Traditionally, transportation road networks have been designed for minimal congestion. Unfortunately, such approaches do not guarantee minimal vehicle emissions. Given the negative impacts of vehicle pollutants as well as tighter national air quality standards, it is critical for regions to be able to identify capacity modifications to road networks such that vehicle emissions are minimal. This ability combined with land use changes and opportunities for non-auto travel are paramount in helping regions improve air quality. However, network design research has yet to directly address this topic. To fill this apparent gap in network design research, an emissions network design problem and solution method are proposed in this thesis. Three air pollutants are considered: hydrocarbons, nitrogen oxides, and carbon monoxide. The proposed model is applied to two road networks: Sioux Falls, ND and Anaheim, CA. The model is a bi-level optimization problem solved using a genetic algorithm and incorporates the influence of demand uncertainty. Findings indicate designing for minimal congestion tends to increase emissions of criteria air pollutants. However, not adding capacity to a road network also increases emissions of pollutants. Therefore, an optimization problem and solution method, such as the model presented here, is useful for identifying capacity additions that reduce vehicle emissions. It is also useful for understanding the tradeoffs between designing a network for minimal congestion versus minimal vehicle emissions. / text

This thesis analyzes investment incentives of strategic firms in industries where either demand is uncertain, or the good produced is economically non-storable and demand fluctuates. In those industries, investment is a long run decision, whereas production has to be adjusted short-run. Prominent examples are recently liberalized utilities such as the electricity sector. Regulated monopolies have been replaced by a small number of competing firms, which often are considered to behave strategically in order to exercise market power. Whereas the regulatory regimes prior to liberalization induced generous (over-)investment choices, we observe increasing unease of experts and policy makers regarding investment incentives in liberalized electricity markets. The first three chapters of this thesis (part one) analyze total capacity choice of strategic firms prior to producing for the spot market. We first determine the equilibrium of the market game. In the remainder of the first part we analyze the interdependency of enhanced spot market competition and firms overall capacity choice. We first analyze the impact of complete elimination of market power at the spot market giving rise to marginal cost pricing. We then consider the impact of price caps at the spot market. And finally we study the impact of reduced market power at the spot markets due to forward contracting. In the second part of the thesis firms can invest into several technologies. This allows them to determine not only their total capacity but also it's precise composition. In the absence of strategic interaction, for a single regulated firm, this has already been thoroughly analyzed in the so called peak load pricing literature, which has been widely applied for electricity markets prior to liberalization. In order to accommodate for the completely changed situation after liberalization, however, we extend this framework to the case of strategically interacting firms. Based on data of the German electricity market, we then illustrate and empirically quantify our theoretical results. We determine firms’ investment choices for different market structures and quantify the impact of spot market interventions on investment decisions and welfare. This allows us to quantify the potential for the exercise of market power, in the long run, when firms’ investment decisions are taken into account.

Demand uncertainty has been recognized as one factor that may cause price dispersion in perfectly competitive markets with costly and perishable capacity. With the persistence of the degree of price dispersion in increasingly competitive markets, demand uncertainty has become more important for us to understand the phenomenon of fare inequality. This dissertation consists of three related studies on this topic. In the first study, Prescott (1975) model is extends by incorporating the heterogeneity of customers' reservation values. The model shows that the equilibrium price dispersion also depends on the mix of customers and their reservation values. With customer segmentation based on reservation values, the equilibrium price dispersion is more efficient than what can be achieved without segmentation. In the airline industry context, the model implies that different prices can exist simultaneously in the market and carriers would provide more seats if they can segment their travelers. This sheds light on an alternative motivation for airlines to require Saturday night stay over other than the practice of price discrimination. In the second study, a price simulation in the airline industry is conducted. The stochastic demand for coach class, nonstop, air travel service on the observed routs is calculated. Then a market price schedule based on Prescott's model is simulated by using nonparametric method. The comparison between the simulated price distribution and the actual price distribution provides evidence that on average more than 60 percent of the fare inequality on the observed routes can be accounted for by cost variation due to demand uncertainty under the condition of perfect competition. At last, an empirical model is specified to explore the relationship between route demand uncertainty and carrier price dispersion in U.S. air travel markets. The results demonstrate that the effect of route demand uncertainty on carrier price dispersion varies with the market structure. In monopoly market, the route demand uncertainty has no effect on carrier price dispersion. While in duopoly and competitive markets, the increase of route demand uncertainty is associated with the decrease of the carrier price dispersion. Furthermore, the negative relationship is magnified when the market becomes more competitive.

The hypothesis in this paper is that the existence of retail markets may not necessarily be determined by spatial factors and increasing return in transportation (or increasing returns in retailing), but can be explained by the rational behaviour of firms operating in a stochastic environment. It is shown that demand uncertainty can serve as an independent source of retail trade. Consequently, the ability of firms to process information and predict demand (i.e., to decrease demand uncertainty) may affect the characteristics of retail markets. The results indicate that risk-averse firms always devote resources to demand forecasting; producers are better off trading with retailers than with final consumers; and the volume of output supplied through retail markets is greater than it would be if producers traded directly with consumers (thus benefiting social welfare). Furthermore, the paper shows that technological progress in data-processing, which allows for cheaper and better predictions of market demand, increases the number of firms operationg in retail markets. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience

We provide a comprehensive characterization of the relationship between optimal capacity and the degree of product substitution/complementarity under price/production postponement, considering different business practices (holdback versus clearance, negative price policies) and different demand models. Specifically, we consider a firm that produces two products, which can be substitutable or complementary. The demand of each product is a linear function of the prices of both products (with the relationship depending on the substitution/complementarity structure), and is subject to an additive stochastic shock. We consider two types of linear demand functions that are commonly used in the economics and operations management literature. The firm operates in a monopolistic setting and acts as a price-setter for both products. Overall the firm needs to make three sets of decisions: capacity, production quantities, and prices. While the capacity investment decision has to be made ex-ante observation of demand curves, price and/or quantity decisions can be postponed until after demand curves are observed. We consider two postponement strategies: price and quantity postponement, and price postponement only. We characterize the optimal pricing/production/investment decisions for each postponement strategy. Using these characterizations, we show that product substitution/complementarity is a key demand characteristic, which has a large impact on the optimal capacity. Our results show that how the optimal capacity behaves in substitution/complementarity parameter is quite similar under both postponement strategies, and under holdback and clearance. However, this behavior depends highly on other underlying assumptions (i.e., whether or not negative prices are allowed) and on the demand model used. / Ph. D.

This dissertation contributes to theoretical and empirical studies in microeconomics, with a focus on evaluating policy relevant problems to provide new insights into questions. Within labor economics, I strive to understand the labor market returns to skills, taking into consideration the business cycle. In the first chapter, I investigate how the labor market returns to cognitive skills and social skills vary during recessions. In the second chapter, I examine the short-, medium and long-term career outcomes of college graduates as a function of economic conditions at graduation and both cognitive and social skills. In the third chapter, within information economics, I study how asymmetric information and demand uncertainty influence pricing strategies through learning. In Chapter 1, I examine how labor market returns to cognitive skills and social skills vary with the business cycle over the past 20 years, using data from the NLSY79 and the NLSY97. Exploiting a comparable set of cognitive and social skill measures across survey waves, I show that an increase in the unemployment rate led to higher demand for cognitive skills in the 2000s. High unemployment also sorted more workers into information use intensive occupations that require computer skills in the 2000s, but it sorted more workers into routine occupations in the 1980s and 1990s. This evidence suggests that recessions accelerate the restructuring of production toward routine-biased technologies. I also find that the returns to social skills increase during periods of high unemployment, though only in terms of the likelihood of full-time employment for experienced workers. Furthermore, an increase in unemployment increases the social skill task intensity of a worker's occupation in the 2000s, while it shows the contrary in the 1980s and 1990s. Based on these results, I argue that routine-biased technological change may not readily substitute for workers in tasks requiring interpersonal interaction, and therefore such technologies demand experienced laborers who have high social skills during recessions. In Chapter 2, I study the impacts of entry conditions on labor market outcomes to cognitive and social skills for the US college graduating classes of 1979–1989. Using the National Longitudinal Survey of Youth 1979, I find that Workers with higher cognitive skills are more likely to be employed, find job more quickly and have higher-quality employment, while those with higher social skills voluntarily switch jobs more often. I also show that graduating in a worse economy intensifies the roles of social skills, allowing workers with higher social skills to catch up more quickly from poor initial conditions by switching jobs more often. This could partly explain why wage returns to cognitive skills declines but wage returns to social skills increases from graduating in recessions. In Chapter 3, we consider a dynamic pricing problem facing a seller who has private information about the quality of her good, but is uncertain about the arrival rate of buyers. Restricting attention to the equilibria in which the high-quality seller insists on a constant price, we show that the low-quality seller's expected payoff and equilibrium pricing strategy crucially depend on buyers' knowledge about the demand state. If they are also uncertain about the demand state, then demand uncertainty increases the low-quality seller's expected payoff, and her optimal pricing strategy is to offer a high price initially and drop it to a low price later. If buyers know the demand state, then demand uncertainty does not affect the low-quality seller's expected payoff, and a simple cutoff pricing strategy cannot be a part of equilibrium. In the latter case, we show that there exists an equilibrium in which the low-quality seller begins with a low price, switches up to a high price, and eventually reverts back to the low price.

This dissertation analyzes pricing under uncertainty focusing on the U.S. airline industry. It sets to test theories of price dispersion driven by uncertainty in the demand by taking advantage of very detailed information about the dynamics of airline prices and inventory levels as the flight date approaches. Such detailed information about inventories at a ticket level to analyze airline pricing has been used previously by the author to show the importance of capacity constraints in airline pricing. This dissertation proposes and implements many new ideas to analyze airline pricing. Among the most important are: (1) It uses information about inventories at a ticket level. (2) It is the first to note that fare changes can be explained by adding dummy variables representing ticket characteristics. Therefore, the load factor at a ticket level will lose its explanatory power on fares if all ticket characteristics are included in a pricing equation. (3) It is the first to propose and implement a measure of Expected Load Factor as a tool to identify which flights are peak and which ones are not. (4) It introduces a novel idea of comparing actual sales with average sales at various points prior departure. Using these deviations of actual sales from sales under average conditions, it presents is the first study to show empirical evidence of peak load pricing in airlines. (5) It controls for potential endogeneity of sales using dynamic panels. The first essay tests the empirical importance of theories that explain price dispersion under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand uncertainty and to identify which flights are peak and which ones are off-peak. It shows that different prices can be explained by the different selling probabilities. The second essay is the first study to provide formal evidence of stochastic peak-load pricing in airlines. It shows that airlines learn about the demand and respond to early sales setting higher prices when expected demand is high and more likely to exceed capacity.

This study investigates the relevance of dynamic traffic assignment models under uncertainty. In the last years researchers have dealt with advanced traffic control systems since road provision is not regarded as a proper solution to relieve congestion. Dynamic assignment which is an essential component of investment planning is regarded as a new research area in the field of urban transportation. In this study the performance of dynamic traffic assignment method, which incorporates time dependent flow, is compared with that of static model. Research outcomes showed that dynamic assignment method provides more reliable outcomes in predicting traffic flow / therefore its solution algorithm is integrated to conventional four staged model. Literature survey showed that researches have hot provided an appropriate framework for transforming networks. This study investigates travel demand variations in a dynamic city and discuses possible strategies to respond dynamic and uncertain properties of individuals&rsquo / travel behavior. Research findings showed that both external and internal uncertainties have significant influences on reliability of the model. Recommended procedure aims reducing uncertainty in order to improve reliability of model. Finally, the relevancy of the problem and the applicability of recently developed methods are discussed in Ankara case.

Hajj is very important to all Muslims across the globe. Because of its religious significance, the Hajj pilgrimage experiences a massive number of visitors each year, most of whom are foreign and require consumer goods during the six-day pilgrimage. The large number of pilgrims often results in a sharp increase in demand for consumer goods. Suppliers must ensure that they have adequate amounts of these products so that they can meet the needs of the pilgrims as well as their different tastes for these goods. It is however usually difficult to determine exactly how much is required. This complexity creates demand uncertainty that the firms in the industry must be able to cope with for them to succeed. Small- and medium-sized enterprises (commonly referred to as SMEs) play an important role in the food chain throughout the Hajj season in the Kingdom of Saudi Arabia (KSA). However, SMEs are recognised to experience severe obstacles that have the potential to threaten their continuity, and the industry succumbs to the crisis of demand uncertainty throughout the short period of the peak season of Hajj each year. This problem is complex due to the constant increase in the number of pilgrims and the continuous changes in their needs and preferences. Demand uncertainty can ultimately result in an increase in production costs, long lead times, substandard service levels, and quality problems, especially in terms of food obsolescence. There is a gap in the literature regarding aligning sources of uncertainty with supply chain strategies in an effort to improve supply chain performance. More specifically, the impact of supply chain integration strategies (SCI) on manufacturing strategies, such as postponement practice (PP) and mass customisation capability (MCC) to mitigate demand uncertainty (DUM), has not been fully explored. This study investigates three fundamental issues: 1 - how effective supply chain integration (internal integration and external integration) can be applied in Saudi’s SMEs food industry, and how the interaction between them mutually manipulates the improvement of postponement practice and mass customisation capability in food productions in Hajj; 2 - how the volume of cooperation leads to the mitigation of demand uncertainty in maintaining the survival of small and medium enterprises that operate in food production in Hajj; and 3 - how the environmental condition (i.e. competitive intensity) moderates the influence of supply chain integration (SCI) on this interaction in Saudi’s food SMEs that operate in Hajj. iii Based on the extended resource-based view (ERBV) of the firm, the strategic resources and knowledge come not only from within the organisation’s boundaries, but also from outside. Thus, a firm’s overall strategic capability may be embedded in a wider network of inter-firm exchange relationship. Contingency theory furthermore argues that an organisation should align their practices, processes and strategies with their business environment. In consideration to the extant literature, a number of hypotheses were defined, demonstrating the correlation between supply chain integration, postponement practice, mass customisation capability and demand uncertainty mitigation. Subsequently, a conceptual framework was developed with the objective to verify the relationship amongst the constructs. Mixed methodologies were employed; qualitatively, with 12 CEOs working in different SMEs in the food industry across KSA were initially interviewed to validate the conceptual framework. Content and face validity was accomplished with a group of academics and experts. A pilot study was carried out on a sample of 50 subcontractors, Hajj campaigns, pilgrimage institutions and food suppliers. Consequently, an online survey was conducted amongst SMEs to test the hypotheses. As a result, 239 responses were received from the SMEs in the food sector in the KSA. Partial Least Square (PLS) was used for the analysis. The interviewees were identified through snowball sampling (Detailed in the next sections). Quantitative data were collected using the convenience sampling technique, given the non-availability of the sampling frame. Based upon the extended resource-based view (ERBV) of the firm, alongside contingency theory, the initial and final results of the pilot test and survey were seen to be steady with these theories, where supply chain integration was viewed not only as having a significant direct and indirect effect on the postponement practice and mass customisation capability by SMEs of food during Hajj, but also as playing a critical role throughout the employment of postponement practice as an important strategy, empowering mass customisation capability to mitigate demand uncertainty. Likewise, both results were seen to be consistent with contingency theory; that is, a firm should coordinate their supply chain integration activities, postponement practice and mass customisation capability to their business environment, particularly with high competitive intensity to enhance demand uncertainty mitigation. In order to achieve competitive intensity, organisations are mainly focused on emerging markets and expanding their product lines. In the event that organisations begin targeting similar set opportunities, they risk bringing upcompetitive intensity for themselves, which increases the cost of growth. The cost of business will be noticeable when considering marketing speed, media inflation, the rate of innovation and trade spend in marketing, all of which are indicators of completion intensity. Improvements in supply chain efficiency, optimising strategies in marketing and extracting the best of return on investments from promotions by organisations also indicates competition intensity. Costs, competition and the ability to differentiate are some of the main determining factors of competition. Importantly, these are all tied up within uncertainty mitigation. However, despite the fact that internal integration has a positive direct and indirect effect on the postponement practice, mass customisation capability is created by SMEs of food industry, in addition to its direct effect upon both supplier integration and customer integration. Customer integration has also been found to improve postponement practice as well as mass customisation capability in a direct fashion. Supplier integration has a significant impact on postponement practice; however, it seems not significantly associated positively with mass customisation capability. Moreover, postponement practice also has an effect in mitigating demand uncertainty, both directly and indirectly, through mass customisation capability. Finally, mass customisation capability similarly has been found to enhance demand uncertainty mitigation. Research indicates that the direct and indirect effects of all constructs increase when there is intense competition in Hajj.

<p dir="ltr">The rising demand for EVs, motivated by their environmental benefits, is generating increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. The high cost of these investments motivates governments to seek optimal decisions on EV-related investments including EV charging infrastructure, and such decisions include locations, capacities, and deployment scheduling of such infrastructure. Additionally, uncertainties in travel demand prediction and EV driving range constraints need to be considered in EV infrastructure investment planning. To help address these questions, this thesis developed a framework to establish optimal schedules and locations for new charging stations and for decommissioning gasoline refueling stations for any given network over a long-term planning horizon, considering uncertainties in travel demand forecasts and EV driving-range heterogeneity. To address the uncertainties, the proposed framework is formulated as a robust mathematical model that minimizes the worst-case total system travel cost and the total penalty for unused charging station capacity. This study uses an adaption of the cutting-plane method to solve the proposed model. In the numerical analyses, the performance of the robust framework and its deterministic counterpart are compared. The results show that the optimal robust plan outperforms the deterministic plan by yielding savings in the costs of travel and electricity charging. The thesis also investigates the effects of investment budget levels of robust planning. The numerical results throw light on the relationships between higher investment levels and electric charging station deployment levels and consequently, the savings in travel costs and impacts on unused charging capacity. The outcomes of this thesis can help road agencies and related private sector entities enhance preparations towards infrastructure investments to support electric charging stations in an efficient manner.</p>