An Application of Field Trips Planning Optimization: A Humanitarian Concept

In times of high uncertainty, global recession, and lack of adequate funding for humanitarian needs, it is crucial to optimize humanitarian operations to achieve high levels of productivity and efficiency and to cut operational costs without compromising delivery of humanitarian aid to the most vulnerable population. One of the major contributors to cost of operations is transportation costs. In the humanitarian field, fleets are operated to deliver aid and assistance to the needy and most vulnerable population around the globe. These fleets are run in the form of field trips, which constitute a vehicle, a professional driver, humanitarian aid officers, and aid cargo. This study aims at optimizing resources and reducing costs of humanitarian field trips using an automated VBA program. The VBA program utilizes optimization algorithms such as compatibility and overlapping matrices to optimize field trip requisitions. The program also uses the Hungarian method to optimally solve the assignment matrices of vehicles-to-field trips and drivers-to-field trips. The outcome of the study is a user-friendly tool which comprises as inputs: vehicles data, drivers data, field trips history, and field trips requisition plan; and provides as outputs: the optimum list of combined field trips, optimum vehicles-to-field trips assignment based on minimum cost of transportation, and optimum drivers-to-field trips assignment based on the maximum productivity of drivers. The outcome of such a practical and utilizable tool goes beyond the humanitarian context and can be further tested and applied in fleet units of various industries. This study demonstrates the successful application of classical operations research techniques, specifically the assignment problem and the Hungarian method, to address resource allocation challenges in a real-world humanitarian logistics setting, providing a valuable bridge between theory and practice. In addition to mathematical modeling, the research leverages real operational data from a humanitarian fleet management system, validating the tool’s effectiveness on a large dataset of over 3,000 trips. The implementation also accounts for driver fatigue constraints and security conditions in trip combinability, offering a highly realistic and ethical optimization framework. Future extensions may include stochastic modeling, full route optimization, and integration with GIS systems for enhanced operational planning.