Hub location and routing problem with alternative paths

Liner shipping is an increasingly attractive maritime transport, not only by the growing volume of goods transported in the world, but also by the diversity of products that can be carried and its service stability. At liner shipping operation, a hub and spoke structure is often used in which deep sea services take place at the hub network and short sea services can be done in the regional network between the hub and its respective spoke nodes. In this work a new hierarchical structure of hub and spoke with sub-hub is presented. Deep sea services and short sea services are depicted in circular form and the sub-hubs are intersection points of neighbour's regional networks. Both cargo-routing with transhipment in hub ports or in sub-hub ports are allowed. The binary integer linear programming model proposed in this work offers, together, a hub location with multiple-spoke allocation problem, a service design problem and the cargo routing problem. Experimental results show good performances comparatively to the classic hub and spoke network structure.

The capacitated directed cycle hub location and routing problem under congestion

Hub covering location problems with different coverage types

PurposeThe purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems.Design/methodology/approachThis study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets.FindingsThis study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates.Practical implicationsHub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time.Originality/valueTo the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

Adapting polyhedral properties from facility to hub location problems

The Tree of Hubs Location Problem

Community structure based global hub location problem in liner shipping

An intermodal hub location problem for container distribution in Indonesia

Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

The Hub Location-Allocation Problem is one of the most important topics in industrial engineering and operations research, which aims to find a form of distribution strategy for goods, services, and information. There are plenty of applications for hub location problem, such as Transportation Management, Urban Management, locating service centers, Instrumentation Engineering, design of sensor networks, Computer Engineering, design of computer networks, Communication Networks Design, Power Engineering, localization of repair centers, maintenance and monitoring power lines, and Design of Manufacturing Systems. In order to define the hub location problem, the present chapter offers two different metaheuristic algorithms, namely Particle Swarm Optimization or PSO and Differential Evolution. The presented algorithms, then, are applied to one of the hub location problems. Finally, the performances of the given algorithms are compared in term of benchmarking.

Hub location under competition

Airports and airlines share a common interest in providing passengers with fast and convenient access to airports. With the introduction of air taxis as a new means of transport, the opportunity will soon emerge to introduce air taxi shuttle services to airports. In this context, for each individual airport the question arises where the facilities for landing, ground handling and take-off, also called vertiports, should be located in the area surrounding the airport. We formulate the problem as a hub location problem with multiple allocation, with the objective to maximize the number of passengers who choose to reach the airport by air taxi. In our model, we explicitly incorporate passengers’ choice behavior with respect to available means of transport using a multinomial logit model. We linearize the problem and provide guidance on the steps needed to use the model in practice. In an extensive case study, we apply the model to real data for Munich Airport, perform a stated preference study to parameterize the multinomial logit model, suggest locations for vertiports throughout Bavaria as a result, and investigate to what extent the introduction of air taxi shuttle flights can reduce travel times to Munich Airport.

Formulating and solving splittable capacitated multiple allocation hub location problems

Transport of goods is a theme that consumes a lot of time in the planning of companies, because it involves different factors that influence in the quality of result. The case of liner shipping network is considered. This one is involved with transportation of bigger volume of goods between different continents. Optimizing the service network, already is possible to reduce the number of vessels and, consequently, of crews, or the lead time of a demand, or the fuel consumption, or increase the number of customers meet in the same time interval. With a hub and spoke structure it is possible to aggregate demands of different points in some ports and it can get a bigger scale economy of transport. This work proposes a hierarchical structure of hub and spoke where the service network between hubs, representing deep sea operations, is in pendular form and the service sub-network, formed by spokes and its respective hub, representing short sea operations, is in cycle form. The use of sub-hubs linking together two sub-network, permits to create alternative paths for some demands, decreasing the travel time and consequently decreasing the CO2 emission of network, conciliating an economy of spending with a sustainable logistic.

In this study, the new constraints which can be applied in network design problem are developed by taking especially the sectoral features of air transportation and aircraft types into consideration. A new mixed integer linear programming model is developed by including the new constraints into the traditional model of uncapacitated single allocation p-hub median problem accordingly. The aim of this study is to analyze the effects of the new constraints on the hub location. The model is tested by using Turkish air cargo statistics and the data related to the aircraft types, A300-B4 and F27-500. The results of the analysis show that the new constraints and the aircraft types used in air cargo transportation have the significant effects on the hub location problem.