Airport Infrastructure in the Shrinking City: Planning for Smart Decline in Cleveland’s Regional Airport System and Its Role in a Dynamic Urban Future

Cleveland, while having experienced some growth and regeneration in the 21st Century, still experiences some of the salient characteristics of the shrinking city. It continues to slowly lose population. Metropolitan-level economic growth remains elusive. Its status as a shrinking city and metropolitan region has consequences for its systems of infrastructure, especially its regional system of airports. This study illustrates how shrinking cities theory applies to Cleveland\u27s airport system. Namely, the airport system has experienced challenges associated with maintaining substantial levels of flight operations in addition to having experienced certain financial challenges since 2000. This study then theorizes how a plan for smart decline in the airport system can mitigate some of these challenges while also supporting dynamic land uses throughout the Cleveland region

Factory Gate Pricing: An Analysis of the Dutch Retail Distribution

Factory Gate Pricing (FGP) is a relatively new phenomenon in retail distribution. Under FGP, products are no longer delivered at the retailer distribution center, but collected by the retailer at the factory gates of the suppliers. Owing to both the asymmetry in the distribution networks (the supplier sites greatly outnumber the retailer distribution centers) and the better inventory and transport coordination mechanisms, this is likely to result in high savings. A mathematical model was used to analyze the benefits of FGP for a case study in the Dutch retail sector. Extensive numerical results are presented to show the effect of the orchestration shift from supplier to retailer, the improved coordination mechanisms, and sector-wide cooperation.supply chain management;factory gate pricing;retail distribution

Three essays on urban freight transport: models and tools for effective city logistics projects

The main purpose of these three years of research, summarized in this thesis, was to investigate the obstacles to the development of the city logistics initiatives by seeking solutions to overcome them through model and framework coming from management and transportation engineering. In particular, following a first analysis of a collection of European projects and a systematic analysis of scientific literature, three main gaps in city logistics have been identified: the lack of the stakeholders’ involvement, the need for data sharing platforms to overcome the current lack of data and the need to define city logistics solutions within the urban ecosystem, making consistent design choices coherently with what is already existing in terms of infrastructures, rules and stakeholders in the context. From these three gaps, three main research questions have arisen: (RQ1) Is it possible to support stakeholders in analysing CL solutions fitting their necessities applying some already existing and consolidate decision-making methods? (RQ2) Is it possible to define a database platform in which it is possible to collect, consult and update as many existing data as possible regarding urban freight transport? (RQ3) How is it possible to optimize city logistics infrastructures in a harmonious and coherent way with respect to the entire city logistics ecosystem? To answers to the research questions, a collection of articles is illustrated in this thesis work. From time to time different methodologies are used and illustrated, derived from the field of management and transport engineering, these different methodologies, such as the Systematic Literature Review, the House of Quality, a framework for building a data sharing platform, the city logistics Ecosystem and a decision-making support model (based on both a covering model and a Monte Carlo simulation) are described in detail in the various chapters of the thesis. In this dissertation work for the first time, the main obstacles to the development of city logistics initiatives, that are the lack of involvement of stakeholders, the lack of data, and the lack of an ecosystem vision of urban transport, have been identified and addressed at the same time. Even if literature sometimes offers some possible solutions to these gaps, few are simple to understand for those who work in the urban freight transport industry, easy to apply and replicable. Both in identifying the gap and in seeking solutions, the solutions showed in this thesis sought to address to those who work in the industry, mainly carriers, retailers, shop owners and public administration representatives, trying to combine scientific research with the search for solutions that can be implemented in practice as requested by such a practical research topic. For this reason, each proposed solution and methodology in this thesis has been implemented and experimented using as a case study the city of Bergamo (and testing its replicability in other European cities such as Saint-Etienne, Luxemburg and Amsterdam). In particular, the initial experience in the “Bergamo Logistica” project, part of the Bergamo 2.035 smart city research program, gave me the opportunity to understand the main critical issues found by the main actors who work in this field (i.e., carriers, couriers, retailers and institutions), to confirm some evidences that I found in the theory (i.e., main research gaps which originates the research questions) and to search for solutions that could both solve research gaps and optimize the daily logistics activities of the operators.The main purpose of these three years of research, summarized in this thesis, was to investigate the obstacles to the development of the city logistics initiatives by seeking solutions to overcome them through model and framework coming from management and transportation engineering. In particular, following a first analysis of a collection of European projects and a systematic analysis of scientific literature, three main gaps in city logistics have been identified: the lack of the stakeholders’ involvement, the need for data sharing platforms to overcome the current lack of data and the need to define city logistics solutions within the urban ecosystem, making consistent design choices coherently with what is already existing in terms of infrastructures, rules and stakeholders in the context. From these three gaps, three main research questions have arisen: (RQ1) Is it possible to support stakeholders in analysing CL solutions fitting their necessities applying some already existing and consolidate decision-making methods? (RQ2) Is it possible to define a database platform in which it is possible to collect, consult and update as many existing data as possible regarding urban freight transport? (RQ3) How is it possible to optimize city logistics infrastructures in a harmonious and coherent way with respect to the entire city logistics ecosystem? To answers to the research questions, a collection of articles is illustrated in this thesis work. From time to time different methodologies are used and illustrated, derived from the field of management and transport engineering, these different methodologies, such as the Systematic Literature Review, the House of Quality, a framework for building a data sharing platform, the city logistics Ecosystem and a decision-making support model (based on both a covering model and a Monte Carlo simulation) are described in detail in the various chapters of the thesis. In this dissertation work for the first time, the main obstacles to the development of city logistics initiatives, that are the lack of involvement of stakeholders, the lack of data, and the lack of an ecosystem vision of urban transport, have been identified and addressed at the same time. Even if literature sometimes offers some possible solutions to these gaps, few are simple to understand for those who work in the urban freight transport industry, easy to apply and replicable. Both in identifying the gap and in seeking solutions, the solutions showed in this thesis sought to address to those who work in the industry, mainly carriers, retailers, shop owners and public administration representatives, trying to combine scientific research with the search for solutions that can be implemented in practice as requested by such a practical research topic. For this reason, each proposed solution and methodology in this thesis has been implemented and experimented using as a case study the city of Bergamo (and testing its replicability in other European cities such as Saint-Etienne, Luxemburg and Amsterdam). In particular, the initial experience in the “Bergamo Logistica” project, part of the Bergamo 2.035 smart city research program, gave me the opportunity to understand the main critical issues found by the main actors who work in this field (i.e., carriers, couriers, retailers and institutions), to confirm some evidences that I found in the theory (i.e., main research gaps which originates the research questions) and to search for solutions that could both solve research gaps and optimize the daily logistics activities of the operators

Power-Aware Datacenter Networking and Optimization

Present-day datacenter networks (DCNs) are designed to achieve full bisection bandwidth in order to provide high network throughput and server agility. However, the average utilization of typical DCN infrastructure is below 10% for significant time intervals. As a result, energy is wasted during these periods. In this thesis we analyze traffic behavior of datacenter networks using traces as well as simulated models. Based on the insight developed, we present techniques to reduce energy waste by making energy use scale linearly with load. The solutions developed are analyzed via simulations, formal analysis, and prototyping. The impact of our work is significant because the energy savings we obtain for networking infrastructure of DCNs are near optimal. A key finding of our traffic analysis is that network switch ports within the DCN are grossly under-utilized. Therefore, the first solution we study is to modify the routing within the network to force most traffic to the smallest of switches. This increases the hop count for the traffic but enables the powering off of many switch ports. The exact extent of energy savings is derived and validated using simulations. An alternative strategy we explore in this context is to replace about half the switches with fewer switches that have higher port density. This has the effect of enabling even greater traffic consolidation, thus enabling even more ports to sleep. Finally, we explore a third approach in which we begin with end-to-end traffic models and incrementally build a DCN topology that is optimized for that model. In other words, the network topology is optimized for the potential use of the datacenter. This approach makes sense because, as other researchers have observed, the traffic in a datacenter is heavily dependent on the primary use of the datacenter. A second line of research we undertake is to merge traffic in the analog domain prior to feeding it to switches. This is accomplished by use of a passive device we call a merge network. Using a merge network enables us to attain linear scaling of energy use with load regardless of datacenter traffic models. The challenge in using such a device is that layer 2 and layer 3 protocols require a one-to-one mapping of hardware addresses to IP (Internet Protocol) addresses. We overcome this problem by building a software shim layer that hides the fact that traffic is being merged. In order to validate the idea of a merge network, we build a simple mere network for gigabit optical interfaces and demonstrate correct operation at line speeds of layer 2 and layer 3 protocols. We also conducted measurements to study how traffic gets mixed in the merge network prior to being fed to the switch. We also show that the merge network uses only a fraction of a watt of power, which makes this a very attractive solution for energy efficiency. In this research we have developed solutions that enable linear scaling of energy with load in datacenter networks. The different techniques developed have been analyzed via modeling and simulations as well as prototyping. We believe that these solutions can be easily incorporated into future DCNs with little effort

Workload Prediction for Efficient Performance Isolation and System Reliability

In large-scaled and distributed systems, like multi-tier storage systems and cloud data centers, resource sharing among workloads brings multiple benefits while introducing many performance challenges. The key to effective workload multiplexing is accurate workload prediction. This thesis focuses on how to capture the salient characteristics of the real-world workloads to develop workload prediction methods and to drive scheduling and resource allocation policies, in order to achieve efficient and in-time resource isolation among applications. For a multi-tier storage system, high-priority user work is often multiplexed with low-priority background work. This brings the challenge of how to strike a balance between maintaining the user performance and maximizing the amount of finished background work. In this thesis, we propose two resource isolation policies based on different workload prediction methods: one is a Markovian model-based and the other is a neural networks-based. These policies aim at, via workload prediction, discovering the opportune time to schedule background work with minimum impact on user performance. Trace-driven simulations verify the efficiency of the two pro- posed resource isolation policies. The Markovian model-based policy successfully schedules the background work at the appropriate periods with small impact on the user performance. The neural networks-based policy adaptively schedules user and background work, resulting in meeting both performance requirements consistently. This thesis also proposes an accurate while efficient neural networks-based pre- diction method for data center usage series, called PRACTISE. Different from the traditional neural networks for time series prediction, PRACTISE selects the most informative features from the past observations of the time series itself. Testing on a large set of usage series in production data centers illustrates the accuracy (e.g., prediction error) and efficiency (e.g., time cost) of PRACTISE. The superiority of the usage prediction also allows a proactive resource management in the highly virtualized cloud data centers. In this thesis, we analyze on the performance tickets in the cloud data centers, and propose an active sizing algorithm, named ATM, that predicts the usage workloads and re-allocates capacity to work- loads to avoid VM performance tickets. Moreover, driven by cheap prediction of usage tails, we also present TailGuard in this thesis, which dynamically clones VMs among co-located boxes, in order to efficiently reduce the performance violations of physical boxes in cloud data centers

Essays on Shipment Consolidation Scheduling and Decision Making in the Context of Flexible Demand

This dissertation contains three essays related to shipment consolidation scheduling and decision making in the presence of flexible demand. The first essay is presented in Section 1. This essay introduces a new mathematical model for shipment consolidation scheduling for a two-echelon supply chain. The problem addresses shipment coordination and consolidation decisions that are made by a manufacturer who provides inventory replenishments to multiple downstream distribution centers. Unlike previous studies, the consolidation activities in this problem are not restricted to specific policies such as aggregation of shipments at regular times or consolidating when a predetermined quantity has accumulated. Rather, we consider the construction of a detailed shipment consolidation schedule over a planning horizon. We develop a mixed-integer quadratic optimization model to identify the shipment consolidation schedule that minimizes total cost. A genetic algorithm is developed to handle large problem instances. The other two essays explore the concept of flexible demand. In Section 2, we introduce a new variant of the vehicle routing problem (VRP): the vehicle routing problem with flexible repeat visits (VRP-FRV). This problem considers a set of customers at certain locations with certain maximum inter-visit time requirements. However, they are flexible in their visit times. The VRP-FRV has several real-world applications. One scenario is that of caretakers who provide service to elderly people at home. Each caretaker is assigned a number of elderly people to visit one or more times per day. Elderly people differ in their requirements and the minimum frequency at which they need to be visited every day. The VRP-FRV can also be imagined as a police patrol routing problem where the customers are various locations in the city that require frequent observations. Such locations could include known high-crime areas, high-profile residences, and/or safe houses. We develop a math model to minimize the total number of vehicles needed to cover the customer demands and determine the optimal customer visit schedules and vehicle routes. A heuristic method is developed to handle large problem instances. In the third study, presented in Section 3, we consider a single-item cyclic coordinated order fulfillment problem with batch supplies and flexible demands. The system in this study consists of multiple suppliers who each deliver a single item to a central node from which multiple demanders are then replenished. Importantly, demand is flexible and is a control action that the decision maker applies to optimize the system. The objective is to minimize total system cost subject to several operational constraints. The decisions include the timing and sizes of batches delivered by the suppliers to the central node and the timing and amounts by which demanders are replenished. We develop an integer programing model, provide several theoretical insights related to the model, and solve the math model for different problem sizes

A Dual Ascent Procedure for Large Scale Uncapacitated Network Design

The fixed-charge network design problem arises in a variety of problem contexts including transportation, communication, and production scheduling.We develop a family of dual ascent algorithms for this problem. This approach generalizes known ascent procedures for solving shortest path, plant location,Steiner network and directed spanning tree problems. Our computational results for several classes of test problems with up to 500 integer and 1.98 million continuous variables and constraints shows that the dual ascent procedure and an associated drop-add heuristic generates solutions that, in almost all cases, are guaranteed to be within 1 to 3 percent of optimality. Moreover, the procedure requires no more than 150 seconds on an IBM 3083 computer. The test problems correspond to dense and sparse networks,including some models arising in freight transport

Modelación En Programación Matemática Y Resolución Del Problema De Localización-Ruteo En Logística Urbana

The implementation of urban distribution centers near to city centers to allow freight consolidation is a widely extended initiative worldwide, seeking to improve traffic congestion and quality of life in downtown, among others. This paper considers the problem of locating urban distribution centers and proposes an exact method, based on integer linear programming for strategic, tactical and operational decision-making. The aim is to solve, in an integer manner, location, sizing and operation (vehicle routing) problems in these logistics platforms. The model is validated using real-data taken from the city of SaintÉtienne, France. Computational experiments are also carried out in order to compare the proposed model with existing procedures from the literature. Results show the efficiency and effectiveness of the proposed model and its applicability in real decision-making for medium sized data sets