Quantifying and mitigating decentralized decision making in humanitarian logistics systems

Abstract

Humanitarian and public health logistics systems are often characterized by decentralized decision makers in the form of response agencies who establish supply chains and the beneficiaries who access them. While classical models assume there is a single decision maker with a global objective and authority, decentralized systems consist of multiple decision makers, each with accomplishing his own objective and scope of control. The literature demonstrates that decentralized systems often perform poorly when compared to their hypothetical centralized counterparts. However, there exist few models in the literature to quantify the impact of decentralization and mechanisms for its mitigation are deficient. This research advances knowledge of decentralized systems through new game theory and optimization models, solution methodologies and theoretical characterizations of system performance. First, the author presents a literature review that synthesizes research regarding the facets of humanitarian operations that can benefit from the application of game theory. The author finds that models of decentralized behavior lack realism, neglecting sources of uncertainty, dynamism and personal preferences that influence individuals' decisions. These findings motivate the remaining components of the thesis. Next, the author focuses on decentralization on the part of response agencies who open service facilities. Decentralization can adversely impact patient access and equity, both critical factors in humanitarian contexts. A dynamic, robust facility location model is introduced to enable a comparison between a given decentralized response and a hypothetical coordinated response using identical resources. The value of the model is demonstrated through a computational study of the response to a recent cholera epidemic. Finally, the author introduces game theory models that represent the decisions of beneficiaries seeking relief. The models account for distance, congestion, and the relative importance an individual places on the two. The author constructs an algorithm that computes a decentralized solution in polynomial time. The author quantifies decentralized system performance in comparison to centralized control, bounding the cost of decentralized decision making for the least and most costly outcomes. The author identifies coordination mechanisms encourage centrally optimal decisions within decentralized systems.