GRASP Metaheuristic for Multiple Allocation p-Hub Location Problem

Hub Location Problems (HLPs), belonging to the field of location theory, have been area of much research over the past two decades. This is due, in large measure, to the applications of hub and spoke networks in practice. Among the most classical versions of HLPs are p-hub location problems (p-HLPs), p-hub location problems are one of the most well studied variants of hub location literature. The primary goal of these models is to allocate p hub facilities in a hub and spoke network so as to concentrate flows (demands) to benefit from economies of scale in cost of transportation. The application of p-hub networks extends beyond the field of telecommunication and includes air freight systems, postal delivery systems and airline industries and several transportation related systems. p-HLPs constitute a challenging class of HLPs and are known to be NP-hard. Several solution approaches have been developed from exact solutions using integer programming techniques to the development of metaheuristics. Even though metaheuristic algorithms cannot guarantee optimality, given complexity of large scale HLPs, they are being used for solving these problems. In this thesis, we focus on the multiple allocation uncapacitated p-hub location problem. Four solution algorithms will be proposed to this problem for solving the Australian Postal (AP) data instances. We start with a very simple algorithm and continue with more complicated one in order to present an efficient high quality feasible solution and to assess the impact of the quality of initial feasible solution on local improvement phase. Computational results from the different algorithms were compared to exact solutions to track the efficiency of the proposed algorithms

Stochastic hub and spoke networks

Transportation systems such as mail, freight, passenger and even telecommunication systems most often employ a hub and spoke network structure since correctly designed they give a strong balance between high service quality and low costs resulting in an economically competitive operation. In addition, consumers are increasingly demanding fast and reliable transportation services, with services such as next day deliveries and fast business and pleasure trips becoming highly sought after. This makes finding an efficient design of a hub and spoke network of the utmost importance for any competing transportation company. However real life situations are complicated, dynamic and often require responses to many different fixed and random events. Therefore modeling the question of what is an optimal hub and spoke network structure and finding an optimal solution is very difficult. Due to this, many researchers and practitioners alike make several assumptions and simplifications on the behavior of such systems to allow mathematical models to be formulated and solved optimally or near optimally within a practical timeframe. Some assumptions and simplifications can however result in practically poor network design solutions being found. This thesis contributes to the research of hub and spoke networks by introducing new stochastic models and fast solution algorithms to help bridge the gap between theoretical solutions and designs that are useful in practice. Three main contributions are made in the thesis. First, in Chapter 2, a new formulation and solution algorithms are proposed to find exact solutions to a stochastic p-hub center problem. The stochastic p-hub center problem is about finding a network structure, where travel times on links are stochastic, which minimizes the longest path in the network to give fast delivery guarantees which will hold for some given probability. Second, in Chapter 3, the stochastic p-hub center problem is looked at using a new methodological approach which gives more realistic solutions to the network structures when applied to real life situations. In addition a new service model is proposed where volume of flow is also accounted for when considering the stochastic nature of travel times on links. Third, in Chapter 4, stochastic volume is considered to account for capacity constraints at hubs and, de facto, reduce the costs embedded in excessive hub volumes. Numerical experiments and results are conducted and reported for all models in all chapters which demonstrate the efficiency of the new proposed approaches