Hub Location Research Articles

Scheduling of transportation fleet based on the customer’s priority in a hub location problem

Scheduling of transportation fleet based on the customer’s priority in a hub location problem

A parallel adaptive memory algorithm for the capacitated modular hub location problem

The capacitated modular hub location problem is a realistic variant of the popular hub location problem arising from the design of telecommunications networks. Given a set of demand nodes, the problem consists in selecting a subset of nodes to represent hubs and assigning the rest of the nodes to the hub nodes, such that the transportation cost is minimized while satisfying the capacity constraints. The adaptive memory algorithm is a hybrid evolutionary heuristic that uses a central memory to store blocks of solutions. At each iteration, the recombination operator uses these solution blocks to create an offspring solution. In this paper, we present a parallel adaptive memory algorithm for the capacitated modular hub location problem that stores both solution blocks and complete solutions in a shared memory for the creation of an offspring. Other distinguishing features of our proposed algorithm include specially designed recombination and mutation operators for search diversification, an effective tabu search procedure for search intensification, and parallel computing for global optimization. Extensive computational results on three well-known data sets of 170 benchmark instances show that the proposed algorithm competes very favorably with the state-of-the-art heuristics from the literature. In particular, it finds 115 improved best-known solutions (for more than 67% of the cases). Furthermore, we analyze the key algorithmic components and shed lights on their impact on the proposed algorithm.

Multiple allocation hub location with service level constraints for two shipment classes

In this paper, we study a hub network design problem arising in the context of a third-party logistics (3PL) service provider, which acts as an intermediary between shippers and carriers. A 3PL service provider usually caters to different classes of shipments that require different levels of service, e.g. two-day delivery, next-day delivery etc. We, therefore, study the problem under stochastic demand from two classes of shipments, with one class receiving priority over the other in service at the hubs to maintain the different service levels required by them. To this end, we present two models for designing a capacitated hub network with a service level constraint, defined using the distribution of time spent at hubs, for each shipment class. The models seek to design the hub network at the minimum total cost, which includes the total fixed cost of equipping open hubs with sufficient processing capacity and the variable transportation costs. The network of hubs, given their locations, is thus modeled as spatially distributed priority queues. The resulting model is challenging to solve, for which we propose a cutting plane-based exact solution method.

A hierarchical hub location model for the integrated design of urban and rural logistics networks under demand uncertainty.

This paper contributes to the integrated design issue of urban and rural logistics networks under demand uncertainty. A hierarchical hub location model is proposed, which minimizes the expected total system cost by optimizing the locations, number and capacities of "urban-town‒village" hierarchical logistics hubs. The interactions among the logistics hubs and among the hub‒and‒spoke connections, as well as the hub capacity constraints are explicitly considered in the presence of logistics demand uncertainty. A demand scenario‒based branch‒and‒Benders‒cut algorithm is developed to solve the proposed model. A case study of Jiangling urban‒rural region in Hubei province of China is conducted for the illustration of the model and solution algorithm. The results generated by the proposed algorithm are benchmarked against those obtained by GUROBI solver and the practical scheme being currently implemented in the region. The results showed that the proposed methodology can greatly improve the efficiency of the urban‒rural logistics system in terms of expected total system cost. It is important to explicitly model the demand uncertainty, otherwise a significant decision bias may emerge. The proposed algorithm outperforms the GUROBI solver in terms of problem size solved and computational time.

Stochastic green profit-maximizing hub location problem

This article proposes a two-stage stochastic profit-maximizing hub location problem (HLP) with uncertain demand. Additionally, the model incorporates several carbon regulations, such as carbon tax policy (CTP), carbon cap-and-trade policy (CCTP), carbon cap policy (CCP), and carbon offset policy (COP). In the proposed models, an enhanced sample average approximation (ESAA) method was used to obtain a suitable number of scenarios. To cluster similar samples, k-means clustering and self-organizing map (SOM) clustering algorithms were embedded in the ESAA. The L-shaped algorithm was employed to solve the model inside the ESAA method more efficiently. The proposed models were analyzed using the well-known Australian Post (AP) data set. Computational experiments showed that all of the carbon regulations could reduce overall carbon emissions. Among carbon policies, CCTP could achieve better economic results for the transportation sector. The results also demonstrated that the SOM clustering algorithm within the ESAA method was superior to both k-means inside ESAA and classical SAA algorithms according to the %gap and standard deviation measures. In addition, the results showed that the L-shaped algorithm performed better than the commercial solver in large-scale instances.

Hierarchical Multimodal Hub Location Problem with Carbon Emissions

The increasing demand for multimodal freight transportation makes the expansion of new secondary hubs necessary. The carbon tax has also received attention from various sectors due to the development requirements of green transportation. Therefore, this study proposed a multimodal transport hub location problem, where carbon emissions are specifically considered to achieve green strategic planning. A mixed integer non-linear programming model was formulated to determine the location of newly-built secondary hubs, the distribution of cargo flow and the modes of the transportation links. A tangible adaptive genetic algorithm (AGA) is designed to effectively search for the optimal solution. The result of the secondary hub location based on Ningbo Port shows that the additional secondary hub brought a 41.46% increase in cargo flow, a 2.2% saving in transportation time and a 2.35% saving in total cost. Furthermore, the sensitivity analysis of the carbon tax reveals that a higher carbon tax increases the transport time, and more secondary hubs are needed to reduce the average cargo transport volume and save the total cost.

An alternative approach to address uncertainty in hub location

An alternative approach to address uncertainty in hub location

Hub and Distribution Center Location for a Third-Party Logistics service Provider: A Case Study at Tehran

The rapid growth of the transportation industry and the advance of the e-commerce have opened opportunities for boom delivery services companies. Although, intense competition between shipping companies has obliged many to amend their shipment networks. In this setting, we aim to provide high-quality shipping services for a third-party logistics company in Iran. Currently, in the selected company, one hub is in the west part of Tehran that gives services to all customers. There are some difficulties in shipping services to the eastern part of Tehran city. Therefore, in this paper, we find the location of another hub center to streamline the shipping process. Moreover, to promote our service levels, some Distribution Centers (DCs) are going to be set up. Due to the lack of shipment data like pick up and delivery data, we tried to find the locations of the hub and the DCs based on Tehran population's data. We clustered 122 regions of Tehran based on population, economic index, accessibility to the internet, and the number of business units. Since some remote regions (which are not in the same vicinity) belong to the same cluster, we defined a relative distance criterion to avoid making remote regions in the same cluster. In each cluster, a fixed or movable DC can be set up to service the regions of its cluster. Moreover, to find the best candidate locations for the hub, each zone of Tehran was assessed based on some criteria like land cost, accessibility to the highways, and distance to its nearest bus terminal. Based on these criteria, some zones have been dominated by others, and the remains were considered as candidate locations in a hub location model. By considering the DCs as spokes in a hub and spoke model, the optimal location for the eastern hub establishment was determined.

Rethinking Postsecondary Access and Engagement for Low-income Adult Learners Through a Community Hub Partnership Approach

This paper draws upon a case study of a campus-community partnership program in Ontario that delivers tuition-free college courses to low-income adult learners in community hub locations. By co-locating college classrooms in existing neighbourhood gathering places (i.e., a community centre and a public library), our research explores whether integrating college capacity and resources in community hub locations can help increase the accessibility of post-secondary education. In doing so, we address a gap in the research in exploring how community hubs provide a support structure that can help boost the motivation of low-income adult learners and better facilitate their pathway to a post-secondary education. Drawing upon a thematic analysis of interview data, we (a) analyze partners’ perspectives on the community hub–based approach in bolstering the accessibility of higher education, (b) reflect on the process of campus-community engagement underpinning the partnership structure, and (c) critically assess the efficacy of the community hub model in connecting learners with an educational pathway.

3D Visualisation of Navigation Catheters for Endovascular Procedures Using a 3D Hub and Fiber Optic RealShape Technology: Phantom Study Results

Fiber Optic RealShape (FORS) is a new technology that visualises the full three dimensional (3D) shape of guidewires using an optical fibre embedded in the device. Co-registering FORS guidewires with anatomical images, such as a digital subtraction angiography (DSA), provides anatomical context for navigating these devices during endovascular procedures. The objective of this study was to demonstrate the feasibility and usability of visualising compatible conventional navigation catheters, together with the FORS guidewire, in phantom with a new 3D Hub technology and to understand potential clinical benefits. The accuracy of localising the 3D Hub and catheter in relation to the FORS guidewire, was evaluated using a translation stage test setup and a retrospective analysis of prior clinical data. Catheter visualisation accuracy and navigation success was assessed in a phantom study where 15 interventionists navigated devices to three pre-defined targets in an abdominal aortic phantom using an Xray or computed tomography angiography (CTA) roadmap. Additionally, the interventionists were surveyed about the usability and potential benefits of the 3D Hub. The location of the 3D Hub and catheter along the FORS guidewire was detected correctly 96.59% of the time. During the phantom study, all 15 interventionists successfully reached the target locations 100% of the time and the error in catheter visualisation was 0.69 mm. The interventionists agreed or strongly agreed that the 3D Hub was easy to use and the greatest potential clinical benefit over FORS is in offering interventionists choice over which catheter they used. This set of studies has shown that FORS guided catheter visualisation, enabled by a 3D Hub, is accurate and easy to use in a phantom setting. Further evaluation is needed to understand the benefits and limitations of the 3D Hub technology during endovascular procedures.

The impact of different cooperation strategies on the efficiency of rural parcel deliveries

In rural areas, parcel deliveries are characterized by a lack of drop density and high-mileage delivery. As a result, rural areas are difficult to make profitable for each individual carrier company. The VIL project “Rural Parcel Delivery” investigates the effect of 2 cooperation strategies: (1) Parcel delivery outsourced to a neutral third party from a common distribution hub and (2) Setting up a shared network of collection points between carriers. MooV has developed a model of the parcel delivery chain to assess the impact of these cooperation strategies on the efficiency of parcel deliveries as well as the location of the common hub. Given the obtained input data for the Walloon region, joint delivery in rural areas results in a reduction in costs (-35%) and CO2 emissions (-60%). Large differences exist between carriers based on the type of their delivery network and parcel volume.

Greening urban logistics: Introducing a method to evaluate optimised decoupling hub locations

Sustainable urban logistics has become challenging over the course of the last decades due to logistics sprawl and increased demands of goods in urban areas. To overcome this problem, a fine-grained network of decoupling hubs, separating suppliers and customers in a spacial and timely manner, are needed. The paper at hand introduces a model of how to calculate optimal decoupling hub locations within urban areas with the goal that inhabitants walk or cycle to the next decoupling hub and do not take a motorised vehicle. The study at hand uses the northern district of Zurich as unit of analysis and shows that by using the developed model, 80% of the inhabitants need to walk less than 250 metres to their next decoupling hub, which is within the radius of 100 metres to the next public transport stop. This supports the integration of picking up or dropping off deliveries while commuting.

Study on the Impact Assessment of Mobility Hubs Location in an Automated Driving Society

Study on the Impact Assessment of Mobility Hubs Location in an Automated Driving Society

Infrastructure planning for ride-hailing services using shared autonomous electric vehicles

Ride-hailing services using shared autonomous electric vehicles (SAEVs) are soon expected to serve a significant portion of the public transportation demands, especially in metropolitan cities. The transportation demands will arise randomly across all transportation nodes and during all hours of operation. This spatio-temporal nature of the transportation demand together with the constraints of trip length, battery size and consumption rate, and power network will guide the actions of the SAEVs. These actions are pair-with-demand, park, charge, and relocate, which in turn will determine the necessary infrastructure plan for ride-hailing services. The plan will comprise SAEV fleet size, charging hub locations across the nodes of the transportation network, and the number of charging stations in each hub. We formulate a profit-maximizing mixed integer linear model comprising a space-time-battery representation of the transportation network and a DC power flow representation of the power network. The model, intended for developing infrastructure plans for ride-hailing services, is robustified to accommodate randomness in travel and electric power demand. The model solution is demonstrated via an illustrative numerical case study representing the Tampa Bay region of Florida, USA. The results show how the infrastructure plans and their profitability depend on the demand fulfillment goal, battery size, battery consumption rate, and power network configurations. A linear relaxation of some of the integer variables of the model to reduce computational time is discussed.

The Integration of Multimodal Networks: The Generalized Modal Split and Collaborative Optimization of Transportation Hubs

Integrated transportation is one of the most important methods to encourage the modal shift from car to public transportation (PT). However, as most cities have an existing multimodal network, it is difficult to expand the current networks by building more PT routes. Thus, integrating different modes through the optimization of hubs is a cost-efficient way to promote sustainable mobility. This paper develops a bilevel multimodal network design problem based on the collaborative optimization of urban transportation hubs. The upper-level problem is formulated as a mixed-integer nonlinear program to achieve a modal shift from congested subnetworks to underutilized subnetworks to realize a balanced use of the entire network. The decision variables are classified into location-based (hub locations) and route-based (route layouts and frequency setting) ones. The lower-level problem is a generalized modal split/traffic assignment problem (GMS/TAP), which captures the mode choices of all modes in the path set. The GMS/TAP is formulated as a nonlinear optimization problem (NLP) and is solved using a hybrid method of the successive average (MSA) algorithm. A hybrid genetic search with advanced diversity control (HGSADC) is developed to solve the bilevel model, where the exploration of the search space is expanded using the biased fitness function and diversification mechanism. The solution properties of the hybrid MSA and HGSADC are demonstrated in two modified nine-node networks. The model performance is illustrated in a real-size network in Jianye district, Nanjing. 9.2% decrease of travel time, 25.7% increase of service level, and a significant modal shift from car to PT are obtained.

Method for Determining the Location of Highway Passenger Transportation Hubs Using POI Data and the Dijkstra Algorithm in Large City

In order to reduce the impact of highway passenger transportation hubs on urban internal traffic operations and improve the efficiency of external travel, a method to determine the location of such hubs based on POI data and the Dijkstra algorithm is proposed. Firstly, the process of connecting the starting point of travel in the city to the urban road node and the highway exit is analyzed. Taking the minimum total time cost as the objective function, the mathematical model of the location method of a highway passenger transportation hub is established. Then, an algorithm for solving the mathematical model is proposed. In the solution method, the concept of “equivalent distance” is introduced into the Dijkstra algorithm to calculate the shortest path for passengers traveling on the urban road network. Weighted random numbers are generated to simulate the choice of highway exits when intercity coaches carrying passengers leave the city. Finally, Zhengzhou City is taken as an example for application analysis. The calculation is carried out using a script written in Python. The calculation results of the proposed method are compared with the results of the P -median model and the current locations of hubs. The results show that the proposed method consumes less time than the P -median method. The total time cost is also lower, which can reduce the time for passengers to leave the city. The location of the highway passenger transportation hub is closer to the periphery of the city and highway nodes, which conforms to the engineering practice and development trend of hub location so as to reduce its impact on urban internal traffic.

A mean-CVaR approach to the risk-averse single allocation hub location problem with flow-dependent economies of scale

The hub location problem (HLP) is a fundamental facility planning problem with various applications in transportation, logistics, and telecommunication systems. Due to strategic nature of the HLP, considering uncertainty and the associated risks is of high practical importance in designing hub networks. This paper addresses a risk-averse single allocation HLP, where the traffic volume between the origin–destination (OD) pairs is considered to be uncertain. The uncertainty in demands is captured by a finite set of scenarios, and a flow-dependent economies of scale scheme is used for transportation costs, modeled as a piece-wise concave function of flow on all network arcs. The problem is cast as a risk-averse two-stage stochastic problem using mean-CVaR as the risk measure, and a novel solution approach combining Benders decomposition and scenario grouping is proposed. An extensive set of computational experiments is conducted to study the effect of different input parameters on the optimal solution, and to evaluate the performance of the proposed solution algorithm. Managerial insights are derived and presented based on the obtained results.

MSC Senescence-Related Genes Are Associated with Myeloma Prognosis and Lipid Metabolism-Mediated Resistance to Proteasome Inhibitors.

Complex carcinogenic mechanisms and the existence of tumour heterogeneity in multiple myeloma (MM) prevent the most commonly used staging system from effectively interpreting the prognosis of patients. Since the microenvironment plays an important role in driving tumour development and MM occurs most often in middle-aged and elderly patients, we hypothesize that ageing of bone marrow mesenchymal stem cells (BM-MSCs) may be associated with the progression of MM. In this study, we collected the transcriptome data on MM from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differentially expressed genes in both senescent MSCs and MM tumour cells were considered relevant damaged genes. GO and KEGG analyses were applied for functional evaluation. A PPI network was constructed to identify hub genes. Subsequently, we studied the damaged genes that affected the prognosis of MM. Least absolute shrinkage and selection operator (lasso) regression was used to identify the most important features, and a risk model was created. The reliability of the risk model was evaluated with the other 3 GEO validation cohorts. In addition, ROC analysis was used to evaluate the novel risk model. An analysis of immune checkpoint-related genes, tumour immune dysfunction and exclusion (TIDE), and immunophenotypic scoring (IPS) were performed to assess the immune status of risk groups. pRRophetic was utilized to predict the sensitivity to administration of chemotherapeutic agents. We identified that MAPK, PI3K, and p53 signalling pathways were activated in both senescent MSCs and tumour cells, and we also located hub genes. In addition, we constructed a 14-gene prognostic risk model, which was analysed with the ROC and validated in different datasets. Further analysis revealed significant differences in predicted risk values across the International Staging System (ISS) stage, sex, and 1q21 copy number. A high-risk group with higher immunogenicity was predicted to have low proteasome inhibitor sensitivity and respond poorly to immunotherapy. Lipid metabolism pathways were found to be significantly different between high-risk and low-risk groups. A nomogram was created by combining clinical data, and the optimization model was further improved. Finally, real-time qPCR was used to validate two bortezomib-resistant myeloma cell lines, and the test confirmed that 10 genes were detected to be expressed in resistant cell lines with the same trend as in the high-risk cohort compared to nonresistant cells. Fourteen genes related to ageing in BM-MSCs were associated with the prognosis of MM, and by combining this genotypic information with clinical factors, a promising clinical prognostic model was established.

Strategic plan for China's air high-speed rail express freight network and its carbon reduction potential.

The booming development of express freight demand is eager for innovative transportation modes to satisfy the demand for capacity, timeliness, and low carbon emissions. China is working to create an air-HSR fast freight mode based on the continuous expansion of its high-speed rail (HSR) network in an effort to relieve the burden on air freight and lower the emissions associated with it. In this study, a novel freight network design model for the air-HSR planning problem is developed. It combines the centrality of nodes, carbon emissions, and time efficiency to resolve the trade-offs between time efficiency and carbon emissions. This research supports the planning decision and the viability of the air-HSR mode. The hub location decision and initial air-HSR express freight network plan for China are obtained by using the traversal search algorithm and real case data. We then further examine the carbon emission reduction potential that the air-HSR intermodal express network can generate compared with the airline network. However, since the real problem is more complicated and involves many stakeholders, the carbon reduction potential is calculated based on some simplified theoretical assumptions to obtain specific quantitative results. The results indicated that the planned air-HSR network could provide express service nationwide in 24h, while the carbon emission was reduced by 2.08 million tons, which is a 57.35% reduction compared to the original airline network, and saved the entire transportation sector 498.7 million RMB. Additionally, carbon emissions have decreased across each city pair, notably in the central and eastern areas where there is a substantial possibility for carbon emission reduction.

Intermodal transportation hub location optimization with governments subsidies under the Belt and Road Initiative

Driven by globalization, the COVID-19 outbreak has severely impacted global transport and logistics systems. To better cope with this globalization crisis, the Belt and Road Initiative (BRI)—based on the concept of cooperation—is more important than ever in the post-pandemic era. Taking the BRI as the background, we design an intermodal hub-and-spoke network to provide reference for governments along BRI routes to improve their cross-border transportation system and promote economic recovery. In the context of the BRI, local governments at different nodes have incentives to subsidize hub construction and/or rail transportation to boost economic development. We consider co-opetition behavior among different levels of government caused by subsidies in this intermodal hub location problem, which we call the intermodal hub location problem based on government subsidies. We establish a two-stage mixed-integer programming model. In the first stage, local governments provide subsidies, then the central government decides the number and location of hubs. In the second stage, freight carriers choose the optimal route to transport the goods. To solve the model, we design an optimization method combining a population-based algorithm using contest theory. The results show that rail subsidies are positively correlated with construction subsidies but are not necessarily related to the choice of hubs. Compared with monomodal transportation, intermodal transportation can reduce costs more effectively when there are not too many hubs and the cost of different modes of transportation varies greatly. The influences of local government competition and hub construction investment on network design and government subsidies are further examined.