Optimization and measurement in humanitarian operations: addressing practical needs
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This thesis focuses on three topics relevant to humanitarian applications: (i) stable and complete assignment of staff members to field offices, (ii) bottleneck management for transportation networks, and (iii) performance measurement of the food assistance supply chain. The assignment and reassignment of personnel to jobs is a large-scale problem faced by many organizations including the military and multi-national organizations. Although successful algorithms have been developed that can ensure matchings that are stable (without incentive to deviate), not all practical concerns have been addressed by these algorithms. For example, the gap we study is that when staff members do not provide preference lists covering all jobs, a complete stable matching is not guaranteed. In the first part of the thesis, we model negotiations, which occur in practice, as part of the problem of matching all agents. We introduce algorithms and structural results for when the organization negotiates with specific agents to modify their preference lists and the centralized objective is to minimize the number or cost of negotiations required to achieve complete stable matchings. An uncertain environment with disruptions is a reality faced by many humanitarian operations but not fully addressed in the literature. Transportation delays are often driven by reliability issues (e.g., customs delays, strikes, and the availability of transport), and the length of wait time can be influenced by congestion. In the second part of the thesis, we describe a queuing model with breakdowns to model delays in port and transportation corridors (the overland travel from discharge ports to delivery points). Using the model, we gain insights into where delays are most detrimental to system performance (i.e., the network's "bottleneck") in port and transportation corridors. We then include our delay modeling in a convex cost network flow model that determines optimal routing when several port and corridor options are available. Finally, we examine a resource allocation model for where to invest in improvements to minimize delay. Throughout, we compare solutions using the optimal approach to rules of thumb and identify important factors that might be missing in practical decision making currently. Third, we present a case study on the implementation of supply chain key performance indicators (KPIs) at a large humanitarian organization. We describe (i) the phases necessary for a full implementation of supply chain KPIs at a humanitarian or non-profit organization, (ii) how to address strategy, mindset, and organizational barriers, and (iii) how to adapt commercial supply chain KPI frameworks to the humanitarian sector, factoring in implementation constraints present in the humanitarian sector that may impact KPI development. Last, a conclusion chapter discusses areas where this research may or may not generalize for each of the three topics studied.