State-controlled epidemic in a game against a novel pathogen

The pandemic reminded us that the pathogen evolution still has a serious effect on human societies. States, however, can prepare themselves for the emergence of a novel pathogen with unknown characteristics by analysing potential scenarios. Game theory offers such an appropriate tool. In our game-theoretical framework, the state is playing against a pathogen by introducing non-pharmaceutical interventions to fulfil its socio-political goals, such as guaranteeing hospital care to all needed patients, keeping the country functioning, while the applied social restrictions should be as soft as possible. With the inclusion of activity and economic sector dependent transmission rate, optimal control of lockdowns and health care capacity management is calculated. We identify the presence and length of a pre-symptomatic infectious stage of the disease to have the greatest effect on the probability to cause a pandemic. Here we show that contrary to intuition, the state should not strive for the great expansion of its health care capacities even if its goal is to provide care for all requiring it and minimize the cost of lockdowns

Bilevel linear programs: generalized models for the lower-level reaction set and related problems

Bilevel programming forms a class of optimization problems that model hierarchical relation between two independent decision-makers, namely, the leader and the follower, in a collaborative or conflicting setting. Decisions in this hierarchical structure are made sequentially where the leader decides first and then the follower responds by solving an optimization problem, which is parameterized by the leader's decisions. The follower's reaction, in return, affects the leader's decision, usually through shaping the leader's objective function. Thus, the leader should take into account the follower's response in the decision-making process. A key assumption in bilevel optimization is that both participants, the leader and the follower, solve their problems optimally. However, this assumption does not hold in many important application areas because: (i) there is no known efficient method to solve the lower-level formulation to optimality; (ii) the follower either is not sufficiently sophisticated or does not have the required computational resources to find an optimal solution to the lower-level problem in a timely manner; or (iii) the follower might be willing to give up a portion of his/her optimal objective function value in order to inflict more damage to the leader. This dissertation mainly focuses on developing approaches to model such situations in which the follower does not necessarily return an optimal solution of the lower-level problem as a response to the leader's action. That is, we assume that the follower's reaction set may include both exact and inexact solutions of the lower-level problem. Therefore, we study a generalized class of the follower's reaction sets. This is arguably the case in many application areas in practice, thus our approach contributes to closing the gap between the theory and practice in the bilevel optimization area. In addition, we develop a method to solve bilevel problems through single-level reformulations under the assumption that the lower-level problem is a linear program. The most common technique for such transformations is to replace the lower-level linear optimization problem by its KKT optimality conditions. We propose an alternative technique for a broad class of bilevel linear integer problems, based on the strong duality property of linear programs and compare its performance against the current methods. Finally, we explore bilevel models in an application setting of the pediatric vaccine pricing problem

Optimizing Resource Allocation for COVID-19 Vaccination Planning within Long-Term Care Facilities: A Refined Multilevel Linear Programming Approach

In this paper, we introduce an algorithm designed to solve a Multilevel MOnoObjective Linear Programming Problem (ML(MO)OLPP). Our approach is a refined adaptation of Sinha and Sinha's linear programming method, incorporating the development of an "interval reduction map" that precisely refines decision variable intervals based on the influence of the preceding level's decision maker. Each construction stage is meticulously examined. The effectiveness of the algorithm is validated through a detailed numerical example, illustrating its practical applicability in resource management challenges. With a specific focus on vaccination planning within long-term care facilities and its relevance to the COVID-19 pandemic, our study addresses the optimization of resource allocation, placing a strong emphasis on the equitable distribution of COVID-19 vaccines

Improving the coordination in the humanitarian supply chain: exploring the role of options contract

The uncertainty associated with the location, severity and timing of disaster makes it difficult for the humanitarian organization (HO) to predict demand for the aid material and thereby making the relief material procurement even more challenging. This research explores whether options contract can be used as a mechanism to aid the HO in making procurement of relief material less challenging by addressing two main issues: inventory risk for buyers and over-production risk for suppliers. Furthermore, a contracting mechanism is designed to achieve coordination between the HO and aid material suppliers in the humanitarian supply chain through optimal pricing. The options contract is modelled as a stylized version of the newsvendor problem that allows the HO to adjust their order quantity after placing the initial order at the beginning of the planning horizon. This flexibility helps to mitigate the risk of both overstocking and understocking for the HO as well as the risk of overproduction for the supplier. Our results indicate that the optimal values for decision parameters are not “point estimates” but a range of prices, which can facilitate negotiation between the two parties for appropriate selection of contract parameters under an options contract. The results imply that options contract can aid in the decentralized approach of fixing the prices between the HO and the supplier, which in turn would help in achieving systemic coordination

Three essays on product management : how to offer the right products at the right time and in the right quantity

Across virtually all industries, firms share one common objective: they strive to match their supply with customer demand. To achieve this goal, firms need to offer the right products at the right time and in the right quantity. Only firms that excel in all three dimensions can provide products with a high customer value and achieve extraordinary profits. This thesis investigates specific challenges that a firm has to overcome on its way to a good match between supply and demand. The first essay investigates how a firm can already select the right products during the product development phase. To make good resource allocation decisions, the firm needs to collect valuable information, and incentivize information sharing across the entire organization. The key result is that the firm needs to balance individual and shared incentives to achieve this goal. However, such compensation schemes come at the cost of overly broad product portfolios. The second essay examines how uncertain customer demand patterns affect seasonal products. Specifically, the timing of the product’s availability is crucial. Too early, and high opportunity and inventory costs may devour profits. Too late, and the firm loses its customers. In short, the firm has to balance a product’s market potential with the costly market time. This tradeoff may induce a firm to stock more inventories to satisfy a smaller market potential. Lastly, the third essay investigates how customer substitution influences the inventory decisions of different supply chain members in the presence of upstream competition. We find that customer substitution has a non-monotonic effect on the supply chain members’ decisions, and that left-over inventories may decline even when initial inventories are raised

How adaptation changes the climate game : climate change regimes in a non-cooperative, asymmetric world

The history of the UNFCCC climate negotiations over the past 20 years has shown how difficult it is to reach an international climate agreement that is both legally binding and environmentally effective enough to ensure that humankind can avoid the worst consequences projected from climate change. Some experts even see the world drifting towards a 4°C mean temperature rise. It is therefore necessary to start exploring what future, non-cooperative climate change regimes might be expected to look like. One immediate consequence is that adaptation to climate change has become increasingly relevant; on a humanitarian, political, economic and the scientific level. The economic incentive structure of adaptation is different and, actually, more favourable than that of mitigation, with respect to both their inter- and intratemporal externalities. The ability to adapt makes a higher level of climate change tolerable. Furthermore, my research shows that adaptation empowers the poor to develop and to enforce a more equitable use of the atmospheric carbon sink; it may potentially also lead to an overall reduction of carbon emissions. Ultimately, it turns out that even in a non-cooperative, asymmetric world, there are prospects for clean technology transfer and adaptation funding. Drawing on the AK growth model with climate change developed by Buckle (2009a,b), the aim of this work is (i) to create a tractable, transparent economic growth model that includes climate damages and emissions abatement, (ii) to develop an adequate analytical representation of adaptation, and (iii) to analyze with the help of game-theoretic methods how the option to undertake adaptation affects the strategic nature of climate negotiations and, in particular, the outcome under a non-cooperative climate change regime

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA

STRATEGIC DECISION MAKING IN SUPPLY CHAINS UNDER RISK OF DISRUPTIONS

Ph.DDOCTOR OF PHILOSOPH