Open Access

Müllerklein Daniel

• This cumulative dissertation addresses four problems in resilient network design under disruption uncertainty with a specific focus on waterborne disruptions that affect the transportation costs within the network. The first paper covers the integrated resilient network design problem under transportation cost un-certainty. The focus is on the integration of strategic, tactical, and operational resilience decisions, as well as the influence of the ability to predict disruptions in the short-term, and potential limitations of the operational decisions within capacity limits. The problem is modeled as a two-stage stochastic program. In addition, a problem-specific Benders decomposition algorithm is developed with a non-standard split of decision variables to solve particularly large problem instances. The second paper focuses on the data-driven optimization of the operational inventory replenishment decision under transportation cost uncertainty. The problem is presented as a multi-stage stochastic program. To solve the problem, aThis cumulative dissertation addresses four problems in resilient network design under disruption uncertainty with a specific focus on waterborne disruptions that affect the transportation costs within the network. The first paper covers the integrated resilient network design problem under transportation cost un-certainty. The focus is on the integration of strategic, tactical, and operational resilience decisions, as well as the influence of the ability to predict disruptions in the short-term, and potential limitations of the operational decisions within capacity limits. The problem is modeled as a two-stage stochastic program. In addition, a problem-specific Benders decomposition algorithm is developed with a non-standard split of decision variables to solve particularly large problem instances. The second paper focuses on the data-driven optimization of the operational inventory replenishment decision under transportation cost uncertainty. The problem is presented as a multi-stage stochastic program. To solve the problem, a cost-focused machine learning framework is proposed that uses the replenishment decisions under perfect information to train a decision tree that then learns a cost-optimal replenishment policy. The third paper evaluates the influence of product characteristics by considering the multi-product two-echelon resilient network design problem under transportation cost uncertainty. Both different product characteristics and the dependency of considering multiple products are analyzed. The fourth paper outlines a seven-step framework that covers the end-to-end steps a decision maker needs to follow to increase their supply chain resilience. The framework guides decision makers through the process from outlining clear resilience objectives to monitoring overall and enduring success. In addition, problem specifics and characteristics for the network design problem under waterborne disruptions are outlined to facilitate future research.…