Industrial Engineering and Operations Research Dissertations Collection

Risk awareness and perception and the novice driver: Development and evaluation of training interventions and their influence on tactical and strategic visual search behavior

Pradhan, Anuj Kumar — Wed, 28 Jul 2010 17:57:37 PDT

Young novice drivers are at a significantly higher risk of having a fatal vehicle crash than experienced drivers. One of the main causes for this statistic is that these drivers lack risk perception skills. They have not developed the ability to efficiently perceive or predict risks while driving. This dissertation will detail the research undertaken to study and remedy this problem.^ Driving behaviors of drivers of different age groups were first evaluated to establish metrics that could discriminate between novice and experienced drivers. The comparison was carried out in a driving simulator while collecting vehicle parameters and eye movement information from the drivers. It was found that eye glance locations serve as effective indicators of the driver’s level of risk awareness and perception: experienced drivers were better at scanning the appropriate locations for risk relevant elements while driving. This finding led to the development of a PC-Based Risk Awareness and Perception Training Program (RAPT) that was used to test the feasibility of training risk perception skills among novice drivers. Evaluation of the trained drivers’ behaviors showed improved risk perception, both in a driving simulator and in the field. The training also generalized to driving situations that were conceptually different from the ones used in training.^ The training program was primarily designed to target the tactical risk perception skills of drivers, i.e., those skills necessary to detect a potentially hazardous scenario materializing at a particular time and location, which can be detected by scanning for and recognizing various configurations and dynamics of elements in the driving situation. However the training also resulted in the improvement of strategic risk anticipation behavior (i.e., scanning patterns when there is no obvious threat but where a driver is required to be always aware of the possibilities of unexpected hazards). The trained drivers had eye movements that would facilitate efficient and early detection of hazards, while at the same time they were able appropriately to regulate the distribution of their glances towards and away from the forward roadway. This risk perception training can thus be used as an effective intervention for the vulnerable novice driver population.^

A PLANNING METHODOLOGY FOR THE ANALYSIS AND DESIGN OF WIND-POWER SYSTEMS.

DAMBOLENA, ISMAEL GERARDO — Thu, 18 Mar 2010 12:58:15 PDT

An integrated methodology for optimal egress route assignment during population evacuation under an evolving emergency event

Stepanov, A. V — Wed, 17 Feb 2010 09:53:43 PST

The primary focus of this research is to develop an integrated methodology for Adaptable Evacuation Planning (AEP). In case of regional evacuation caused by a hazardous event, one of main objectives of AEP is the optimal design and analysis of evacuation routes in transportation networks that will minimize total clearance time, traveled distance, and potential congestion on roads to ensure overall safety of the evacuated population. The problem under analysis is complex and challenging due to its multi-objective nature, potential congestion, blocking and queueing along routes. In addition, the hazardous event, which caused the evacuation may evolve and affect the population on egress routes, deplete the capacity of the road network and therefore make the initial assumptions of the evacuation policy invalid. To cope with the complexity of the problem, we consider it as an interaction of events in two overlapping and orthogonal networks. The first network represents a surface wild-fire propagation through a complex landscape. The second is a regional evacuation network for which route assignment optimization models are suggested. The first model utilizes a Delaunay triangulation to represent surface fire spread as movement of the fire event within the network. A data dependent procedure to construct the triangulation and estimate the rate of spread along the edges of the network is discussed. After the Delaunay triangulation is constructed, a two pass shortest path algorithm is incorporated to estimate the minimum travel time paths and fire event arrival times. In the next part of the dissertation, an integer programming (IP) formulation and model for optimal route assignment is presented, which utilizes state dependent queueing models to cope with congestion and time delays on road links. State dependent simulation software is used to evaluate performance measures of the evacuation plan: clearance time, total distance travelled and blocking probabilities. The resulting methodology allows a decision maker to adapt routing policies effectively, in case of change in hazardous event behavior, road infrastructure failure, or traffic incidents. The third model integrates the evacuation model and the fire event model and allows one to reroute the population dynamically. Finally, in the third model demonstration we illustrate proposed methodology with a case study, where regional evacuation for the Western Massachusetts is modeled.^

Investments in Energy Technological Change Under Uncertainty

Shittu, Ekundayo — Thu, 08 Oct 2009 11:35:56 PDT

This dissertation addresses the crucial problem of how environmental policy uncertainty influences investments in energy technological change. The rising level of carbon emissions due to increasing global energy consumption calls for policy shift. In order to stem the negative consequences on the climate, policymakers are concerned with carving an optimal regulation that will encourage technology investments. However, decision makers are facing uncertainties surrounding future environmental policy. The first part considers the treatment of technological change in theoretical models. This part has two purposes: (1) to show-through illustrative examples-that technological change can lead to quite different, and surprising, impacts on the marginal costs of pollution abatement. We demonstrate an intriguing and uncommon result that technological change can increase the marginal costs of pollution abatement over some range of abatement; (2) to show the impact, on policy, of this uncommon observation. We find that under the assumption of technical change that can increase the marginal cost of pollution abatement over some range, the ranking of policy instruments is affected. The second part builds on the first by considering the impact of uncertainty in the carbon tax on investments in a portfolio of technologies. We determine the response of energy R&D investments as the carbon tax increases both in terms of overall and technology-specific investments. We determine the impact of risk in the carbon tax on the portfolio. We find that the response of the optimal investment in a portfolio of technologies to an increasing carbon tax depends on the relative costs of the programs and the elasticity of substitution between fossil and non-fossil energy inputs. In the third part, we zoom-in on the portfolio model above to consider how uncertainty in the magnitude and timing of a carbon tax influences investments. Under a two-stage continuous-time optimal control model, we consider the impact of these uncertainties on R&D spending that aims to lower the cost of non-fossil energy technology. We find that our results tally with the classical results because it discourages near-term investment. However, timing uncertainty increases near-term investment.

Hedging Future Uncertainty: A Framework for Obsolescence Prediction, Proactive Mitigation and Management

Josias, Craig Lindsay — Tue, 21 Jul 2009 08:02:18 PDT

Component obsolescence in the "high-tech" electronics industry has become a problem that cannot be ignored. Although recent attention has been given to component obsolescence, in general this issue is still dealt with reactively. This often results in sustainment of a long-life system such as ships, airplanes, power plant, and space based programs to be extremely costly. In addition, delayed schedules, extended downtimes, and technology lags are common occurrences in approaches that deal with obsolescence as it occurs. In wake of the rapid pace of technology innovation, turbulent markets and growing globalization, developing proactive approaches for dealing with obsolescence is a necessity for companies to remain competitive in the marketplace. Thus this dissertation focuses on three fundamental objectives that highlight the importance, provide new insight, and offer solutions to the problem of component obsolescence. The first objective concentrates on the importance of prediction models in determining the life cycle of a component. Obsolescence prediction is key in identifying the items most vulnerable and allows the company to effectively hedge against future uncertainty long before the problem arises. The second objective concentrates on proactive management approaches. This is accomplished through a case study with an industry partner. The purpose of an obsolescence management strategy is to ensure that, issues of obsolescence are anticipated, identified, analyzed, mitigated, reported, and dealt with in a cost effective and timely manner. In addition, it provides life cycle "support and guidance" to the management team. Dealing intelligently with flexibility and uncertainty is characteristic of the Real Options Pricing approach. Thus, the third objective concentrates on options pricing as a decision making tool for mitigating the effects of obsolescence. Making strategic decisions about when to invest, what technology to invest in, waiting until a future point in time when a new technology may be available, are all complex questions to answer. Real options pricing offers a novel approach to addressing issues of obsolescence in sustainment based technologies. Thus this dissertation demonstrates that obsolescence prediction, proactive management and mitigation and the use of real options is key in determining optimal decisions and staying competitive in the "high-tech" electronics industry.

Dynamic pricing for revenue maximization in supply chains

Feldmann, Gunnar — Fri, 27 Mar 2009 17:00:51 PDT

The exchange of goods and services is affected by pricing policies, of which there are two broad categories: posted-price mechanisms (take-it-or-leave-it pricing), and price-discovery mechanisms (auction pricing). In the past, companies fixed prices of a product for a relatively long time period; i.e., prices were considered static. The reason for this strategy is found in the absence of accurate demand information, high transaction costs associated with changing prices, and huge investment in software and hardware to implement a dynamic pricing strategy. Although dynamically posted prices are also take-it-or-leave-it prices, the seller can dynamically change prices over time. The goal here is to balance demand and supply via dynamic pricing. Early adopters of dynamic pricing methods are commonly found in industries where the short-term capacity is difficult to change such as airlines, and hotels. Most of these industries operate in a centralized fashion, which allows prices to be changed at little or no cost. Contrasting to the former are retail-like industries, for whom short-term supply is more flexible but price changes are costly.^ The primary focus of this research is on the latter type industry. We consider a two-echelon supply chain with a single retailer and a single supplier for whom we develop a dynamic pricing policy, which optimizes the net profit in the presence of costly price changes. Besides that cost, we consider that under a dynamic pricing policy costs for buyers are uncertain. This uncertainty is addressed in an optimal order policy. In addition, repeated buyer seller interaction in a dynamic pricing setting requires the incorporation of buyer behavior and, therefore, it is included in the dynamic pricing policy.^ The results we obtain are twofold. First, we show that the use of dynamic pricing is beneficial to the individual node, but when applied to multiple nodes in a supply chain environment results in price amplification, or price bullwhip. Second, we observe that the cost for changing price is the unique contributor to "sticky prices", prices that remain fixed for a certain period of time.^

Improving the road scanning behavior of older drivers through the use of situation-based learning strategies

Romoser, Matthew Ryan Elam — Fri, 27 Mar 2009 17:00:43 PDT

Older drivers are over-represented in angled crashes when compared with younger experienced drivers. Past research primarily points to age-related cognitive and physical decline, which can impede older drivers' ability to monitor their driving environment efficiently and decrease their ability to maintain adequate situational awareness. Despite compensatory behaviors such as driving less, driving more slowly or avoiding driving in inclement conditions, there is evidence that in some cases these drivers may be under-compensating, as older drivers are still involved in more angled crashes than any other category. Of particular concern are intersections in which other vehicles can approach from the side.^ Two experiments described here investigate whether tailored feedback based on a driver's own unsafe behaviors and active, situation-based training in a simulator can change drivers' attitudes about their own abilities, raise their awareness of the crash risks for older drivers and lead to long-term improvements of driving behavior such as increased side-to-side scanning while negotiating intersections.^ Experiment 1 investigated whether customized feedback tailored to the individual's specific unsafe driving behaviors in a simulator can successfully alter an older driver's perceptions of his driving skills. Experiment 2 compared how effectively customized feedback about a driver's specific unsafe driving behaviors on the open road followed by active situation-based training in a simulator can improve road scanning and head turning behavior when compared with lecture-style training. The results from Experiment 1 demonstrated that letting drivers make errors in a simulator and then providing customized feedback was successful in changing older drivers' perception of their ability, making them more willing to change driving behavior.^ The results from Experiment 2 indicated that capturing drivers' errors on the road, providing customized feedback, and then adding active training in a simulator increased side-to-side scanning in intersections by nearly 100% in both post-training simulator and field drives. A second group, which received passive classroom-style training, demonstrated no significant improvement. In summary, compared with passive training programs, error capture, feedback, and active situation-based practice in a simulated environment is a much more effective strategy for raising awareness and increasing the road scanning behavior of older drivers.^

Multiscale decision-making: Bridging temporal and organizational scales in hierarchical systems

Wernz, Christian L — Fri, 27 Mar 2009 17:00:38 PDT

Effective decision-making is a key prerequisite for a successful organization. Today’s organizations are large and continue to grow in size and scope. This development leads to higher complexity in managing and controlling organizations. Consequentially, selecting the right course of action has become more difficult for the individual decision-maker. Considering only immediate and local effects of actions reduces decision complexity but also decision quality. Effective decision-makers need to take into consideration the consequences of their actions on different time and organizational scales. In our research we develop a framework for multiscale decision-making, which gives decision-makers the ability to make opportune decisions in the face of multiscale system properties. Also, we provide organizations with a tool with which they can gauge the consequences of various organizational parameters on hierarchically interacting agents over multiple organizational and temporal scales.^ We begin our investigation with a model of two hierarchically interacting agents in a superior-subordinate relationship. The agents influence each other's rewards and chances of success with their decisions. This bi-directional influence creates a game-theoretic situation. Using the concept of Nash equilibria, we determine the agents' optimal strategies for different organizational parameters. Results are presented through phase diagrams, which graphically capture how variations in organizational parameters affect agent behavior.^ We extend this model of hierarchical interaction between two agents to a generalized tree-structured interaction network of many agents. This network resembles the typical organizational form of an enterprise. We visualize the hierarchical agent interaction with dependency graphs, which provide a compact representation of the organization and the associated parameters.^ In a final step, we extend the one-period model to allow for multiple time periods. We use Markov decision processes to model the multi-time-scale interactions. We take into consideration that decisions on lower organizational scales are made at a higher frequency than decisions at higher organizational scales. By considering the interdependence of decision frequency and hierarchal level, we fuse the temporal scale with the organizational scale which results in one comprehensive multiscale decision-making model.^

Capacity and flexibility investment decisions under pricing, product substitution and supply chain performance considerations

Lus, Betul — Fri, 27 Mar 2009 17:00:17 PDT

This dissertation studies capacity investment decisions of a manufacturing firm facing high demand uncertainty in a make-to-order environment. Two important strategies are discussed to better respond to uncertainty in product demands; manufacturing/capacity flexibility and price flexibility. ^ The first part considers high level models of pricing, production and capacity investment decisions of a firm offering substitutable products, where the price and production decisions are made when demand is realized. The study of the impact of product substitution on the firm's optimal investment strategy and the design of the production network leads to following conclusions: (1) Under realistic assumptions on the distribution of the demand functions, the optimal investment in flexible capacity tends to decrease as the products become closer substitutes. (2) By not taking into account product substitution at the investment stage, the firm significantly overestimates the optimal capacity investment levels. (3) Assigning substitutable products to different production plants increases the firm's expected profits with lower investment costs; this is because the system becomes more flexible since production can be transferred from one plant to the other by diverting the demand for one product to its substitute through pricing.^ The second part considers a more detailed capacity investment model where prices are fixed at the beginning of the season and the company faces short-term (period-to-period) demand variability associated with a make-to-order environment. An optimization-based simulation model is used to understand the impact of pricing and increased manufacturing flexibility on the performance of the supply chain. The results show that both manufacturing and pricing flexibilities significantly reduce the optimal investment levels while increasing the expected profits, but lead to higher production variability, system inventory and variability observed by suppliers upstream. As a second step, the model is modified to include outbound transportation costs and service constraints, which help to allocate the production to satisfy at least a certain percentage of demand in different demand regions. We show that considering service constraints significantly increases the minimum service level among different demand regions at the expense of higher system inventory and outbound transportation costs, especially when capacity is scarce.^

Design, evaluation and optimization of the evacuation problem of multi-story facilities

Wen, Yiqing — Fri, 19 Dec 2008 10:16:14 PST

Given a multi-story facility, the problem is how to design its evacuation system so that, when an emergency occurs, it will take the minimal clearance time to evacuate the occupant population from the facility. This research formulates the design problem of an evacuation planning system of a multi-story facility on a rectilinear metric and presents an approach to its solution based on a tri-partite series of optimization and stochastic models.^ In order to find the optimal locations of stairwells on each floor, a heuristic for equi-area partitioning for rectilinear simple polygons is developed. The facility evacuation system is studied as a state dependent stochastic model, and a simulation program based on this model is used to evaluate the efficiency of the evacuation system. This research also addresses the problem of how to optimally determine the width of the stairwells with the objective of obtaining the minimal clearance time of the evacuation.^ Finally, a case study is conducted based on a real world problem - the evaluation of the evacuation process of the New York International Gift Fair held at the Jacob Javits Convention Center. The problem is modeled with the state dependent stochastic model; and an evacuation is conducted and suggested solutions for improvement are recommended.^