Decision, Operations & Information Technologies Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2761
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Item DATA-DRIVEN OPTIMIZATION AND STATISTICAL MODELING TO IMPROVE DECISION MAKING IN LOGISTICS(2019) Sinha Roy, Debdatta; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we develop data-driven optimization and statistical modeling techniques to produce practically applicable and implementable solutions to real-world logistics problems. First, we address a significant and practical problem encountered by utility companies. These companies collect usage data from meters on a regular basis. Each meter has a signal transmitter that is automatically read by a receiver within a specified distance using radio-frequency identification (RFID) technology. The RFID signals are discontinuous, and each meter differs with respect to the specified distance. These factors could lead to missed reads. We use data analytics, optimization, and Bayesian statistics to address the uncertainty. Second, we focus on an important problem experienced by delivery and service companies. These companies send out vehicles to deliver customer products and provide services. For the capacitated vehicle routing problem, we show that reducing route-length variability while generating the routes is an important consideration to minimize the total operating and delivery costs for a company when met with random traffic. Third, we address a real-time decision-making problem experienced in practice. In one application, routing companies participating in competitive bidding might need to respond to a large number of requests regarding route costs in a very short amount of time. In another application, during post-disaster aerial surveillance planning or using drones to deliver emergency medical supplies, route-length estimation would quickly need to assess whether the duration to cover a region of interest would exceed the drone battery life. For the close enough traveling salesman problem, we estimate the route length using information about the instances. Fourth, we address a practical problem encountered by local governments. These organizations carry out road inspections to decide which street segments to repair by recording videos using a camera mounted on a vehicle. The vehicle taking the videos needs to proceed straight or take a left turn to cover an intersection fully. Right turns and U-turns do not capture an intersection fully. We introduce the intersection inspection rural postman problem, a new variant of the rural postman problem involving turns. We develop two integer programming formulations and three heuristics to generate least-cost vehicle routes.Item Heuristics for Solving Three Routing Problems: Close-Enough Traveling Salesman Problem, Close-Enough Vehicle Routing Problem, Sequence-Dependent Team Orienteering Problem(2009) Mennell, William Kenneth; Golden, Bruce L.; Wasil, Edward A.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we examine three important routing problems. In the second chapter we investigate the Close-Enough Traveling Salesman Problem (CETSP) in which a salesman must get within a specified radius of each node to visit it. The third chapter studies the multi-vehicle extension of the CETSP, the Close-Enough Vehicle Routing Problem (CEVRP). In the fourth chapter, we develop a post-processor to improve the accuracy of our heuristics for solving the CETSP and CEVRP. In the fifth chapter, we solve the Sequence-Dependent Team Orienteering Problem (SDTOP) in which the profit received for each node visited is dependent on the sequence in which all the nodes are visited. We summarize the dissertation in the final chapter. The CETSP, CEVRP, and SDTOP have application in aerial reconnaissance route planning. We formulate each problem as a mathematical program and apply heuristic and combinatorial optimization techniques to solve them. We present the results of extensive computational experiments that show that our methods produce high-quality solutions quickly.Item A Study of Four Network Problems in Transportation, Telecommunications, and Supply Chain Management(2007-08-01) Chen, Si; Golden, Bruce; Raghavan, Subramanian; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The increasing material costs and the rapid advances in computing technology have both motivated and promoted the study of network problems that arise in several different application domains. This dissertation consists of four chapters on network applications in transportation, telecommunications, and supply chain management. The core of our research is to apply heuristic search procedures and combinatorial optimization techniques to various practical problems. In the second chapter we investigate the split delivery vehicle routing problem (SDVRP), where a customer's demand can be split among several vehicles. The third chapter deals with the regenerator location problem (RLP) that arises in optical networks. The fourth chapter solves the parametric uncapacitated network design problems on series-parallel graphs, which have potential application in supply chain management. In the fifth chapter we study the arc routing problem that arises in the small package delivery industry. The last chapter summarizes the dissertation. The results in this dissertation indicate that the methodologies developed to solve the network problems in the four different applications are quite efficient. Consequently, when applied in practice, they have the potential to significantly improve the operational efficiency of organizations in the relevant application domains.Item Air Transportation System Performance: Estimation and Comparative Analysis of Departure Delays(2006-11-22) Tu, Yufeng; Ball, Michael; Jank, Wolfgang; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet, many existing decision support tools for air traffic flow management take a deterministic approach to problem solving. In this study, we focus on the flight departure delays because such delays serve as inputs to many air traffic congestion prediction systems. Modeling the randomness of the delays will provide a more accurate picture of the airspace traffic situation, improve the prediction of the airspace congestion and advance the level of decision making in aviation systems. We first develop a model to identify the seasonal trend and daily propagation pattern for flight delays, in which we employ nonparametric methods for modeling the trends and mixture distribution for the residual errors estimation. This model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We emphasize that a major objective is to produce not just point estimates but estimates of the entire distribution since the congestion estimation models envisioned require delay distribution functions, e.g. to produce probability of certain delays or expected traffic levels for arbitrary time intervals. Local optima problems are typically associated with mixture distribution estimation. To overcome such problems, we develop a global optimization version of the Expectation Maximization algorithm, borrowing ideas from Genetic Algorithms. This optimization algorithm shows the ability to escape from local traps and robustness to the choice of parameters. Finally, we propose models to estimate the so called "wheels-off delays" for flights within the NAS while incorporating a dynamic update capability. Approaches are evaluated based on their ability to reduce variance and their predictive accuracy. We first show that how a raw histogram can be misleading when a trend is present and how variance can be reduced by trend estimation. Then, various techniques are explored for variance reduction. The multiple seasonal trends method shows great capability for variance reduction while staying parsimonious in parameters. The downstream ripple effect method further enhances the variance reduction capability and makes real-time prediction practical and accurate. A rolling horizon updating procedure is described to accommodate the arrival of new information. Finally different models are compared with the current model adopted by the ETMS systems and the predictive capabilities of all models are shown.