Browsing by Subject "Multiple allocation"

Now showing 1 - 4 of 4

Open Access
Allocation Strategies in Hub Networks
(Elsevier, 2011-06-11) Yaman, H.
In this paper, we study allocation strategies and their effects on total routing costs in hub networks. Given a set of nodes with pairwise traffic demands, the p-hub median problem is the problem of choosing p nodes as hub locations and routing traffic through these hubs at minimum cost. This problem has two versions; in single allocation problems, each node can send and receive traffic through a single hub, whereas in multiple allocation problems, there is no such restriction and a node may send and receive its traffic through all p hubs. This results in high fixed costs and complicated networks. In this study, we introduce the r-allocation p-hub median problem, where each node can be connected to at most r hubs. This new problem generalizes the two versions of the p-hub median problem. We derive mixed-integer programming formulations for this problem and perform a computational study using well-known datasets. For these datasets, we conclude that single allocation solutions are considerably more expensive than multiple allocation solutions, but significant savings can be achieved by allowing nodes to be allocated to two or three hubs rather than one. We also present models for variations of this problem with service quality considerations, flow thresholds, and non-stop service.
Open Access
A capacitated hub location problem under hose demand uncertainty
(Elsevier, 2017) Meraklı, M.; Yaman, H.
In this study, we consider a capacitated multiple allocation hub location problem with hose demand uncertainty. Since the routing cost is a function of demand and capacity constraints are imposed on hubs, demand uncertainty has an impact on both the total cost and the feasibility of the solutions. We present a mathematical formulation of the problem and devise two different Benders decomposition algorithms. We develop an algorithm to solve the dual subproblem using complementary slackness. In our computational experiments, we test the efficiency of our approaches and we analyze the effects of uncertainty. The results show that we obtain robust solutions with significant cost savings by incorporating uncertainty into our problem.
Open Access
Hub location proplems under polyhedral demand uncertainty
(2015-07) Meraklı, Merve
Hubs are points of consolidation and transshipment in many-to-many distribution systems that bene t from economies of scale. In hub location problems, the aim is to locate hub facilities such that each pairwise demand is satis ed and the total cost is minimized. The problem usually arises in the strategic planning phase prior to observing actual demand values. Hence incorporating robustness into hub location decisions under data uncertainty is crucial for achieving a reliable hub network design. In this thesis, we study hub location problems under polyhedral demand uncertainty. We consider uncapacitated multiple allocation p-hub median problem under hose and hybrid demand uncertainty and capacitated multiple allocation hub location problem under hose demand uncertainty. We propose mixed integer linear programming formulations and devise several exact solution algorithms based on Benders decomposition in order to solve large-scale problem instances. Computational experiments are performed on instances of three benchmark data sets from the literature.
Open Access
Robust intermodal hub location under polyhedral demand uncertainty
(Elsevier, 2016) Meraklı, M.; Yaman, H.
In this study, we consider the robust uncapacitated multiple allocation p-hub median problem under polyhedral demand uncertainty. We model the demand uncertainty in two different ways. The hose model assumes that the only available information is the upper limit on the total flow adjacent at each node, while the hybrid model additionally imposes lower and upper bounds on each pairwise demand. We propose linear mixed integer programming formulations using a minmax criteria and devise two Benders decomposition based exact solution algorithms in order to solve large-scale problems. We report the results of our computational experiments on the effect of incorporating uncertainty and on the performance of our exact approaches.