Hub Location Research Articles

ILS-based algorithms for the profit maximizing uncapacitated hub network design problem with multiple allocation

This study addresses a hub network design problem to maximize net profit. This problem considers an incomplete hub network with multiple allocation that does not impose capacity constraints, does not allow direct connections between non-hub nodes, and accepts the demand to be partially met, being satisfied only when profitable. To tackle this problem, which is NP-hard, we propose two heuristic algorithms based on the Iterated Local Search (ILS) metaheuristic, a standard ILS algorithm, and an Enhanced ILS algorithm, which increases the perturbation level only after a few unsuccessful attempts at improvement. Both algorithms use Random Variable Neighborhood Descent in the local search. Computational experiments were performed using benchmark instances for hub location problems, and statistical analyzes of the algorithms were presented. Numerical results confirm that both algorithms yield good-quality solutions with an acceptable runtime. In particular, the proposed algorithms obtain the optimal solution for most instances with up to 150 nodes, which have known optimal solutions. Furthermore, the proposed algorithms were able to handle instances with up to 500 nodes.

Multimodal Public Transit Network Design Method Based on Hub-and-Spoke Infrastructure

This paper presents a two-phase method for generating a hub-and-spoke network for a multimodal public transit network (MPTN). The first phase addresses the problem of clustering in MPTNs, which is a key operation in solving the hub location problem (HLP) in MPTN design. However, previous work often oversimplifies or neglects the clustering phase, so this study presents a spatial clustering algorithm that can process real urban MPTNs without reducing their size or dividing them into zones. The algorithm is tested on four large city datasets and compared with popular spatial clustering algorithms. In the second phase, a heuristic algorithm is presented that aims to maximize network coverage. The proposed method is tested using the MPTN of Greater London and the resulting hub-and-spoke network is compared with the Journey API provided by Transport for London. The results show that the total travel time is improved by 16.9% with a strict hubbing policy. Transportation planners can adapt the proposed method to their specific needs by changing the size and number of clusters using the cluster scaling factor ([Formula: see text]) parameter and including or excluding any criteria in the decision equations.

A two-stages mathematical model for a Covid-19 vaccination fuzzy scheme and resource allocation in hub locations considering transmission risk and environment factor

Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients’ safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.

A GPU-Based Genetic Algorithm for the Multiple Allocation P-Hub Median Problem

As the sizes of realistic hub location problems increase as time goes on (reaching thousands of nodes currently) this makes such problems difficult to solve in a reasonable time using conventional computers. This study aims to show that such problems may be solved in a short computing time and with high-quality solutions using the computational power of the GPU (actually available in most personal computers). So, we present a GPU-based approach for the uncapacitated multiple allocations p-hub median problems. Our method identifies the nodes that are likely to be hubs in the optimal solution and improves them via a parallel genetic algorithm. The obtained GPU implementation reached within seconds the optimal or the best solutions for all the known benchmarks we had access to and solved larger instances up to 6000 nodes so far unsolved. Compared to this study, no other article dealing with hub location problems has presented results for instances as large.

Domestic flight network hub location problem under traffic disruption with sustainability provision

There are many models in the current literature used extensively for the last 20 years on hub location problems and collect-distributive network structures. A centrally hub located facility serves as the main distribution base and flows from other facilities are collected in this hub then distributed. This centralization approach and expansion of the operational network brings the advantage of economies of scale in these days. According to the airline companies, the flight network in form of a distributor increases the profit of airline companies and provides significant savings in costs. In this study, a new hub location problem as homogeneous multi allocation p-hub median problem is proposed and solved considering the effects of airplane types and characteristics on hub locations were investigated. The results showed that the constraints and parameters used in the developed model have a significant effect on hub site selection and assignment to the different flight routes of low-cost airline companies.

A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours

A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours

Multi-modal and multi-product hierarchical hub location problem with fuzzy demands

The hub location is a major problem in the systems with high dependence on the transportation of goods, information, and passengers between the constituent parts. The present study is aimed to introduce a multi-commodity multi-model hierarchical hub location problem with uncertain demand. Thus, it is estimated by intuitionistic fuzzy variables. The objective function in the present problem is to minimize the total transportation costs in the network to determine the optimal hub location, allocate non-hub nodes to the hubs and the ground hubs to the air hubs, and identify the type of vehicles required in each route. The model has a hierarchical structure consisting of a three-echelon network (i.e., central hubs, non-central hubs, and demand nodes) connected in the star-tour-star form. A four-index mathematical model was presented and solved by GAMS optimization software. The results indicated that the objective function (cost) has a descending trend with increasing the number of hubs which can be assigned to the decline in the transportation costs. The costs increased by a decrease in the maximum delivery time which could be due to the selection method of the transportation mode. In the multi-product case, the costs declined by increasing the number of hubs as these hubs decremented the network complexity. Regarding the NP-Hard nature of the hub problems, a genetic meta-heuristic algorithm was proposed in the MATLAB software to solve the large-scale problem. According to the results, this algorithm offered close-optimal solutions. Noteworthy, this study employed known AP datasets.

Reliable Liner Shipping Hub Location Problem Considering Hub Failure

Hub ports play an important role in reducing the number of container routes and saving the operating costs of liner companies. However, hub failures caused by uncertainties such as natural disasters may cause huge recovery costs. Considering the accidental failure probability of hub ports and the reallocation of feeder ports, a global reliable liner shipping hub location problem (RLSHLP) is studied. We use the k-means algorithm to divide the global shipping network into different communities and propose two mixed integer linear programs to determine the hub port of each community. Then Benders decomposition algorithm is applied to the global RLSHLP and compared with our method. Numerical experiments verify the effectiveness of our method and determine the optimal hub port location scheme considering hub failure.

A new formulation and branch-and-cut method for single-allocation hub location problems

A new compact formulation for uncapacitated single-allocation hub location problems with fewer variables than the previous Integer Linear Programming formulations in the literature is introduced. Our formulation works even with costs not based on distances and not satisfying triangle inequality. Moreover, costs can be given in aggregated or disaggregated way. Different families of valid inequalities that strengthen the formulation are developed and a branch-and-cut algorithm based on a relaxed version of the formulation is designed, whose restrictions are inserted in a cut generation procedure together with two sets of valid inequalities. The performance of the proposed methodology is tested on well-known hub location data sets and compared to the most recent and efficient exact algorithms for single-allocation hub location problems. Extensive computational results prove the efficiency of our methodology, that solves large-scale instances in very competitive times.

Branch-and-price-and-cut algorithm for the capacitated single allocation hub location routeing problem

The paper focuses on a variant of hub location routeing problem arising in the design of intra-city express service networks, named as capacitated single allocation hub location routeing problem, in which each non-hub node should be served by exactly one hub, and both hub capacity and vehicle capacity are considered. A new mixed-integer programming formulation for the problem is provided, and a solution algorithm is developed on the basis of the column generation scheme to exactly solve the problem for the first time. The pricing subproblem is solved by a bidirectional labelling algorithm, and the master problem is strengthened by valid inequalities. Numerical experiments are conducted on the instances generated from the Australian Post dataset to test the performance of the model and the developed algorithm. Computational results prove that the algorithm outperforms the CPLEX and is able to provide optimal solutions for instances with up to 35 non-hub nodes and high-quality solutions for instances with 40 non-hub nodes within reasonable computational time, which indicates the feasibility and efficiency of the model and algorithm proposed.

Understanding community concerns in the Goat Islands logistics hub debate as a form of environmental justice

AbstractThe conflict between environmental justice and economic development is an issue central to the developing world. Using an interview analysis of community residents, this study seeks to identify whether the formally proposed trans‐shipment hub sited for the Goat Islands in Jamaica is an environmental injustice. The site, situated in the Portland Bight Protected Area, is one of Jamaica's largest fish sanctuaries and provides hundreds in the community with their livelihoods. The hub, which was promoted as an economic development project by the Jamaican government, would have allowed Jamaica to enter into the global trans‐shipment chain, subsequently reaping millions of dollars in profit. Resultant themes include issues related to environmental degradation, displacement of community members, hope for employment opportunities and investments into communities and desire for consultation. While many community residents had concerns regarding the potential location of the logistics hub, there were mixed reactions as to whether this type of development should never be allowed to take place. The study reveals the conundrum that is faced by states when it comes to promoting economic development initiatives. On the one hand, there is a desire for these forms of investment to spur economic advancement, yet on the other hand, the environmental injustices cannot be ignored. Further, the paper reveals the importance of consultation and recognition when development projects are proposed.

Port investments to address diversified risks under risk-sensitive behavior: Prevention or adaptation?

The hub location and key socioeconomic functions of seaports in the global supply chain make them vulnerable to the dual challenges of congestion and climate change. Appropriate scale and timing of prevention and adaptation can be targeted to reduce the probability of congestion and potential damage from climate change-related risks. We propose a two-period model to explore the optimal scale and timing of joint prevention and adaptation for port authorities (PAs) with risk-averse behavior. Our analysis shows that private PAs are better off making preventive efforts early and adaptive efforts late, while publicly owned PAs are better off making adaptive efforts early. Resource allocation for prevention and adaptation is strongly influenced by risk-sensitive behavior. As budgets increase, less risk-averse PAs prioritize congestion prevention followed by climate change adaptation, while more risk-averse PAs prioritize climate change adaptation followed by congestion prevention as budgets increase. The findings in this study provide significant managerial insights for decision-makers and policy-makers in ports and governments.

Hub-and-spoke network design for container shipping in inland waterways

The enormous growth in the volume of river transportation and the upgrade of inland waterways make inland shipping more and more important in transportation and logistics. This work proposes a hub-and-spoke network design (HSND) for container shipping in inland waterways based on the tree-like structure river. Firstly, the characteristics of the hub-and-spoke network in the inland waterways are presented in detail. Then, an integer linear programming model is proposed to simultaneously determine the optimal hub location, feeder port allocation, and fleet deployment to minimize the total cost of ships, transportation, and transshipment. A decomposition-based math-heuristic method and an enhanced genetic algorithm are then proposed to solve the HSND. A case study based on the traffic on the Yangtze River and extensive computational experiments are conducted to verify the effectiveness of the proposed models and methods. In addition, the impacts of the number of hub ports and the economies of scale resulting from the hub-and-spoke network are investigated from the economic and network structure perspectives, which leads to some managerial insights.

Построение самопроверяемых цифровых устройств на основе модифицированных кодов Хэмминга с контролем самодвойственности вычисляемых функций

The article presents a methodology for optimizing transport costs with the use of modeling in PTV Visum software package. Based on the analysis of transport problems in large urban agglomerations, a quality criterion was chosen for the assessment of passenger transport performance. An approach has been proposed to introduce transport hub systems with the purpose to minimize time spending on transfer, in particular, and on trip by public transport in a whole. This optimization method for transport spending represents an algorithm of 3 key blocks: the formation of transport model for being studied agglomeration, the placement conditions and the choice of transport hub locations. The model is formed on the base of 4-step approach, the trips distribution in a network is based on entropy approach which is considered in the second section in more details. The placement conditions go from an idea to minimize time total spending on moving, the number of hubs being introduced is limited by their construction financing. The approach application is demonstrated on the example of northwestern urban agglomeration Sertolovo-Osinovaya Roshcha-Pargolovo which represents being actively built-up residential districts on St. Petersburg city border. The specificities of its transport network, including also hubs and highways with complicated traffic, are determined. Based on open sources and statistical data, a transport model was developed, its calibration was pursued. 3 options for placing transport hubs on network of the agglomeration are presented. As a result of the research, the analysis for the options of transport hub placements is presented and the most promising option from the view of purpose meaning achievement for hub placement in being considered agglomeration is proposed.

Формирование системы транспортно-пересадочных узлов в городской агломерации

The article presents a methodology for optimizing transport costs with the use of modeling in PTV Visum software package. Based on the analysis of transport problems in large urban agglomerations, a quality criterion was chosen for the assessment of passenger transport performance. An approach has been proposed to introduce transport hub systems with the purpose to minimize time spending on transfer, in particular, and on trip by public transport in a whole. This optimization method for transport spending represents an algorithm of 3 key blocks: the formation of transport model for being studied agglomeration, the placement conditions and the choice of transport hub locations. The model is formed on the base of 4-step approach, the trips distribution in a network is based on entropy approach which is considered in the second section in more details. The placement conditions go from an idea to minimize time total spending on moving, the number of hubs being introduced is limited by their construction financing.
 The approach application is demonstrated on the example of northwestern urban agglomeration Sertolovo-Osinovaya Roshcha-Pargolovo which represents being actively built-up residential districts on St. Petersburg city border. The specificities of its transport network, including also hubs and highways with complicated traffic, are determined. Based on open sources and statistical data, a transport model was developed, its calibration was pursued. 3 options for placing transport hubs on network of the agglomeration are presented. As a result of the research, the analysis for the options of transport hub placements is presented and the most promising option from the view of purpose meaning achievement for hub placement in being considered agglomeration is proposed.

Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy

In this paper, we study an essential and more realistic variant of the hub location-routing problem in which third-party logistics (3PL) are incorporated into a self-built distribution hub network. A two-stage mixed-integer programming model is developed to address the proposed hub location-routing problem with mixed logistical strategies. In the strategic stage, the hub location and allocation decisions are made before the realization of uncertain demands to minimize the total construction and transportation costs. In the operational stage, the managers make vehicle routing decisions with and without the 3PL strategy, and route planning is considered for multiple owned and 3PL vehicles on the basis of simultaneous pickup and delivery. We investigate the effects of uncertain factors on the network design of this framework, as the node demands are often unobservable or difficult to obtain accurately in practice. To address the inherent uncertainty of the problem, we propose an adaptive distributionally robust model by limiting uncertain demands to a specified ambiguity set. Moreover, we reformulate the proposed model into tractable robust counterpart forms under the ambiguity set with information about the support, mean and upper bounds on the dispersion. We design a genetic algorithm with local search to solve the reformulated model on a large scale. Finally, we conduct a case study for a leading retail enterprise in the Beijing–Tianjin–Hebei region to demonstrate the effectiveness and applicability of our model.

Surrogate-based optimization approach for capacitated hub location problem with uncertainty

CJ Logistics has started to consider opening single new hub facility to expand the current transportation network system. This naturally leads to formulating a research question “where should the new hub facility be located in South Korea to minimize total transportation cost of the network system operated by the company?”. This research aims to answer the question by proposing a surrogate-based optimization approach. In addition to finding an optimal location of the new hub facility, this research performs sensitivity analysis to study the correlation between hub capacity (i.e., the source of uncertainty) and transportation cost. The results indicate that (1) total transportation cost after the establishment of the new hub facility at the optimal location is reduced by approximately 14% compared to the current transportation network system and (2) the currently operated hub facility located in Daejeon has the greatest influence on total transportation cost; while the existing hub facilities located in Cheongwon, Yongin, and Gunpo have little impact on total transportation cost after the construction of the new hub facility. It is expected that the outcome of this research helps the company systematically manage the transportation network system when the new hub facility is constructed.

Hub location–allocation for combined fixed-wireless and wireline broadband access networks

Hub location–allocation for combined fixed-wireless and wireline broadband access networks

Modeling and Solving a Linear Integer Problem (PLNE) for the Optimal Localization of a Hub Air Transport in the WAEMU Zone

In this article, we propose a linear integer model for the optimal location of a hub for air traffic in the WAEMU zone. A hub represents for an airline a base where an essential part of its activities is concentrated. Its location must therefore be judiciously determined. The hub location problem is one of the new and promising areas of research in the field of location theory. In order to satisfy a demand, the location of the hub involves the movement of people, goods between origin destination pairs required. Hubs are applied to reduce the number of transport links between the origin and destination airports. The proposed model minimizes the distances and takes into account the flow of passengers registered in the different airports and minimizes the total cost of the transfer via the hub airport. The simulations were made with the programming language Python.

Multi-period single-allocation hub location-routing: Models and heuristic solutions

This work investigates the use of time-dependent decisions in the context of hub-location routing. Instead of setting up the entire system at once, a planning horizon partitioned into several periods is considered during which the system is to be phased-in. In addition to installing the hubs, decisions are also to be made concerning the hub-level network, namely, the hub edges to use. The origin-destination flows are assumed to be time-dependent as well as the costs underlying the problem which include, set up costs for hubs and hub edges and variable operational costs at the hubs. A mathematical model is developed for the problem that can be solved up to proven optimality with a general-purpose solver for small instances of the problem. For larger instances, a four-phase matheuristic that combines principles of relax-and-fix, variable neighborhood descent and local branching schemes is proposed. In addition, two variants of the matheuristic have been developed. The above model and methodology are tested using data generated by extending existing hub location instances to our problem. The obtained results are detailed and analyzed in depth. The major conclusion is that by capturing time in the decision-making process, one may find solutions that better hedge against parameter changes throughout time. Furthermore, the overall procedure presented in this work is quite general in the sense that it can be easily adapted to other multi-period decision making problems and different objective functions.