Facility Location Planning Under Disruption

Facility Location Problems (FLPs) such as the Uncapacitated Facility Location (UFL) and the Capacitated Facility Location (CFL) along with the k-Shortest Path Problem (k-SPP) are important research problems in managing supply chain networks (SCNs) and related operations. In UFL, there is no limit on the facility serving capacity while in CFL such limit is imposed. FLPs aim to find the best facility locations to meet the customer demands within the available capacity with minimized facility establishment and transportation costs. The objective of the (k-SPP) is to find the k minimal length and partial overlapping paths between two nodes in a transport network graph. In the literature, many approaches are proposed to solve these problems. However, most of these approaches assume totally reliable facilities and do not consider the failure probability of the facilities, which can lead to notably higher cost. In this thesis, we investigate the reliable uncapacitated facility location (RUFL)and the reliable capacitated facility location (RCFL) problems, and the k-SPP where potential facilities are exposed to disruption then propose corresponding solution approaches to efficiently handle these problems. An evolutionary learning technique is elaborated to solve RUFL. Then, a non-linear integer programming model is introduced for the RCFL along with a solution approach involving the linearization of the model and its use as part of an iterative procedure leveraging CPLEX for facility establishment and customer assignment along with a knapsack implementation aiming at deriving the best facility fortification. In RUFL and RCFL, we assume heterogeneous disruption with respect to the facilities, each customer is assigned to primary and backup facilities and a fixed fortification budget allows to make a subset of the facilities totally reliable. Finally, we propose a hybrid approach based on graph partitioning and modified Dijkstra algorithm to find k partial overlapping shortest paths between two nodes on a transport network that is exposed to heterogeneous connected node failures. The approaches are illustrated via individual case studies along with corresponding key insights. The performance of each approach is assessed using benchmark results. For the k-SPP, the effect of preferred establishment locations is analyzed with respect to disruption scenarios, failure probability, computation time, transport costs, network size and partitioning parameters

Graph Partitioning of Transportation Networks under Disruption

This research is concerned with providing a solution capable of treating network complexity and scalability effectively so that it overcomes administrative, environmental and technique boundaries. One good approach dealing with this matter is applying graph partitioning techniques. Graph partitioning is an optimization problem with the aim of dividing a large geographical network into manageable size districts called sub-networks with less complexity in favor of balancing the workload and minimizing the communication among them, with the aim of maximizing their independency as much as possible. Over the past decades various models have been developed in such a way to satisfy a multi-objective problem such as delivery time and managerial cost. In real life, due to inevitable changes during network’s lifetime, it is vital to offer survivability and resilience in the existence of network failure and disruption. Further, it is essential to maintain functionality in critical facilities and high priority connections in the time of crisis. This paper suggests four partitioning techniques namely “Hierarchical recursive progression1^+ “(HRP1^+) and “Hierarchical recursive progression2^+ “(HRP2^+) and their extensions called “HRP1^+control” and “HRP2^+control” to solve the scalability as well as complexity of a network. For this matter, the initial balanced partition is produced on a predefined network. Furthermore two different approaches namely “complete failure update “and “partial failure update” are proposed and demonstrated in the occurrence of network disruption. In sum, the three main objectives of this thesis are as follows: Modeling disruption on logistics networks Assuring and strengthen connectivity in the disrupted network for routing purposes Developing partitioning approaches in favor of generating roughly equal sized and balanced partitions in the disrupted network