Barge Prioritization, Assignment, and Scheduling During Inland Waterway Disruption Responses

Inland waterways face natural and man-made disruptions that may affect navigation and infrastructure operations leading to barge traffic disruptions and economic losses. This dissertation investigates inland waterway disruption responses to intelligently redirect disrupted barges to inland terminals and prioritize offloading while minimizing total cargo value loss. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). A previous study formulated the CPTAP as a non-linear integer programming (NLIP) model solved with a genetic algorithm (GA) approach. This dissertation contributes three new and improved approaches to solve the CPTAP. The first approach is a decomposition based sequential heuristic (DBSH) that reduces the time to obtain a response solution by decomposing the CPTAP into separate cargo prioritization, assignment, and scheduling subproblems. The DBSH integrates the Analytic Hierarchy Process and linear programming to prioritize cargo and allocate barges to terminals. Our findings show that compared to the GA approach, the DBSH is more suited to solve large sized decision problems resulting in similar or reduced cargo value loss and drastically improved computational time. The second approach formulates CPTAP as a mixed integer linear programming (MILP) model improved through the addition of valid inequalities (MILP\u27). Due to the complexity of the NLIP, the GA results were validated only for small size instances. This dissertation fills this gap by using the lower bounds of the MILP\u27 model to validate the quality of all prior GA solutions. In addition, a comparison of the MILP\u27 and GA solutions for several real world scenarios show that the MILP\u27 formulation outperforms the NLIP model solved with the GA approach by reducing the total cargo value loss objective. The third approach reformulates the MILP model via Dantzig-Wolfe decomposition and develops an exact method based on branch-and-price technique to solve the model. Previous approaches obtained optimal solutions for instances of the CPTAP that consist of up to five terminals and nine barges. The main contribution of this new approach is the ability to obtain optimal solutions of larger CPTAP instances involving up to ten terminals and thirty barges in reasonable computational time

ROBUST DECISION-MAKING AND DYNAMIC RESILIENCE ESTIMATION FOR INTERDEPENDENT RISK ANALYSIS

When systems and subsystems are put under external shocks and duress, they suffer physical and economic collapse. The ability of the system components to recover and operate at new stable production levels characterizes resilience. This research addresses the problem of estimating, quantifying and planning for resilience in interdependent systems, where interconnectedness adds to problem complexity. Interdependence drives the behavior of sectors before and after disruptions. Among other approaches this study concentrates on economic interdependence because it provides insights into other levels of interdependence. For sectors the normalized losses in economic outputs and demands are suitable metrics for measuring interdependent risk. As such the inoperability input-output model enterprise is employed and expanded in this study to provide a useful tool for measuring the cascading effects of disruptions across large-scale interdependent infrastructure systems. This research defines economic resilience for interdependent infrastructures as an "ability exhibited by such systems that allows them to recover productivity after a disruptive event in a desired time and/or with an acceptable cost". Through the dynamic interdependent risk model resilience for a disrupted infrastructure is quantified in terms of its average system functionality, maximum loss in functionality and the time to recovery, which make up a resilience estimation decision-space. Estimating such a decision-space through the dynamic model depends upon the estimation of the rate parameter in the model. This research proposes a new approach, based on dynamic data assimilation methods, for estimating the rate parameter and strengthening post-disaster resilience of economic systems. The solution to the data assimilation problem generates estimates for the rate of resilient recovery that reflects planning considerations interpreted as commodity substitutions, inventory management and incorporating redundancies. The research also presents a robust optimization based risk management approach for strengthening interdependent static resilience estimation. There is a paucity of research dealing with quantification and assessment of uncertainties in interdependency models. The focus here is more on the extreme bounds of event and data uncertainties. The deterministic optimization becomes a robust optimization problem when extremes of uncertainties are considered. Computationally tractable robust counterparts to nominal problems are presented here. Also presented in this research is a discrete event simulation based queuing model for studying multi-modal transportation systems with particular focus on inland waterway ports. Such models are used for impact analysis studies of inland port disruptions. They can be integrated with the resilience planning methodologies to develop a framework for large-scale interdependent risk and recovery analysis