Category
Applied
Description
This project investigates how linear programming can be used as a decision-support tool to inform missionary allocation across unreached people groups in India. The central thesis is that quantitative optimization methods can help identify regions of greatest unmet need and minimize the number of resources required to complete the Great Commission. Prior work in operations research has demonstrated the effectiveness of linear programming for resource allocation problems in logistics, healthcare, and humanitarian aid; however, such methods have rarely been applied to missiological contexts. Using figures from Joshua Project’s South Asia data, a mixed-integer linear programming model was constructed with assumptions regarding missionary reach and impact proportions within districts. The model was implemented and solved using Google OR-Tools, producing an optimal allocation across Indian districts. While the model is necessarily simplified and limited by data quality and reach assumptions, it serves as a heuristic framework rather than a prescriptive solution. By having a mathematical voice in fulfilling the Great Commission, leaders can make more informed decisions regarding missionary sending. Potential applications of this model include strategic planning, sensitivity analysis of mission strategies, and adaptation to other humanitarian efforts. Future work may refine assumptions, incorporate additional sociocultural variables, and expand the model into incorporate new regions.
Quantitative Stewardship of the Great Commission: A Linear Programming Approach to Missionary Allocation in India
Applied
This project investigates how linear programming can be used as a decision-support tool to inform missionary allocation across unreached people groups in India. The central thesis is that quantitative optimization methods can help identify regions of greatest unmet need and minimize the number of resources required to complete the Great Commission. Prior work in operations research has demonstrated the effectiveness of linear programming for resource allocation problems in logistics, healthcare, and humanitarian aid; however, such methods have rarely been applied to missiological contexts. Using figures from Joshua Project’s South Asia data, a mixed-integer linear programming model was constructed with assumptions regarding missionary reach and impact proportions within districts. The model was implemented and solved using Google OR-Tools, producing an optimal allocation across Indian districts. While the model is necessarily simplified and limited by data quality and reach assumptions, it serves as a heuristic framework rather than a prescriptive solution. By having a mathematical voice in fulfilling the Great Commission, leaders can make more informed decisions regarding missionary sending. Potential applications of this model include strategic planning, sensitivity analysis of mission strategies, and adaptation to other humanitarian efforts. Future work may refine assumptions, incorporate additional sociocultural variables, and expand the model into incorporate new regions.
