A Quantitative Investigation into Parochial Restructuring in the Diocese of Trenton

Molina, Carlos [Browse]
Senior thesis


van Handel, Ramon [Browse]
Princeton University. Department of Operations Research and Financial Engineering [Browse]
Princeton University. Program in Applications of Computing [Browse]
Class year
Summary note
As caretakers of a number of parishes, diocesan bishops rely on national statistics and forecasts, weekly attendance numbers, and sacramental records to make periodic decisions about changes to parish organization. Bishop David O'Connell's "Faith in Our Future" initiative for the Diocese of Trenton, promulgated in 2017, is one such example [50]. C.A.R.A. researcher Dr. Gautier has studied the responses of diocesan planners to parishes with more Catholics, falling mass attendances, or limited numbers of priests [33]. The Catholic spiritual focus on the parish church as a heart of its sacramental rites rightly creates a significant reluctance to applying more rigorous operations research principles to its planning. On one hand, the explicit bias against models that do not take particular character and vitality of the churches can be seen as a real constraint for optimization, especially amidst questions brought on by the so-called "vocations crisis." On the other hand, the simplicity of existing models is an opening for academic inquiry to add a complementary perspective to parochial planning in the Diocese of Trenton. A resource-allocation-perspective-based model can be created for this situation using a MILP variation of the transportation problem, with constraints on the number of masses and the capacities of the churches. Parishioner can be selectively grouped into clusters with techniques like k-medoids clustering. The transportation cost can be provided with any reasonable measure, including one borrowed from the load re-balancing problem. The parameters of this one-period MILP, like priest-count and parishioner attendance rate, can be varied for robustness. Separately, hierarchical clustering can be used to group the growth profiles of parishes based on historical correlations in attendances. Improvements can come in the form of better distance measures, record-keeping, or stochastic perspectives.

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