nodes and the hubs after the location. On the basis of the traditional single distribution hub-and-spoke network, the congestion flow waiting model (CFWM) and the congestion flow redistribution model (CFRM) are proposed in this paper after considering traffic waiting and traffic diversion, respectively, in the case of hub congestion. The presented models focus on the design of single distribution hub-and-spoke logistics network under traffic congestion. The objective function minimizes the total cost of the road network on the premise of ensuring the normal operation of the logistics network, which effectively balances the contradiction between the economic benefits of traffic scale and the congestion cost. Given the complexity of the problem, the congestion cost function is linearized, and the mutational particle swarm optimization (MPSO) is employed for the solution. Additionally, certain calculation experiments and sensitivity analysis of the congestion optimization model are conducted to verify the effectiveness and applicability of the constructed hub-and-spoke network and the congestion solutions. The results indicate that the optimized logistics network may effectively alleviate congestion, balance the network freight flow, and improve the stability of the hub-and-spoke network.

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Section: Related Workmentioning

confidence: 99%

Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion

Huang

Qiu

2021

Journal of Advanced Transportation

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“…[40] compared the results of a single allocation model with two different cost functions for congestion: a power-law function and a Kleinrock average delay function. [41] focused on solving large-scale instances of their previously introduced models and presented a generalized Benders decomposition and an outer approximation method. [42] introduced the perspective of a network user, and compared their solutions to those from the perspective of the network owner.…”

Section: Introductionmentioning

confidence: 99%

Modeling congestion and service time in hub location problems

Alumur

Nickel

Rohrbeck

et al. 2018

Applied Mathematical Modelling

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In this paper, we present a modeling framework for hub location problems with a service time limit considering congestion at hubs. Service time is modeled taking the traveling time on the hub network as well as the handling time and the delay caused by congestion at hubs into account. We develop mixed-integer linear programming formulations for the single and multiple allocation versions of this problem. We further extend the multiple allocation model with a possibility of direct shipments. We test our models on the well-known AP data set and analyze the effects of congestion and service time on costs and hub network design.We introduce a measure for the value of modeling congestion and show that not considering the effects of congestion may result in increased costs as well as in building infeasible hub networks.

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“…It entails discarding the current Benders decomposition scheme and moving on to the generalized Benders decomposition (GBD) of Geoffrion [83], which is capable of solving non-linear convex problems. According to a preliminary study on the work of de Camargo & de Miranda Jr. [52], all the methods embedded into the Benders scheme (i.e., the enhancements of [125], [147] and [136], as well as the ad hoc techniques applied to both inelastic and elastic demand versions) can be easily incorporated into the GBD. As a first attempt, passenger waiting times under non-congested scenarios will need to be dealt with.…”

Section: Discussionmentioning

confidence: 99%

Conjoint design of railway lines and frequency setting under semi-congested scenarios

López Ramos

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This thesis develops mathematical programming models which integrate network design (ND) and line frequency setting (LFS) phases. These appear in transport planning studies that extend an existing urban public transportation system (UPTS) and are suitable for underground and rapid transit systems. The ND phase extends the working UPTS, taking as inputs the locations of candidate stretches and stations on the new lines, as well as construction costs which cannot exceed the available infrastructure budget. Regarding the LFS phase, frequencies and vehicles are assigned to functioning and newly built lines, providing that they do not exceed resource capacities and the time horizon. The developed models take into account the type of service patterns that may operate on the lines of the transport system. They include local services, where vehicles halt at every node in the line, and express services, in which vehicles halt at only a subset of nodes in the line. A passenger assignment model allows solving, simultaneously, the ND and LFS phases under a system optimum point of view. The combined model has two variants: one which deals with inelastic demand and another which faces elasticities in demand. The latter originates from changes in the modal choice proportions of travelers and may result from modifications in the public transport system. The former does not take into account competition among several modes of transportation and it is formulated as a mixed-integer linear programming problem. In contrast, the latter allows passengers to travel via two modes of transportation: public transport and private car. It is formulated as a mixed-integer linear bi-level programming problem (MILBP) with discrete variables only in the upper level. In both models, a complementary network is used to model transfers among lines and to reach the passenger¿s origin and/or destination nodes when the constructed UPTS does not cover them. The model with inelastic demand is initially solved by means of the commercial solver CPLEX under three different mathematical formulations for the ND phase. The first two are exact approaches based on extensions of Traveling Salesman Problem formulations for dynamic and static treatment of the line¿s subtours, whereas the last one is an approximation inspired by constrained k-shortest path algorithms. In order to deal with large-sized networks, a quasi-exact solution framework is employed. It consists of three solving blocks: the corridor generation algorithm (CGA), the line splitting algorithm (LSA), and a specialized Benders decomposition (SBD). The LSA and CGA are heuristic techniques that allow skipping some of the non-polynomial properties. They are related to the number of lines under construction and the number of feasible corridors that can be generated. As for the SBD, it is an exact method that splits the original mathematical programming problem into a series of resolutions, composed of two mathematical problems which are easier to solve. Regarding the elastic demand variant, it is solved under the same framework as the specialized Benders decomposition adaptation for solving MILBP, which results from this variant formulation. The inelastic demand variant is applied to two test cases based on underground network models for the cities of Seville and Santiago de Chile. Origin destination trip matrices and other parameters required by the models have been set to likely values using maps and published studies. The purpose of these networks is to test the models and algorithms on realistic scenarios, as well as to show their potentialities. Reported results show that the quasi-exact approach is comparable to approximate techniques in terms of performance. Regarding the elastic demand variant, the model is more complex and can be applied only to smaller networks. Finally, some further lines of research for both modeling and algorithmic issues are discussed.

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Addressing congestion on single allocation hub-and-spoke networks

Cited by 5 publications

References 56 publications

Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion

Study on the Optimization of Hub-and-Spoke Logistics Network regarding Traffic Congestion

Modeling congestion and service time in hub location problems

Conjoint design of railway lines and frequency setting under semi-congested scenarios

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