Path relinking approach for multiple allocation hub maximal covering problem

We consider the multiple allocation hub maximal covering problem (MAHMCP): Considering a serviced O–D flow was required to reach the destination optionally passing through one or two hubs in a limited time, cost or distance, what is the optimal way to locate p hubs to maximize the serviced flows? By designing a new model for the MAHMCP, we provide an evolutionary approach based on path relinking. The Computational experience of an AP data set was presented. And a special application on hub airports location of Chinese aerial freight flows between 82 cities in 2002 was introduced.     

Models and solution methods for the uncapacitated r‐allocation p‐hub equitable center problem

Hub networks are commonly used in telecommunications and logistics to connect origins to destinations in situations where a direct connection between each origin–destination (o‐d) pair is impractical or too costly. Hubs serve as switching points to consolidate and route traffic in order to realize economies of scale. The main decisions associated with hub‐network problems include (1) determining the number of hubs (p), (2) selecting the p‐nodes in the network that will serve as hubs, (3) allocating non‐hub nodes (terminals) to up to r‐hubs, and (4) routing the pairwise o‐d traffic. Typically, hub location problems include all four decisions while hub allocation problems assume that the value of p is given. In the hub median problem, the objective is to minimize total cost, while in the hub center problem the objective is to minimize the maximum cost between origin–destination pairs. We study the uncapacitated (i.e., links with unlimited capacity) r‐allocation p‐hub equitable center problem (with) and explore alternative models and solution procedures.     

Integrators’ Air Transport Networks in Europe

Integrators are shipping carriers that control complete air and road delivery networks and offer a wide range of package delivery services. Despite the increasing relevance of small package delivery services in the European air transport market, very little has been written on integrated carriers’ air transport networks on the Old Continent. In this paper we examine the network configurations of DHL, FedEx, TNT, and UPS in terms of hubs, spokes, and market shares. Our results show that integrators operate hub-and-spoke networks. Network indices and centrality measures confirm that their network structures are more similar to those of full-service passenger carriers rather than those of low-cost carriers. However, the nature of their hub-and-spoke systems is different because freight tons, as compared to passengers, are more easily flown along multiple-stop and circular routes. As a consequence, FedEx, TNT, and UPS operate non-pure star networks with a dominant central hub and a set of intermediate airports acting as stops between the central hub and (usually) one “external airport”. DHL operates a multi-hub architecture, with a main dominant hub in Leipzig and a set of “secondary hubs” that provide several connections to other network nodes. Furthermore, we provide evidence of the most important intra-Europe and long-haul routes for each integrator, showing that DHL seems to have a more developed Europe-Asia connection, and is the only integrator to connect Europe to Sub-Saharan Africa. Finally, we show the high degree of complementarity existing between FedEx and TNT networks and that such complementarity is confirmed also by an analysis of their market shares in the different European sub-markets. Despite the significant level of market concentration, our analysis shows that the recent merger between FedEx and TNT is not expected to significantly modify market equilibrium in Europe.

     

Star p-hub center problem and star p-hub median problem with bounded path lengths

We consider two problems that arise in designing two-level star networks taking into account service quality considerations. Given a set of nodes with pairwise traffic demand and a central hub, we select p hubs and connect them to the central hub with direct links and then we connect each nonhub node to a hub. This results in a star/star network. In the first problem, called the Star p-hub Center Problem, we would like to minimize the length of the longest path in the resulting network. In the second problem, Star p-hub Median Problem with Bounded Path Lengths, the aim is to minimize the total routing cost subject to upper bound constraints on the path lengths. We propose formulations for these problems and report the outcomes of a computational study where we compare the performances of our formulations.     

Uncapacitated single allocation p-hub maximal covering problem

The hub location problem is to find a set of hub nodes on the network, where logistics transportation via the hubs is encouraged because of the cost or distance savings. Each node that has a specified amount of demands can be connected to one of p hubs. The uncapacitated single allocation p-hub maximal covering problem is to maximize the logistics covered, where the logistics of demand is said to be covered if the distance between two nodes is less than or equal to the specified range in consideration of the distance savings between hubs. The aim of our model is to locate the hub, and to allocate non-hub nodes to the located hub nodes; the hub can maximize the demand covered by deadline traveling time. It is presented an integer programming formulation for the new hub covering model, and a computational study based on several instances derived from the CAB (Civil Aeronautics Board) data set. Two heuristics, distance based allocation and volume based allocation methods, are suggested with a computational experiment on the CAB data set. Performances of heuristics are evaluated, and it is shown that good solutions are found in a relatively reasonable computation time for most of instances.     

Forecasting air passenger traffic flow based on the two-phase learning model

The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.

     

Star p-hub median problem with modular arc capacities

We consider the hub location problem, where p hubs are chosen from a given set of nodes, each nonhub node is connected to exactly one hub and each hub is connected to a central hub. Links are installed on the arcs of the resulting network to route the traffic. The aim is to find the hub locations and the connections to minimize the link installation cost. We propose two formulations and a heuristic algorithm to solve this problem. The heuristic is based on Lagrangian relaxation and local search. We present computational results where formulations are compared and the quality of the heuristic solutions are tested.     

Locomotive assignment and freight train scheduling using genetic algorithms

This study considers the movement of freight trains through a passenger rail network, a common occurrence in many developing countries. Passenger trains run according to a fixed schedule while freight trains need to be accommodated and run on the same track, ensuring that they do not interfere with passenger train movements. Operationally, this requires the assignment of a locomotive to a freight rake and then creating a workable schedule. Accordingly, we propose to solve the problem in two phases. In the first phase, we assign locomotives with partial scheduling with the objectives of minimizing total deadheading time and total coupling delay. We use a genetic algorithm to find non‐dominant locomotive assignment solutions and propose a method for evaluating its performance. The solutions are then ranked using two approaches, based on the decision maker's preferences. In the second phase, we select a locomotive assignment solution based on the ranking and find the lower bound on the arrival time of freight trains at their destinations. We use a genetic algorithm again to schedule the freight trains in the passenger rail network, with prescribed locomotive assignment precedence constraints with the objective of minimizing total tardiness. Computational results confirm the efficacy of the proposed method.     

Routing and scheduling decisions in the hierarchical hub location problem

Hubs are facilities that consolidate and disseminate flow in many-to-many distribution systems. The hub location problem considers decisions that include the locations of hubs in a network and the allocations of demand (non-hub) nodes to these hubs. We propose a hierarchical multimodal hub network structure, and based on this network, we define a hub covering problem with a service time bound. The hierarchical network consists of three layers in which we consider a ring-star-star (RSS) network. This multimodal network may have different types of vehicles in each layer. For the proposed problem, we present and strengthen a mathematical model with some variable fixing rules and valid inequalities. Also, we develop a heuristic solution algorithm based on the subgradient approach to solve the problem in more reasonable times. We conduct the computational analysis over the Turkish network and the CAB data sets.     

The q-Ad Hoc Hub Location Problem for Multi-modal Networks

This research proposes a spatial optimization problem over a multi-modal transportation network, termed the q-Ad-hoc hub location problem (AHLP), to utilize alternative hubs in an ad-hoc manner in the wake of a hub outage. The model aims to reorganize the spatial structure of disrupted networks: unaffected hubs are utilized as ad-hoc hubs through which alternative routes connect supply and demand nodes. As a case study, the AHLP is applied to a multi-modal freight transport system connecting international destinations with the United States. The models are utilized to establish a new ranking methodology for critical infrastructure by combining metrics capturing nodal criticality and network resilience and recuperability. The results show that the AHLP is both an effective and practical recovery approach for a hub network to respond to the potential disruptions of hubs and a novel methodology for ranking critical infrastructure.     

A 2-phase algorithm for solving the single allocation p-hub center problem

The single allocation p-hub center problem is an NP-hard location–allocation problem which consists of locating hub facilities in a network and allocating non-hub nodes to hub nodes such that the maximum distance/cost between origin–destination pairs is minimized. In this paper we present an exact 2-phase algorithm where in the first phase we compute a set of potential optimal hub combinations using a shortest path based branch and bound. This is followed by an allocation phase using a reduced sized formulation which returns the optimal solution. In order to get a good upper bound for the branch and bound we developed a heuristic for the single allocation p-hub center problem based on an ant colony optimization approach. Numerical results on benchmark instances show that the new solution approach is superior over traditional MIP-solver like CPLEX. As a result we are able to provide new optimal solutions for larger problems than those reported previously in literature. We are able to solve problems consisting of up to 400 nodes in reasonable time. To the best of our knowledge these are the largest problems solved in the literature to date.     

A genetic algorithm for the uncapacitated single allocation planar hub location problem

Given a set of n interacting points in a network, the hub location problem determines location of the hubs (transfer points) and assigns spokes (origin and destination points) to hubs so as to minimize the total transportation cost. In this study, we deal with the uncapacitated single allocation planar hub location problem (PHLP). In this problem, all flow between pairs of spokes goes through hubs, capacities of hubs are infinite, they can be located anywhere on the plane and are fully connected, and each spoke must be assigned to only one hub. We propose a mathematical formulation and a genetic algorithm (PHLGA) to solve PHLP in reasonable time. We test PHLGA on simulated and real life data sets. We compare our results with optimal solution and analyze results for special cases of PHLP for which the solution behavior can be predicted. Moreover, PHLGA results for the AP and CAB data set are compared with other heuristics.     

Solving the p-hub Median Problem Under Intentional Disruptions Using Simulated Annealing

Hubs are special facilities designed to act as switching, transshipment and sorting points in various distribution systems. Since hub facilities concentrate and consolidate flows, disruptions at hubs could have large effects on the performance of a hub network. In this paper, we have formulated the multiple allocation p-hub median problem under intentional disruptions as a bi-level game model. In this model, the follower’s objective is to identify those hubs the loss of which would most diminish service efficiency. Moreover, the leader’s objective is to identify the set of hubs to locate in order to minimize expected transportation cost while taking normal and failure conditions into account. We have applied two algorithms based on simulated annealing to solve the defined problem. In addition, the algorithms have been calibrated using the Taguchi method. Computational experiments on different instances indicate that the proposed algorithms would be efficient in practice.     

Hub network design problem in the presence of disruptions

The main issue in p-hub median problem is locating hub facilities and allocating spokes to those hubs in order to minimize the total transportation cost. However hub facilities may fail occasionally due to some disruptions which could lead to excessive costs. One of the most effective ways to hedge against disruptions especially intentional disruptions is designing more reliable hub networks. In this paper, we formulate the multiple allocation p-hub median problem under intentional disruptions by a bi-level model with two objective functions at the upper level and a single objective at the lower level. In this model, the leader aims at identifying the location of hubs so that minimize normal and worst-case transportation costs. Worst-case scenario is modeled in the lower level where the follower’s objective is to identify the hubs that if lost, it would mostly increase the transportation cost. We develop two multi-objective metaheuristics based on simulated annealing and tabu search to solve the problem. Computational results indicate the viability and effectiveness of the proposed algorithms for exploring the non-dominated solutions.     

HubLocator: an exact solution method for the multiple allocation hub location problem

HubLocator is a new branch-and-bound procedure for the uncapacitated multiple allocation hub location problem. An existing optimal method developed by Klincewicz (Location Sci. 4 (1996) 173) is based on dual ascent and dual adjustment techniques applied to a disaggregated model formulation. These techniques have already successfully been used to solve the closely related simple plant location problem. However, due to the specific structure of the problem at hand, the success of these techniques in reducing the computational effort is rather restricted. Therefore, HubLocator additionally considers an aggregated model formulation enabling us to significantly tighten the lower bounds. Upper bounds which satisfy complementary slackness conditions for some constraints are constructed and improved by means of a simple heuristic procedure. Computational experiments demonstrate that optimal solutions for problems with up to 40 nodes can be found in a reasonable amount of time.Scope and purposeGround and air transportation networks, postal delivery networks, and computer networks are often configured as hub-and-spoke systems. Traffic between two locations is not transported directly between these locations, but routed via particular switching or consolidation points called hubs. Due to increased traffic on linkages between hubs, larger vehicles can be used or the capacity of existing vehicles can be utilized more efficiently, resulting in smaller per unit transportation costs. The exploitation of scale economies as a result of the reduced number of linkages, which have to be operated in a hub-and-spoke system, compared to a fully interconnected network is an important advantage of this type of system.Designing hub-and-spoke networks deals with the selection of hubs from a given set of potential locations and the routing of traffic. We consider a special type of such a hub location problem and adapt a successful technique developed to find an optimal solution for the well-known simple plant location problem.     

Trading hubs construction in electricity markets using evolutionary algorithms

The trading hubs construction problem for electricity markets under locational marginal prices is considered. Given historical prices for all nodes of the electricity grid and for all market participants over a sufficiently long period of time, the problem is to choose a required number of node clusters (hubs) and to assign market participants to hubs so as to minimize the deviation of hub prices from the prices of participants under certain constraints. In view of problem complexity, two evolutionary algorithms are proposed: a genetic algorithm and a hybrid local search heuristic. It is proved that the proposed genetic algorithm converges to optimum almost surely. The algorithms are tested and compared on the real-life data. The structure of the fitness landscapes is analyzed using multiple restarts of the local search and the behavior of the evolutionary algorithms is explained on the basis of this analysis.     

An improved mixed integer program for single allocation hub location problems with stepwise cost function

Recently, a new model for the uncapacitated single allocation p‐hub median problem was defined, which uses a more realistic cost structure. Instead of measuring the transport costs as a linear function of the volume, integer variables for the number of used vehicles are introduced. This leads to a more precise model if the number of vehicles is low and capacity utilization plays a major role. We will introduce a new mixed integer program formulation of the problem that uses fewer variables but more constraints. This study shows its numerical advantages.     

Hub location for time definite transportation

Time definite motor carriers provide very reliable scheduled truck transportation service between specified terminals. They provide service competitive with airfreight carriers over continental-scale distances at a much lower cost. This paper provides time definite models for multiple allocation p-hub median problems and hub arc location problems. Service levels are imposed by limiting the maximum travel distance via the hub network for each origin–destination pair. Computational results are presented to demonstrate the effects of the time definite service levels on practical network design for truck transportation in North America.     

A tabu-search based heuristic for the hub covering problem over incomplete hub networks

Hub location problems deal with finding the location of hub facilities and with the allocation of demand nodes to these located hub facilities. In this paper, we study the single allocation hub covering problem over incomplete hub networks and propose an integer programming formulation to this end. The aim of our model is to find the location of hubs, the hub links to be established between the located hubs, and the allocation of non-hub nodes to the located hub nodes such that the travel time between any origin–destination pair is within a given time bound. We present an efficient heuristic based on tabu search and test the performance of our heuristic on the CAB data set and on the Turkish network.     

On the selection of hub airports for an airline hub-and-spoke system

We consider the 1-stop multiple allocation p-hub median problem. We formulate the problem as a p-median problem and propose a branch-and-bound algorithm and a greedy-type heuristic algorithm. We report computational results for problems with airline passenger interactions between 25 US cities in 1970 evaluated by the Civil Aeronautics Board. For further investigation, we made computational experiments with some random data. The obtained results also show that the proposed algorithms work better than the well-known nested-dual algorithm, particularly for relatively small problems.