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Hosting the Olympics or not
(2017)
This dissertation investigates capacity and technology choice decisions in maritime container shipping under demand and regulatory uncertainty. In an introductory overview, we discuss the industry and the challenges that complicate investment decisions in shipping: e. g., the multitude of decisions, market volatility, excess capacities and the trend of new environmental regulation. Real option valuation methods can account for strategic options and the uncertainties in capacity decisions in shipping. To assess the impact of chartering on maritime investment, we analyze investment and charter options individually in a continuous-time model. We combine both in a discrete-time approach taking into account key features of the industry: investment with time to build, divestment, chartering, an endogenous charter rate, layup, and demand uncertainty. While we find demand volatility to increase optimal capacities if only investment with time to build is possible, chartering reduces this effect. It adds value to the overall project, should be mainly applied to compensate unexpected capacity shortages and needs to be considered in decision-making. Uncertainty about future eco-regulation is a further challenge for the industry. In an approximate dynamic programming model extension, we account for a stochastic introduction of operating cost-increasing regulation to assess optimal capacity choice under regulatory uncertainty. Regulation can allow for grandfathering, affecting only newly acquired vessels. We find uncertainty about future regulation with grandfathering to induce heavy up-front investment to secure a low cost base even in regulated markets. Such uncertainty may increase excess capacities and industry emissions. Uncertainty without grandfathering, however, reduces overall investment and emissions. In this case, the market may contract as investors resort to chartering until uncertainty is resolved. To also assess technology choice in light of uncertain future regulation, we develop a two-phase regime-switching model. We derive analytical solutions and study the effects of regulatory uncertainty on technology choice in a numerical extension that relaxes restrictive assumptions. We find that such uncertainty can increase optimal capacities and that a single-technology strategy is preferable over a fleet of mixed technologies in most cases. We further extend the model and compare the effectiveness of two different regimes—an emissions cap and an emissions tax. Results suggest that an emissions cap is more effective at reducing overall emissions while a tax causes lower regulation cost. The regime choice also determines the optimal technological fleet composition. The main implication is that chartering and regulatory uncertainty have a strong effect on optimal investment in shipping and should be considered in project valuation. Further, regulatory uncertainty can lead to unintended investment behavior that undermines regulatory goals. Lastly, regulatory regimes are not equally effective/efficient in reducing the environmental footprint of shipping and constitute varying incentives for investing in eco-friendly technology.
In this thesis, we(1) use operations research methods to provide insights into three areas associated with health care operations management. In Chapter 2, we use a discreteevent supply chain simulation to asses if coordination among partners is beneficial in a supply chain with the characteristics of the German pharmaceutical market. We find that the greatest cost savings and service levels could be achieved through a highly integrated collaboration although most of its impact could already be achieved through sharing point-of-sales demand information. Results suggest that coordination is most beneficial in situations where product shelf life is short and demand variation is high.
In Chapter 3 we consider quality-of-life maximizing sequences of prophylactic surgeries for female carriers of a BRCA1/2 genetic mutation, who face a significantly elevated breast and ovarian cancer risk. Using a Markov Decision Process model, we determine the optimal surgery sequence that maximizes the carrier’s expected lifetime qualityadjusted life years (QALYs). Baseline results demonstrate that a QALY-maximizing sequence recommends a bilateral mastectomy between ages 30 and 60 and bilateral salpingo-oophorectomy after age 40 for BRCA1 carriers. Surgeries are recommended later for BRCA2 carriers, as their cancer risk is lower. The model’s structural properties show that when one surgery has already been completed, there exists an optimal control limit after which performing the other surgery is always QALY-maximizing.
In Chapter 4, we develop a two-stage model for optimizing when and where to assign Ebola treatment unit (ETU) beds—across geographic regions—during an infectious disease outbreak’s early phase. The first stage includes a dynamic transmission model that forecasts occurrence of new cases at the regional level, thus capturing connectivity among regions; in this stage we introduce a coefficient for behavioral adaptation to changing epidemic conditions. The second stage includes two approaches to efficiently allocate intervention resources across affected regions. Such an allocation could have prevented up to 3,434 infections over an 18-week period during the 2014 Ebola outbreak in West Africa, a 58% improvement compared with the actual allocation.
(1) In Chapter 2, 3, and 4, the term ’we’ refers to the authors of Nohdurft & Spinler (2016), Nohdurft et al. (2016a), and Nohdurft et al. (2016b), respectively.
Servant Leadership in der IT
(2017)
Status dynamics and their effects on individual cooperation and performance in interdependent teams
(2017)
This dissertation assesses investment decisions in container shipping. To understand the current state of the industry, key characteristics and challenges, such as overcapacity, eroding margins due to low freight rates, long investment lead times, and frequent changes in alliance structure are introduced.
The nature of the industry motivates the application of real options, hence a real options investment model in oligopolistic competition is presented. An analytic solution in continuous time as well as a dynamic programming solution in discrete time are derived. The model takes into account an endogenous price function, fuel-efficient investment, endogenous lead times, and endogenous price formation in the secondary vessel market. This allows to study the impact of competitive intensity, number of players, volatility, fuel-efficiency, lead time, and variable cost on optimal capacity. An investigation of optimal investment policies shows that strategic action increases firm value and strategic alliances might help alleviate some of the industry’s challenges.
Since the container shipping market is characterized by frequent alliance changes, the performance of the real options model in the context of a cooperative shipping game is assessed. Extending the coalition structure value concept it can be shown that, compared with discounted cash flow, the real options trigger performs better, especially in light of high competitive intensity and freight rate volatility while not exhibiting substantial disadvantages in other settings. A further assessment of a number of drivers for alliance instability finds that alliance complexity cost, freight rate volatility, and competitive intensity increase alliance changes.
To verify the investment approach, a characterization of the container freight rate is provided with an empirical Autoregressive Integrated Moving Average (ARIMA) model. It can be observed that the freight rate exhibits a negative relationship with capacity deployment; hence the oligopoly price function is confirmed. Based on the freight rate characterization, a back testing of the real options investment approach is provided. It shows that if players had applied the presented approach, capacities would have decreased and rates improved. A number of limitations of the real options approach are identified,
i.e. substantial impact of volatility expectation, potentially induced cyclicality from trigger approaches, and the timing impact of investment and divestment lead times.
The implications of this research are that strategic action in the container shipping industry is worthwhile and understanding the market specifics (such as competitive intensity, volatility, and freight rate characterization) is very important. Container carriers should add a real options approach to their investment toolkit and keep an eye on potential overcapacity. Finally, entering strategic alliances is suggested, but complexity should be avoided.