Profit Maximizing Hub Location Problems

The final publication is available at Elsevier via https://doi.org/10.1016/j.omega.2018.05.016 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we study profit maximizing hub location problems. We formulate mathematical models determining the location of hubs, designing the hub networks, and routing the demand in order to maximize profit. The profit is calculated by summing the total revenue minus total cost. Total cost includes the total transportation cost, the installation cost of hubs, and the cost of operating hub links. We consider all possible allocation strategies: multiple allocation, single allocation, and r-allocation. As an extension, for each allocation strategy, we also model the cases in which direct connections between non-hub nodes are allowed. To test and evaluate the performances of the proposed models, we use two well-known data sets from the literature. We analyze the resulting hub networks under various different parameter settings.Natural Sciences and Engineering Research Council of Canada [RGPIN-2015-05548

Collection Center Location with Equity Considerations in Reverse Logistics Networks

This is an Accepted Manuscript of an article published by Taylor & Francis in INFOR: Information Systems and Operational Research on 2014-11-01, available online: http://dx.doi.org/10.3138/infor.52.4.157In this paper, we study a collection center location problem with equity considerations within reverse logistics network design. The aim of the problem is to determine the locations and the capacities of the collection centers through the planning horizon. For each time period, the decisions to be made include the location and the capacities of the collection centers, the amounts of products to send from each generation point to each collection center, and the amounts of products to send from each collection center to each firm. The problem has three objectives. The first one is to minimize total cost, the second one is to ensure equity among different firms, and the third is to provide steady flow of products to each firm along the planning horizon. The problem is modeled as a multi-objective mixed integer programming formulation. An implementation of the problem in Turkey within the context of waste electrical and electronic equipment collection is presented. Sensitivity analyses are conducted to observe the effect of changes in the problem parameters on the solutions. The analyses include changes in the fixed costs and container capacities, changes in the amount of supply and changes in the growth rate. In addition, the solution potential of the model and value of using a multi-period model as opposed to using a static one are investigated

The design of capacitated intermodal hub networks with different vehicle types

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.trb.2016.01.011 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this study, we allow using alternative transportation modes and different types of vehicles in the hub networks to be designed. The aim of the problem is to determine the locations and capacities of hubs, which transportation modes to serve at hubs, allocation of non-hub nodes to hubs, and the number of vehicles of each type to operate on the hub network to route the demand between origin-destination pairs with minimum total cost. Total cost includes fixed costs of establishing hubs with different capacities, purchasing and operational costs of vehicles, transportation costs, and material handling costs. A mixed-integer programming model is developed and a variable neighborhood search algorithm is proposed for the solution of this problem. The heuristic algorithm is tested on instances from the Turkish network and CAB data set. Extensive computational analyzes are conducted in order to observe the effects of changes in various problem parameters on the resulting hub networks. (C) 2016 Elsevier Ltd. All rights reserved.Scientific and Technological Research Council of Turkey (TUBITAK) [111M553

Recent advances in location analysis

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.cor.2015.04.013 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Introduction to a special issue of Computers & Operations Research dedicated to recent advances in location analysis

Benders Decomposition for Profit Maximizing Hub Location Problems with Capacity Allocation

This paper models capacity allocation decisions within profit maximizing hub location problems to satisfy demand of commodities from different market segments. A strong deterministic formulation of the problem is presented and two exact algorithms based on a Benders reformulation are described to solve large-size instances of the problem. A new methodology is developed to strengthen the Benders optimality cuts by decomposing the subproblem in a two-phase fashion. The algorithms are enhanced by the integration of improved variable fixing techniques. The deterministic model is further extended by considering uncertainty associated with the demand to develop a two-stage stochastic program. To solve the stochastic version, a Monte-Carlo simulation-based algorithm is developed that integrates a sample average approximation scheme with the proposed Benders decomposition algorithms. Novel acceleration techniques are presented to improve the convergence of the algorithms proposed for the stochastic version. The efficiency and robustness of the algorithms are evaluated through extensive computational experiments. Computational results show that large-scale instances with up to 500 nodes and three demand segments can be solved to optimality, and that the proposed algorithms generate cuts that provide significant speedups compared to using Pareto-optimal cuts. The proposed two-phase methodology for solving the Benders subproblem as well as the variable fixing and acceleration techniques can be used to solve other discrete location and network design problems

Modeling congestion and service time in hub location problems

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.apm.2017.10.033 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we present a modeling framework for hub location problems with a service time limit considering congestion at hubs. Service time is modeled taking the traveling time on the hub network as well as the handling time and the delay caused by congestion at hubs into account. We develop mixed-integer linear programming formulations for the single and multiple allocation versions of this problem. We further extend the multiple allocation model with a possibility of direct shipments. We test our models on the well-known AP data set and analyze the effects of congestion and service time on costs and hub network design. We introduce a measure for the value of modeling congestion and show that not considering the effects of congestion may result in increased costs as well as in building infeasible hub networks

Spatial Analysis of Single Allocation Hub Location Problems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11067-015-9311-9Hubs are special facilities that serve as switching, transshipment and sorting nodes in many-to-many distribution systems. Flow is consolidated at hubs to exploit economies of scale and to reduce transportation costs between hubs. In this article, we first identify general features of optimal hub locations for single allocation hub location problems based on only the fundamental problem data (demand for travel and spatial locations). We then exploit this knowledge to develop a straightforward heuristic methodology based on spatial proximity of nodes, dispersion and measures of node importance to delineate subsets of nodes likely to contain optimal hubs. We then develop constraints for these subsets for use in mathematical programming formulations to solve hub location problems. Our methodology can also help narrow an organization's focus to concentrate on more detailed and qualitative analyses of promising potential hub locations. Results document the value of including both demand magnitude and centrality in measuring node importance and the relevant tradeoffs in solution quality and time.Turkish Academy of Science

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

The design of single allocation incomplete hub networks

The hub location problem deals with finding the location of hub facilities and allocating the demand nodes to these hub facilities so as to effectively route the demand between any origin-destination pair. In the extensive literature on this challenging network design problem, it has widely been assumed that the subgraph induced by the hub nodes is complete. Relaxation of this basic assumption constitutes the starting point of the present work. In this study, we provide a uniform modeling treatment to all the single allocation variants of the existing hub location problems, under the incomplete hub network design. No network structure other than connectivity is imposed on the induced hub network. Within this context, the single allocation incomplete p-hub median, the incomplete hub location with fixed costs, the incomplete hub covering, and the incomplete p-hub center network design problems are defined, and efficient mathematical formulations for these problems with O(n3) variables are introduced. Computational analyses with these formulations are presented on the various instances of the CAB data set and on the Turkish network.Hub location Network design p-hub median Hub covering p-hub center