Hub Location Research Articles

The role of hubs and economies of scale in network expansion

Freight distribution often operates on the basis of consolidation, which is achieved through the use of hub facilities that allow for economies of scale. Freight networks need to be expanded to meet future demand, to cater for new markets, and to accommodate trends in global supply chains, for which strategic decisions need to be made. These decisions mainly entail the number and location of new hubs to be established. As network expansions require significant capital, striking a balance between the benefits afforded by the new hubs and the expansion costs is crucial. This paper investigates a hub network expansion problem where the configuration of the resulting network is determined by the trade-off between the fixed costs of locating new hubs and new links, as well as routing costs of shipping commodities, and the cost reductions achieved through economies of scale, without imposing a predetermined network structure. This paper also describes a mixed integer programming formulation of the problem and a Benders decomposition algorithm that uses several enhancement techniques to efficiently solve the model to optimality. The application of the algorithm on a real-life case study arising in the expansion of the Indonesian freight transport network yields several managerial insights. In particular, expanding the network with additional hubs and links can yield substantial cost savings, averaging at 47.6%, although at the expense of an increase in the length of the commodity paths. Failing to operate the network at the selected level of economies of scale can result in an increase in the routing costs by up to 58.8%. Expanding the network with no additional hubs leads to a rise in total costs of up to 20.9%. Finally, lower economies of scale leads to an increase in the length of commodity paths, with the routing cost being identified as the most sensitive parameter.

Modeling the Feasibility of Fueling CNG Heavy-Duty Trucks in Toronto, Canada

Compressed Natural Gas (CNG) is cleaner than diesel for powering heavy duty trucks. A framework for establishing mobile on-site CNG refueling at truck yards is proposed and evaluated for the Greater Toronto and Hamilton Area (GTHA), Canada. The locations for potential CNG hubs where mobile trailers obtain CNG to fuel parked trucks at yards were determined. Also, the demand representing parked CNG trucks was estimated across the GTHA. With 15 potential CNG hubs, location-allocation modeling suggests that one CNG hub located in the Peel region can cover 88% of the demand within a 30-minute driving window across the study area.

Retraction Note: Sustainable multi-period hub location under uncertainty

Retraction Note: Sustainable multi-period hub location under uncertainty

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.

Cross-species striatal hubs: Linking anatomy to resting-state connectivity

Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders.

Stratified p-Hub Median and Hub Location Problems: Models and Solution Algorithms

Stratified <i>p</i>-Hub Median and Hub Location Problems: Models and Solution Algorithms

Determining the optimal food hub location in the fresh produce supply chain

Purpose Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions. Design/methodology/approach Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches. Findings Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making. Practical implications This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers. Social implications The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs. Originality/value This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.

An enhanced Modelling approach for warehouse sharing platform system designing problem

With the increasing importance of sustainability, warehouse sharing arises as a possible way to improve the logistics system efficiency. This paper studied the warehouse sharing platform systems (WSPS) and proposed an enhanced modelling approach for the WSPS design problem (WSPSDP) using the multi-allocation hub location routing problem framework. New elements, such as inter-warehouse transportation and multi-allocation scheme, were considered compared to the existing WSPS model. Then, an adaptive large neighbourhood decomposition search heuristic was applied to solve our problem. Computational experiments were conducted on different-sized instances to compare the WSPS model without inter-warehouse transportation (WSPSDP-WI) and the WSPS model with single-allocation scheme (WSPSDP-SA). The results suggested that our proposed WSPSDP model is more cost-efficient than the existing WSPS models, reducing 15.38% and 1.43% operation costs compared to the WSPSDP-WI and WSPSDP-SA models. Finally, our proposed WSPSDP model also has the potential to promote the utilisation of existing cheap idle warehouses.

Construction of Hub Location Model Based on Genetic Algorithm

This paper conducts an analysis of the cost composition of the China Railway Express train operation system, taking into account the choice of exit ports and the time value cost of goods during the operation of trains by China Railway Express. A site selection optimization model is then established with the objective of minimizing the total cost of the China Railway Express service network. A genetic algorithm is designed to solve the optimization model. The location model is then addressed using the designed algorithm, leading to the determination of the site selection plan for the central hub. The optimization results of the site selection plan are subsequently verified under different conditions. The node cities of the China Railway Express in China and the source cities of the freight trains are used as the objects of analysis during the process of solving the location model. The findings indicate that selecting Xi’an as the central hub and Chengdu as the sub-hub could result in an 18.1% reduction in the system cost of China Railway Express, while maintaining the advantage of logistics costs, particularly in scenarios where the freight subsidy is canceled. Furthermore, this choice ensures the stability of the logistics cost of the entire system, even in the event of serious congestion.

Facility Location Problem: Modeling Joint Disruptions Using Subordination

We study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103 .

Cooperative supply game and its revenue allocation method considering location of transportation hub

PurposeThis paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.Design/methodology/approachBased on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.FindingsThrough numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.Originality/valueThis paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

An exact method for trilevel hub location problem with interdiction

In this paper, we study the problem of designing a hub network that is robust against deliberate attacks (interdictions). The problem is modeled as a three-level, two-player Stackelberg game, in which the network designer (defender) acts first to locate hubs to route a set of flows through the network. The attacker (interdictor) acts next to interdict a subset of the located hubs in the designer’s network, followed again by the defender who routes the flows through the remaining hubs in the network. We model the defender’s problem as a trilevel optimization problem, wherein the attacker’s response is modeled as a bilevel hub interdiction problem. We study such a trilevel problem on three variants of hub location problems studied in the literature namely: p-hub median problem, p-hub center, and p-hub maximal covering problems. We present a cutting plane based exact method to solve the problem. The cutting plane method uses supervalid inequalities, which is obtained from the solution of the lower level interdiction problem. To solve the lower level hub interdiction problem efficiently, we propose a penalty-based reformulation of the problem. Using the reformulation, we present a branch-and-cut based exact approach to solve the problem efficiently. We conduct experiments to show the computational advantages of the above algorithm. To the best of our knowledge, the cutting plane approach proposed in this paper is among the first exact method to solve trilevel location–interdiction problems. Our computational results show interesting implications of incorporating interdiction risks in the hub location problem.

Intelligent decision modeling for optimizing railway cold chain service networks under uncertainty

Railway cold chain service network design (RCC-SND) aims to optimize the utilization of stations and lines as well as train allocations in a manner that minimizes costs while satisfying the service requirements of shippers. Furthermore, the uncertainties associated with freight demand, transportation costs, quality loss, station handling capacity, and arc capacity make the RCC-SND a complex decision-making problem. To tackle this challenge, we first formulate a Mixed-Integer Nonlinear Programming (MINLP) model to determine hub locations, freight wagon flows, and service frequency. To cope with uncertain parameters with varying degrees of uncertainty incorporated in the model, we extend the problem using fuzzy programming and further convert it to its crisp counterpart. A real-world cases study in Southwest China is performed to validate the proposed model, whose results provide different strategies for decision-makers with varying preferences. There are some main findings: As the number of hubs increases from 5 to 6, a maximum total cost savings of 1.99% can be achieved. Railway operators may opt for different decision preferences, for decisions prioritizing economic efficiency, the cost can decrease by 2.69% compared to deterministic optimization.

Organizational resilience of the airline industry using an Integrated epidemic and airline hub location model with traffic prediction

Organizational resilience of the airline industry using an Integrated epidemic and airline hub location model with traffic prediction

A holistic approach to introducing a light electric freight vehicle (LEFV) system in a historic urban environment: The case of Quito

In this paper we propose a comprehensive approach for introducing a first/last mile system based on Light Electric Freight Vehicles (LEFVs) in the historical centre of Quito (Ecuador). The approach includes four crucial steps for successful launching and establishment of a LEFVs system. The first step considers organizational aspects by proposing a number of alternative cooperative business models. In the second step, the LEFVs fleet mix problem is defined as a Multicriteria Decision Making Problem (MCDM) and solved by the Analytic Network Process (ANP). The third step includes the problem of optimal micro hub location as well as the problem of optimal routing of LEFVs. The micro hub location problem is modelled as a capacitated p-median problem and solved by an exact solution algorithm. The problem of optimal LEFVs routing is defined as the capacitated pickup and delivery with time windows addressed by a web-based optimization tool. The fourth step includes an assessment of the existing policy framework and proposing a set of additional legal instruments for facilitating the LEFVs market uptake. Based on the proposed four-step approach, the first/last mile system is currently being implemented in the historical centre of Quito. The initial results appear promising regarding the success of the new LEFVs first/last mile system.

The Classical p-median Problem as a Surrogate Model in Hub Location

The Classical p-median Problem as a Surrogate Model in Hub Location

Advances in hub location problems: a literature review and research agenda

PurposeOver the past two decades, the hub location domain has witnessed remarkable growth, yet no prior study reviewed and synthesised problem formulation and solution methodologies to address real-life challenges.Design/methodology/approachThe current study conducts a comprehensive bibliometric literature review to develop a thematic framework that describes and presents hub location problems. The work employs cluster, bibliometric, and social network analyses to delve into the essential themes.FindingsKey themes include cooperation, coopetition, sustainability, reshoring, and dynamic demand, contributing to the complex challenges in today’s hub location problems. As the first work in this field, the study serves as a valuable single-source reference, providing scholars and industry practitioners with key insights into the evolution of hub location research, prominent research clusters, influential authors, leading countries, and crucial keywords.Research limitations/implicationsFindings have significant implications since they highlight the current state of hub location research and set the stage for future endeavours. Specifically, by identifying prominent research clusters, scholars can explore promising directions to push the boundaries of knowledge in this area.Originality/valueThis work is a valuable resource for scholars in this domain and offers practical insights for industry practitioners seeking to understand the hub location problems. Overall, the study’s holistic approach provides a solid foundation for advancing future research work in the hub location field.

Some new clique inequalities in four-index hub location models

Hub location problems can be modeled in several ways, one of which is the path-based family of models that make use of four-index variables. Clique inequalities are frequently used to describe solution characteristics for optimization problems with binary variables. In this study, new valid inequalities of the clique type are introduced for the path-based family of hub location models. Some of their properties and corresponding lifting are discussed, and a separation heuristic algorithm is proposed. We also report a set of computational experiments to evaluate the usefulness of the proposals when embedded in a commercial solver.

Round-trip hub location problem

In this paper, we introduce a novel network design for the Hub Location Problem, inspired by the round-trip structure commonly used by transport service providers. Our design integrates spoke nodes assigned to a central hub node, creating round-trips where the hub node serves as the starting point, visits all assigned spoke nodes, and returns to the hub. To enhance transportation services and provide additional redundancy, we introduce a new type of nodes called runaway nodes to the network. The motivation for this research arises from two real-life cases encountered during consultancy projects, underscoring the necessity for an optimized network design in transportation services. To address the proposed problem, we introduce a mixed-integer linear programming (MIP) mathematical model. However, due to the problem's complexity, the feasibility of the MIP model is limited to small-scale instances. To tackle medium and large-scale instances, we introduce two hyper-heuristic approaches based on reinforcement learning. These hyper-heuristic approaches harness the power of reinforcement learning to guide the selection of low-level heuristics and improve solution quality. We conduct extensive computational experiments to evaluate the efficiency and effectiveness of the proposed approaches. The results of our experiments affirm the efficiency of the proposed hyper-heuristic approaches, showcasing their ability to discover high-quality solutions for the Hub Location Problem.

P.033 Electrophysiological signatures of sedation in pediatric patients

Background: Sedation in PICU masks physical exam findings, leading to diagnostic challenges. In adult models, electroencephalography can evaluate the brain’s response to sedation using feedforward connectivity and anteriorization of alpha hubs, proving useful for prognostication. Feasibility of model translation into pediatric population was assessed, with the hypothesis that the same markers of adaptive reconfiguration would correlate with a higher potential for recovering consciousness. Methods: Electroencephalograms from children undergoing sedation were analyzed for strength and direction of functional connectivity using the weighted and directed phase lag index. Target population was refined with an iterative inclusion criteria. We examined relationships between hub location reconfiguration, directed phase lag index, baseline Glasgow Coma Scale, and 3-month post-treatment Glasgow Outcome Scale-Extended. Results: Evaluation of 14 subjects showed promise in children aged 5-18 undergoing sedation with midazolam, dexmedetomidine, and propofol. Further analysis of five subjects revealed a correlation between adaptive reconfiguration during anesthesia and both higher baseline Glasgow Coma Scale and Glasgow Outcome Scale-Extended scores post-treatment. Conclusions: The findings indicate that the functional brain network connectivity model may have diagnostic and prognostic potential regarding children’s consciousness levels. While the initial data is promising, further analysis of six additional cases is pending and deemed essential to thoroughly evaluate the model’s efficacy.