Browsing by Subject "Logistics"
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Item The aerial fleet refueling problem(2001-08) Wiley, Victor Duane; Barnes, John W.The deployment stage of the Aerial Fleet Refueling Problem (AFRP) for Air Mobility Command (AMC), Scott AFB, IL is efficiently solved using a Group Theoretic Tabu Search (GTTS). The GTTS uses the Symmetric Group on n-letters (Sn) and applies it to this problem using the JavaTM ObjectOriented Programming (OOP) language. The GTTS approach is sufficiently robust to be applied to other problem areas at AMC including the employment stage of the AFRP as well as the deployment and employment stages of the Airlift Problem. In the appendices, a brief description of the JavaTM implementation of the Sn, developed as an essential part of this research, is presented.Item Dynamic and Robust Capacitated Facility Location in Time Varying Demand Environments(2010-07-14) Torres Soto, JoaquinThis dissertation studies models for locating facilities in time varying demand environments. We describe the characteristics of the time varying demand that motivate the analysis of our location models in terms of total demand and the change in value and location of the demand of each customer. The first part of the dissertation is devoted to the dynamic location model, which determines the optimal time and location for establishing capacitated facilities when demand and cost parameters are time varying. This model minimizes the total cost over a discrete and finite time horizon for establishing, operating, and closing facilities, including the transportation costs for shipping demand from facilities to customers. The model is solved using Lagrangian relaxation and Benders? decomposition. Computational results from different time varying total demand structures demonstrate, empirically, the performance of these solution methods. The second part of the dissertation studies two location models where relocation of facilities is not allowed and the objective is to determine the optimal location of capacitated facilities that will have a good performance when demand and cost parameters are time varying. The first model minimizes the total cost for opening and operating facilities and the associated transportation costs when demand and cost parameters are time varying. The model is solved using Benders? decomposition. We show that in the presence of high relocation costs of facilities (opening and closing costs), this model can be solved as a special case by the dynamic location model. The second model minimizes the maximum regret or opportunity loss between a robust configuration of facilities and the optimal configuration for each time period. We implement local search and simulated annealing metaheuristics to efficiently obtain near optimal solutions for this model.Item Effectively managing multi-source, Multi-site technology deployments(2011-08) Emanuel, Mark Eugene; Darwin, Thomas Jason, 1966-; Nichols, Steven Parks, 1950-Information Technology infrastructures continue to be dynamic, evolving, and business critical investments for companies of all sizes. Even with moves to virtualize end user computing functions, the evolution of network architectures, mobile computing devices and corporate security requirements will continue to necessitate technology upgrades requiring, at their core, the rudimentary act of placing hardware at specific physical locations on a prescribed timeline. In distributed corporate environments, deploying a range of devices sourced from multiple suppliers into geographically dispersed locations can be a challenge in material management and logistics planning. This Multi-Source, Multi-Site style of deployment is a complex balance of competing timelines where failures to meet delivery targets can have costly impacts that cascade throughout the project. Perturbations in global supply chains, manufacturing schedules, and local shipping capacities drive fluctuations in a supplier's ability to consistently and predictably execute to delivery timelines so it is the task of a deployment Project Manager to interpret a variety supply chain signals and take action to minimize the negative impacts of supply chain challenges. In that effort, the deployment PM will benefit from a structured approach to defining how available supply chain data will be used to help manage expectations, monitor execution, and effect the overall deployment success. In this paper, I present an approach that breaks deployment planning into 3 primary deliverables; the Site Plan, the Data Plan, and the Monitoring Plan. Executing those three plans will drive a PM to understand the supply chain data available to them, translate that data into information useful and understandable by all stakeholders, and monitor the progress of the supply chain against a deployment schedule. In practical terms, those plans culminate in a data mining and data management methodology that can be supported with spreadsheet based dashboards that provide both a fixed Snapshot of the status of the deployment as well as a rolling Timeline of key material movements over the duration of the deployment. The data management approach described here is specifically designed to avoid complex macro development, database queries, or software purchases that may not be available to all Project Managers. Applying the Multi-Source, Multi-Site approach, a PM can gain useful and relevant information from various streams of supply chain data using straightforward spreadsheet manipulations. With a clearer picture of supply chain execution, a PM tasked with a Multi-Source, Multi-Site deployment can better leverage project change control methods to improve their chances of successfully meeting their schedule and cost targets.Item Relay Network Design in Logistics and Telecommunications: Models and Solution Approaches(2011-08-08) Kewcharoenwong, PanitanStrategic network design has significant impacts on the operational performance of transportation and telecommunications industries. The corresponding networks are typically characterized by a multicommodity ow structure where a commodity is defined by a unique origin-destination pair and an associated amount of ow. In turn, multicommodity network design and hub location models are commonly employed when designing strategic networks in transportation and telecommunications applications. In this dissertation, these two modeling approaches are integrated and generalized to address important requirements in network design for truckload transportation and long-distance telecommunications networks. To this end, we first introduce a cost effective relay network design model and then extend this base model to address the specific characteristics of these applications. The base model determines relay point (RP) locations where the commodities are relayed from their origins to destinations. In doing this, we explicitly consider distance constraints for the RP-RP and nonRPRP linkages. In truckload transportation, a relay network (RP-network) can be utilized to decrease drivers' driving distances and keep them within their domiciles. This can potentially help alleviate the high driver turnover problem. In this case, the percentage circuitry, load-imbalance, and link-imbalance constraints are incorporated into the base model to control related performance metrics that are affected by the distance constraints. When compared to the networks from other modeling approaches, the RP-network is more effective in controlling drivers' tour lengths and capable of controlling the empty mileage to low levels without adding a large amount of additional travel distance. In telecommunications, an RP-network can be beneficial in long-distance data transfers where the signals' delity must be improved/regenerated at RPs along their travel paths. For this setting, we extend the base model to include fixed link setup costs and capacities. From our computational results, our models provide better network configuration that is cost effective and facilitates a better service quality (shorter delays and better connectivity). Concerning methodology, we develop effcient exact solution algorithms based on Benders decomposition, Lagrangean decomposition, and Lagrangean relaxation. The performance of the typical solution frameworks are enhanced via numerous accelerating techniques to allow the solution of large-sized instances in reduced solution times. The accelerating techniques and solution approaches are transferable to other network design problem settings with similar characteristics.