In today’s extremely competitive marketplace, firms are facing the need to meet or exceed increasing customer expectations while cutting costs to stay competitive in a global market. To develop competitive advantage in this business climate, companies must make informed decisions regarding their supply chain. In recent years, supply chain networks have received increasing attention among companies. The decision makers confront the network design problem in different situations. In order to make decisions, especially in strategic supply chain management, decision makers must have a holistic view of all the components. Supply chain network design, particular facility location problems, is one of the most complex strategic decision problems in supply chain management The aim of this dissertation is to make an inquiry about the facility location problems and related issues in supply chain and logistics management, and the use of modelling approaches to solve these problems. The methodology is to construct a review protocol by forming a review panel, and developing a detailed search strategy with clear inclusion and exclusion criteria. In addition, the measurement for evaluating the quality of studies is presented with a strategy for extracting data and synthesising the methodologies. The search results show the background of the facility location problems, the importance and the basic questions of these problems. The taxonomy of facility location problems with eighteen factors is presented. The basic static and deterministic problems in facility location including the covering, centre, median and fixed charge problems are discussed. Also, the extension of facility location problems comprises of location-allocation, multi-objective, hierarchical, hub, undesirable and competitive problems. In terms of uncertainty, dynamic, stochastic and robust facility location problems are presented. Finally, strengths and weaknesses of different modelling approaches are discussed; importantly, gaps from the review process are indentified. Recommendations of future research are described; and the facility location problem to be addressed by the proposed research is shown. In addition, contributions of the proposed facility location problem are illustrated.

A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. While utilizing the branch and bound framework, machine and deep learning models enhance either the computational efficiency or performance of the model. This thesis examines how imitating different variable selection strategies of classical MIP solvers behave on a state-of-the-art deep learning model. A recently developed deep learning algorithm is used in this thesis, which represents the branch and bound state as a bipartite graph. This graph serves as the input to a graph network model, which determines the variable in the MIP on which branching occurs. This thesis compares how imitating different classical branching strategies behaves on different algorithm outputs and, most importantly, time span. More specifically, this thesis conducts an empirical study on a MIP known as the facility location problem (FLP) and compares the different methods for imitation. This thesis shows that the deep learning algorithm can outperform the classical methods in terms of time span. More specifically, imitating the branching strategies resulting in small branch and bound trees give rise to a more rapid performance in finding the global optimum. Lastly, it is shown that a smaller embedding size in the network model is preferred for these instances when looking at the trade-off between variable selection and time cost. / En ny typ av maskin och djupinlärningsmodeller har utvecklats inom villkors optimering, specifikt för så kallade blandade heltalsproblem (MIP). Dessa modeller hämtar inspiration från tidigare lösningsmetoder, främst en heuristisk som kallas “branch and bound”. Genom att använda “branch and bound” ramverket förbättrar maskin och djupinlärningsmodeller antingen beräkningshastigheten eller prestandan hos modellen. Denna uppsats undersöker hur imitation av olika strategier för val av variabler från klassiska MIP-algoritmer beter sig på en modern djupinlärningsmodell. I denna uppsats används en nyligen utvecklad djupinlärningsalgoritm som representerar “branch and bound” tillståndet som en bipartit graf. Denna graf används som indata till en “graph network” modell som avgör vilken variabel i MIP-problemet som tas hänsyn till. Uppsatsen jämför hur imitation av olika klassiska “branching” strategier påverkar olika algoritmutgångar, framför allt, tidslängd. Mer specifikt utför denna uppsats en empirisk studie på ett MIP-problem som kallas för “facility location problem” (FLP) och jämför imitationen av de olika metoderna. I denna uppsats visas det att denna djupinlärningsalgoritm kan överträffa de klassiska metoderna när det gäller tidslängd. Mer specifikt ger imitation av “branching” strategier som resulterar i små “branch and bound” träd upphov till en snabbare prestation vid sökning av den globala optimala lösningen. Slutligen visas det att en mindre inbäddningsstorlek i nätverksmodellen föredras i dessa fall när man ser på avvägningen mellan val av variabler och tidskostnad.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Jessica L. Heier Stamm / Coordination between humanitarian organizations is critical during the response effort to a disaster, as coordinating aid improves efficiency, reduces duplication of efforts, and ultimately leads to better outcomes for beneficiaries. One particular challenge arises when temporary facilities must be established post-disaster due to the destruction of buildings. For example, the 2015 Nepal earthquakes created a need for the placement of over 4,000 temporary learning facilities after several school buildings were damaged or destroyed. It is important that humanitarians coordinate well to fill these needs efficiently and effectively, while maintaining equity among beneficiaries in the affected areas. This means ensuring that enough facilities are provided in a timely manner, and are distributed fairly to all in need. The goals of this thesis are to study coordination strategies focusing primarily on the placement of temporary educational facilities for children following a disaster. This research also aims to gather useful data by surveying active humanitarians in order to better understand their decisions made in the field. This work uses the results of this survey, along with publicly available data published after the 2015 Nepal earthquakes to create an agent-based simulation model, and uses the Nepal case study to demonstrate the efficacy of the model framework. This research finds that organizations' initial location of operation can greatly impact the number of facilities they are collectively able to establish, the geographic disparity across the region, and the organizations' utilization. Specifically, while focusing efforts on the districts with the most need is most efficient and effective, a more uniform approach yields a more equitable response. This work also finds that there can be a trade-off between overall effectiveness and the number of partnerships established in the field. These findings show a need for further study into the intricacies of coordination between humanitarian workers. This author advocates for the use of information sharing mechanisms among practitioners, as well as further utilization of agent-based modeling as a means of studying the complex nature of disaster response. Specifically there is a need to further study educational needs as a logistical problem, and strategies for solving the post-disaster facility location problem.

Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

The reliability issue of the infrastructure systems has become one of the major concerns of the system operators. This dissertation is a collection of four published and working papers that address the specific reliable design and operations problems from three different application settings: transportation/telecommunications network, distribution network, and power plant. In these four projects, key random factors like site disruption and uncertain demand are explicitly considered and proper research tools including stochastic programming, robust optimization, and variants of robust optimization are applied to formulate the problems based on which the important and challenging modelling elements (nonlinear congestion, disruption caused demand variation, etc.) can be introduced and studied. Besides, for each of the optimization models, we also develop advanced solution algorithms that can solve large-scale instances within a short amount of time and devise comprehensive numerical experiments to derive insights. The modelling techniques and solution methods can be easily extended to study reliability issues in other applications.

The exploding HD video streaming traffic calls for deploying content servers deeper inside network operators infrastructures. Telco-CDN are new content distribution services that are managed by Internet Service Providers (ISP). Since the network operator controls both the infrastructure and the content delivery overlay, it is in position to engineer Telco-CDN so that networking resources are optimally utilized. In this thesis, we focus on the optimal resource placement in Telco-CDN. We first investigated the placement of application components in Telco-CDN. Popular services like Facebook or Twitter, with a size in the order of hundreds of Terabytes, cannot be fully replicated on a single data-center. Instead, the idea is to partition the service into smaller components and to locate the components on distinct sites. It is the same and unique method for Telco-CDN operators. We addressed this k-Component Multi-Site Placement Problem from an optimization standpoint. We developed linear programming models, designed approximation and heuristic algorithms to minimize the overall service delivery cost. Thereafter, we extend our works to address the problem of optimal video place- ment for Telco-CDN. We modeled this problem as a k-Product Capacitated Facility Location Problem, which takes into account network conditions and users¿ prefer- ences. We designed a genetic algorithm in order to obtain near-optimal performances of such "push" approach, then we implemented it on the MapReduce framework in order to deal with very large data sets. The evaluation signifies that our optimal placement keeps align with cooperative LRU caching in term of storage efficiency although its impact on network infrastructure is less severe. We then explore the caching decision problem in the context of Information Cen- tric Network (ICN), which could be a revolutionary design of Telco-CDN. In ICN, routers are endowed with caching capabilities. So far, only a basic Least Recently Used (LRU) policy implemented on every router has been proposed. Our first contri- bution is the proposition of a cooperative caching protocol, which has been designed for the treatment of large video streams with on-demand access. We integrated our new protocol into the main router software (CCNx) and developed a platform that automatically deploys our augmented CCNx implementation on real machines. Ex- periments show that our cooperative caching significantly reduces the inter-domain traffic for an ISP with acceptable overhead. Finally, we aim at better understanding the behavior of caching policies other than LRU. We built an analytical model that approximates the performance of a set of policies ranging from LRU to Least Frequently Used (LFU) in any type of network topologies. We also designed a multi-policy in-network caching, where every router implements its own caching policy according to its location in the network. Compared to the single LRU policy, the multi-caching strategy considerably increases the hit- ratio of the in-network caching system in the context of Video-on-Demand application. All in one, this thesis explores different aspects related to the resource placement in Telco-CDN. The aim is to explore optimal and near-optimal performances of various approaches.

This thesis covers three main issues in content delivery with a focus on placement algorithms of replica servers and replica contents. In a content delivery system, the location of replicas is very important as perceived by a quotation: Closer is better. However, considering the costs incurred by replication, it is a challenge to deploy replicas in a cost-effective manner. The objective of our work is to optimally select the location of replicas which includes sites for replica server deployment, servers for replica contents hosting, and en-route caches for object caching. Our solutions for corresponding applications are presented in three parts of the work, which makes significant contributions for designing scalable, reliable, and efficient systems for Internet content delivery. In the first part, we define the Fault-Tolerant Facility Allocation (FTFA) problem for the placement of replica servers, which relaxes the well known Fault-Tolerant Facility Location (FTFL) problem by allowing an integer (instead of binary) number of facilities per site. We show that the problem is NP-hard even for the metric version, where connection costs satisfy the triangle inequality. We propose two efficient algorithms for the metric FTFA problem with approximation factors 1.81 and 1.61 respectively, where the second algorithm is also shown to be (1.11,1.78)- and (1,2)-approximation through the proposed inverse dual fitting technique. The first bi-factor approximation result is further used to achieve a 1.52-approximation algorithm and the second one a 4-approximation algorithm for the metric Fault-Tolerant k-Facility Allocation problem, where an upper bound of facility number (i. e. k) applies. In the second part, we formulate the problem of QoS-aware content replication for parallel access in terms of combined download speed maximization, where each client has a given degree of parallel connections determined by its QoS requirement. The problem is further converted into the metric FTFL problem and we propose an approximation algorithm which is implemented in a distributed and asynchronous manner of communication. We show theoretically that the cost of our solution is no more than 2F* + RC*, where F* and C* are two components of any optimal solution while R is the maximum number of parallel connections. Numerical experiments show that the cost of our solutions is comparable (within 4% error) to the optimal solutions. In the third part, we establish mathematical formulation for the en-route web caching problem in a multi-server network that takes into account all requests (to any server) passing through the intermediate nodes on a request/response path. The problem is to cache the requested object optimally on the path so that the total system gain is maximized. We consider the unconstrained case and two QoS-constrained cases respectively, using efficient dynamic programming based methods. Simulation experiments show that our methods either yield a steady performance improvement (in the unconstrained case) or provide required QoS guarantees. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1461921 / Thesis (Ph.D.) - University of Adelaide, School of Computer Science, 2009

abstract: Due to environmental and geopolitical reasons, many countries are embracing electric vehicles (EVs) as an alternative to gasoline powered automobiles. Other alternative-fuel vehicles (AFVs) powered by compressed gas, hydrogen or biodiesel have also been tested for replacing gasoline powered vehicles. However, since the associated refueling infrastructure of AFVs is sparse and is gradually being built, the distance between recharging points (RPs) becomes a crucial prohibitive attribute in attracting drivers to use such vehicles. Optimally locating RPs will both increase demand and help in developing the refueling infrastructure. The major emphasis in this dissertation is the development of theories and associated algorithms for a new set of location problems defined on continuous network space related to siting multiple RPs for range limited vehicles. This dissertation covers three optimization problems: locating multiple RPs on a line network, locating multiple RPs on a comb tree network, and locating multiple RPs on a general tree network. For each of the three problems, finding the minimum number of RPs needed to refuel all Origin-Destination (O-D) flows is considered as the first objective. For this minimum number, the location objective is to locate this number of RPs to minimize weighted sum of the travelling distance for all O-D flows. Different exact algorithms are proposed to solve each of the three algorithms. In the first part of this dissertation, the simplest case of locating RPs on a line network is addressed. Scenarios include single one-way O-D pair, multiple one-way O-D pairs, round trips, etc. A mixed integer program with linear constraints and quartic objective function is formulated. A finite dominating set (FDS) is identified, and based on the existence of FDS, the problem is formulated as a shortest path problem. In the second part, the problem is extended to comb tree networks. Finally, the problem is extended to general tree networks. The extension to a probabilistic version of the location problem is also addressed. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

In this paper, a two-stage stochastic facility location problem integrated with inven- tory and recourse decisions is studied and solved. This problem is inspired by an industrial supply chain design problem of a large retail chain with slow-moving prod- ucts. Uncertainty is expressed by a discrete and finite set of scenarios. Recourse actions can be taken after the realization of random demands. Location, inventory, transportation, and recourse decisions are integrated into a mixed-integer program with an objective minimizing the expected total cost. A dual heuristic procedure is studied and embedded into the sample average approximation (SAA) method. The computation experiments demonstrate that our combined SAA with dual heuristic algorithm has a similar performance on solution quality and a much shorter compu- tational time. / Thesis / Master of Applied Science (MASc)

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