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Description
Today's competitive markets force companies to constantly engage in the complex task of managing their demand. In make-to-order manufacturing or service systems, the demand of a product is shaped by price and lead times, where high price and lead time quotes ensure profitability for supplier, but discourage the customers from placing orders. Low price and lead times, on the other hand, generally result in high demand, but do not necessarily ensure profitability. The price and lead time quotation problem considers the trade-off between offering high and low prices and lead times. The recent practices in make-to- order manufacturing companies reveal the importance of dynamic quotation strategies, under which the prices and lead time quotes flexibly change depending on the status of the system. In this dissertation, the objective is to model a make-to-order manufacturing system and explore various aspects of dynamic quotation strategies such as the behavior of optimal price and lead time decisions, the impact of customer preferences on optimal decisions, the benefits of employing dynamic quotation in comparison to simpler quotation strategies, and the benefits of coordinating price and lead time decisions. I first consider a manufacturer that receives demand from spot purchasers (who are quoted dynamic price and lead times), as well as from contract customers who have agree- ments with the manufacturer with fixed price and lead time terms. I analyze how customer preferences affect the optimal price and lead time decisions, the benefits of dynamic quo- tation, and the optimal mix of spot purchaser and contract customers. These analyses necessitate the computation of expected tardiness of customer orders at the moment cus- tomer enters the system. Hence, in the second part of the dissertation, I develop method- ologies to compute the expected tardiness in multi-class priority queues. For the trivial single class case, a closed formulation is obtained. For the more complex multi-class case, numerical inverse Laplace transformation algorithms are developed. In the last part of the dissertation, I model a decentralized system with two components. Marketing department determines the price quotes with the objective of maximizing revenues, and manufacturing department determines the lead time quotes to minimize lateness costs. I discuss the ben- efits of coordinating price and lead time decisions, and develop an incentivization scheme to reduce the negative impacts of lack of coordination.
ContributorsHafizoglu, Ahmet Baykal (Author) / Gel, Esma S (Thesis advisor) / Villalobos, Jesus R (Committee member) / Mirchandani, Pitu (Committee member) / Keskinocak, Pinar (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Healthcare operations have enjoyed reduced costs, improved patient safety, and
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.
ContributorsBren, Austin (Author) / Saghafian, Soroush (Thesis advisor) / Mirchandani, Pitu (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. In addition, most of the current real-time freeway traffic estimators consider only data from loop detectors. This dissertation develops a bi-level data fusion approach using heterogeneous multi-sensor measurements to estimate real-time lane-based freeway traffic conditions, which integrates a link-level model-based estimator and a lane-level data-driven estimator.
Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers’ anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system.
The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters.
Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.
Macroscopic traffic flow models describe the evolution of aggregated traffic characteristics over time and space, which are required by model-based traffic estimation approaches. Since current first-order Lagrangian macroscopic traffic flow model has some unrealistic implicit assumptions (e.g., infinite acceleration), a second-order Lagrangian macroscopic traffic flow model has been developed by incorporating drivers’ anticipation and reaction delay. A multi-sensor extended Kalman filter (MEKF) algorithm has been developed to combine heterogeneous measurements from multiple sources. A MEKF-based traffic estimator, explicitly using the developed second-order traffic flow model and measurements from loop detectors as well as GPS trajectories for given fractions of vehicles, has been proposed which gives real-time link-level traffic estimates in the bi-level estimation system.
The lane-level estimation in the bi-level data fusion system uses the link-level estimates as priors and adopts a data-driven approach to obtain lane-based estimates, where now heterogeneous multi-sensor measurements are combined using parallel spatial-temporal filters.
Experimental analysis shows that the second-order model can more realistically reproduce real world traffic flow patterns (e.g., stop-and-go waves). The MEKF-based link-level estimator exhibits more accurate results than the estimator that uses only a single data source. Evaluation of the lane-level estimator demonstrates that the proposed new bi-level multi-sensor data fusion system can provide very good estimates of real-time lane-based traffic conditions.
ContributorsZhou, Zhuoyang (Author) / Mirchandani, Pitu (Thesis advisor) / Askin, Ronald (Committee member) / Runger, George C. (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2015