Hub Location Problem Research Articles

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.

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

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.

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.

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.

Meeting economic and social viability goals in regional airline schemes through hub-and-spoke network connectivity

AbstractWe examine the viability of regional connectivity schemes by considering both social and economic objectives. In India the scheme is called UDAN (loosely translated affordable air travel) which is designed to include economically backward communities in India into the air travel grid. Using secondary data sources from the airline sector in India, and qualitative interviews of knowledgeable personnel in the airline industry, we demonstrate the importance of hub-and-spoke network design in comparison to point-to-point connectivity for regional connectivity networks. Specifically, we develop Viable Hub Location Problem for Regional Connectivity (VHLPRC) for resilience and sustainability through bilevel optimization with single leader and two independent followers. We test our proposed approach using datasets from USA and India. Our analyses suggest strategically choosing primary hubs and re-routing traffic through regional hubs for long-term commercial viability or survivability of regional connectivity schemes. The introduction of regional hubs had mixed effects. On the positive side, it improved reach, albeit at considerable (hidden) costs. On the negative side, several sub-programs had to be abandoned for a variety of reasons, including lack of demand traffic. The lessons learned from this study inform policy makers, academics, and practicing managers on how to remain viable and sustain operations in regional connectivity schemes. With the introduction of social variables, commercial viability has been shown to face specific real-life challenges. An attempt to help solve these problems is also presented in this paper through risk reduction, capacity augmentation, and by continuing fare subsidies.

Exact Method for Production Hub Location

This study proposes a production hub location (PHL) problem that integrates the classical multiplant lot-sizing and hub location problems. The PHL problem is to determine the location of production facilities, lot-sizing, inventory, hub location, and the distribution of multiple commodities from plants to customers, with an objective to minimize the total production, setup, inventory, hub operating, and transportation costs. The PHL problem applies to manufacturing companies that either have built a hub-and-spoke distribution network or are accessible to such a network through collaborations with other third-party logistics companies. Because the PHL problem is [Formula: see text]-hard, we propose an exact method that integrates dynamic programming and Benders decomposition (DPBD) for solving the problem. The DPBD method is enhanced by exploring several problem properties, such as a multicut reformulation, the generation of Pareto-optimal cuts, a two-stage hub elimination and restoration procedure, and the inclusion of a novel heuristic procedure. We compare the PHL model with several related models theoretically and computationally with newly created benchmark instances. The computational results indicate that the proposed model can reduce the total costs and facilitate better network designs and system decisions, highlighting the value of an integrated approach. We also provide managerial insights into the benefits of integration and show the efficiency of the DPBD method through an extensive number of computational tests. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2023.0339 .

Reliable multiple allocation hub location problem under disruptions

Reliable multiple allocation hub location problem under disruptions

The maximal detour liner shipping hub location problem: Improving the applicability of the p-hub center problem

This paper investigates a novel maximal detour hub location problem arising in liner shipping, referred to as a maximal detour liner shipping hub location problem, which can be regarded as a novel p-hub center problem. Different from conventional formulations of the p-hub center problem, both cost and service can be kept at a good level based on our investigated problem, although economic objectives are not considered in our problem. By considering origin–destination (OD) path based detour and sub-path based detours, we propose three mixed-integer linear programming models for our problem. The proposed formulations with sub-path based detours can be efficiently solved by CPLEX. To efficiently solve our formulation with OD path based detour, we propose an improved Benders decomposition algorithm, where the inspection strategy, the heuristic strategy and the multi-cut strategy are developed for improving algorithm efficiency. Finally, we provide numerical experiments to account for the effectiveness and efficiency of the proposed formulations and algorithm.

Liner shipping hub location and empty container repositioning: Use of foldable containers and container leasing

Considering the influence of economies of scale and ship capacity on empty container repositioning, using foldable containers and container leasing may be effective ways to save the operating costs of liner shipping companies. Considering the minimization of the transportation costs of empty and laden containers, this paper studies a liner shipping hub location problem with empty container repositioning (LSHLPECR) considering foldable containers. We propose a mixed integer linear program (MILP) for the formulation of the LSHLPECR. In terms of the optimal solution of our MILP, this paper further explores the container leasing pricing problem (CLPP), where our aim is to obtain perceived container leasing prices (PCLPs) utilizing the inverse optimization technique. Numerical experiments verify the effectiveness of the studied problems.

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Abstract Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.

Capacitated multiple allocation hub location problems under the risk of interdiction: model formulations and solution approaches

Capacitated multiple allocation hub location problems under the risk of interdiction: model formulations and solution approaches

Variable Neighborhood Search Algorithm for the Single Assignment Incomplete Hub Location Problem with Modular Capacities and Direct Connections

In distribution systems such as airlines and express package delivery, the use of hub-and-spoke networks is common, and flow consolidation at hub facilities is essential for cost reduction. While a constant discount factor is typically used to model cost reduction in interhub links, this paper explores an extension of the incomplete hub location problem with modular capacity that enables direct connections between non-hub nodes. The modified approach, called MHLPDC, aims to locate a set of hub facilities, connect each non-hub node to a hub, and activate hub facility links, access arc links, and direct links between non-hub nodes to minimize network costs. The MHLPDC integrates link activation decisions into the decision-making process and utilizes modular arc costs to model the flow dependence of transportation costs in all arcs. To solve the problem, the paper presents a mixed-integer mathematical programming formulation and heuristic algorithm based on a greedy randomized adaptive search and variable neighborhood search approach. The proposed algorithm produces high-quality solutions, as demonstrated through computational experiments on benchmark instances with up to 40 nodes. Furthermore, a sensitivity analysis of the optimal network structure indicates that increasing the discount factor, by varying hub and access arc capacities as well as the associated variable costs, results in fewer hubs being established and more direct shipments between non-hub nodes being permitted.

Multi-period hub location problem considering polynomial time-dependent demand

This paper presents a bi-objective, nonlinear mathematical model for designing a multi-period hub network, considering a polynomial function for time-dependent transportation demand. The proposed demand function can be used for various applications of the hub network design problem. Hubs’ capacity in the proposed model is considered to be modular. Each module corresponds to a number of servers at hubs. Objectives of the problem include minimizing network costs and maximizing responsiveness by minimizing the sum of the maximum travel times in all time periods. The proposed model provides the best timing for implementing decisions during the planning horizon. To solve the model, NSGA-II, NRGA, PESA-II, and SPEA-II meta-heuristics are used. To compare the performance of the solution algorithms, some multi-objective metrics and statistical tests on the CAB, AP, and TR datasets are applied. We also perform sensitivity analysis on some critical parameters. The sensitivity analysis results show that the network design with economic and responsiveness considerations simultaneously has a higher cost than the network design with only economic considerations.

A heuristic approach to the stochastic capacitated single allocation hub location problem with Bernoulli demands

Hubs are critical components of transportation and distribution systems, and hub networks play a special role in freight and passenger transportation services worldwide. This paper studies a capacitated single allocation hub location problem with Bernoulli demands. Since the origin-destination (OD) demands are stochastic in nature, and the nodes are allocated to hubs before knowing their realized values, the actual total demand allocated to each hub is uncertain. Therefore, demand can exceed the capacity of hubs, rendering a need for outsourcing. The problem is studied under two distinct outsourcing policies, namely the facility and customer outsourcing. Mathematical models are developed for each case as two-stage stochastic programs. Deterministic equivalent formulations are obtained for problems, assuming a homogeneous demand distribution for all OD pairs. A Tabu Search-based algorithm is presented as a solution approach to deal with large problem instances. Extensive computational tests demonstrate the outstanding performance of the developed models and the metaheuristic procedure in terms of solution quality and computational time. The relevance of using stochastic programming approach for the problems is demonstrated via computational results. This study also reports solutions for problems with 200 nodes for the first time.

A GVNS algorithm applied to the single allocation hub location problem with heterogeneous economies of scale

This paper addresses the single allocation hub location problem with heterogeneous economies of scale. This problem consists of selecting the nodes to establish the hubs in order to minimize the total cost of installing the hubs and routing demand flows, assuming that there is an economy of scale that depends on the amount of flow routed in each arc. We proposed a formulation to model the problem and an algorithm based on the General Variable Neighborhood Search (GVNS) meta-heuristic to handle the problem. Computational experiments were performed using instances from the literature with up to 500 nodes. The results show that the GVNS algorithm is effective for solving the instances in terms of runtime and solution quality. The results also show that the proposed formulation outperforms the literature formulation when activating the Benders decomposition method available in the CPLEX solver.

Lagrangean relaxation algorithms for fixed-charge capacitated relay network design

We consider a fixed-charge capacitated relay network design problem (CRNDP), which has widespread applications in the design of telecommunication and transportation networks, where relay points are presented as signal regenerators, truckload driver and/or tractor exchange stations or electrical vehicle recharging station. Our CRNDP integrates the key features of single allocation hub location problem (SAHLP) and capacitated network design problem (CNDP). Typical range restrictions in truckload transportation, electrical vehicles as well as for keeping signal strength in telecommunications, and further limitations of flow on the links of an underlying network in these mediums motivate the use of distance constraints and link capacity constraints in the design of a relay network for this diverse set of applications. To efficiently solve our model, we propose a Lagrangean relaxation algorithm, where the upper bounds are enhanced by a heuristic during the iterations and the lower bound is improved by the introduction of surrogate constraints. The extensive computational studies are conducted to examine the performance of the algorithm and the results show that the solution method we propose can efficiently solve CRNDP and obtain good-quality solutions, even for very large size instances.

Stochastic regional-based profit-maximizing hub location problem: A sustainable overview

Climate change is one of the most important concerns globally, and countries need to improve their transportation infrastructure to handle flows with the least air pollution. In specific cities and regions, carbon emissions may cause in future many issues for people and societies. Although transportation hubs are very helpful in the reduction of transportation costs, they may cause an increase in carbon emissions in some regions, leading to long-term environmental problems. In this study, a model is developed for the profit-maximizing hub location problem that incorporates three pillars of sustainability. The proposed model is not only designed to maximize profit but also to control carbon emissions using a carbon cap policy and to reduce differences in emissions between regions with consideration of population density. This scheme will lead to achieving a sustainable transportation network. A two-stage stochastic programming approach is employed to cope with demand uncertainty. An enhanced sampling based on the self-organizing map method was utilized to cluster scenarios that lead to dealing with small-sized problems. Furthermore, classical Benders decomposition, Pareto-optimal cut Benders decomposition, and L-shaped algorithms are employed to solve the proposed models more efficiently. The proposed models are analyzed using the well-known Turkish Network (TR) data set. Computational results demonstrate that the proposed models help to achieve sustainability. Factors of sustainability such as environmental and social can be achieved by slightly reducing the amount of profit in the economic factor. Furthermore, results show that the L-shaped algorithm with a multi-cut scheme outperforms the commercial solver, classical Benders decomposition, and Pareto-optimal cut Benders decomposition algorithms in large-size instances.