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Navigating the Best Path to Optimality in a University Grants Administration Workload Assignment Problem
Author(s) -
Martin Megan Wydick,
Ragsdale Cliff
Publication year - 2020
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12440
Subject(s) - workload , status quo , computer science , integer programming , operations research , linear programming , task (project management) , scheduling (production processes) , operations management , economics , management , mathematics , algorithm , market economy , operating system
This research addresses a preaward grant administration workload‐to‐agent assignment problem that occurs in the office of research and sponsored programs (ORSP) at many universities. We first identify the optimal (utopian) workload assignment plan using a mixed‐integer linear programming problem. This optimal assignment of academic department workload to ORSP administrators may differ considerably from the status quo, requiring multiple reassignments from the current state to reach optimality. The number of reassignments raises concerns related to loss of administrator–department relationship, loss of department‐related knowledge, and increase in managerial inconvenience. To achieve the best workload reassignment with the fewest changes from the current status quo, while still placing a greater emphasis on the effective use of limited resources, we propose and illustrate a multiple objective optimization technique to identify the N best departmental reassignments from the current state that provide the greatest progress toward the utopian balanced workload solution. Solving this problem over several values of N and plotting the results allow the decision maker to visualize the trade‐off between the number of reassignments and the resulting progress achieved toward the utopian solution. This system could support the manager of pre‐award administrators in making an informed decision about the best number of reassignments to choose based on an objective assessment of the relevant trade‐offs. The technique proposed in this article is widely relevant with slight modifications to the mathematical model. Additional applications include the management of employee‐to‐task assignments in home healthcare, youth sports, nurse scheduling, blood bank facilities, and early education.