Optimal specialization of a maintenance workforce

• Published in: IIE transactions Vol. 29; no. 5; pp. 423 - 433
• Main Authors: DIETZ, DENNIS C., ROSENSHINE, MATTHEW
• Format: Journal Article
• Language: English
• Published: Norcross, GA Taylor & Francis Group 01.05.1997; Institute of Industrial Engineers; Taylor & Francis Ltd
• Subjects: Aircraft; Algorithms; Applied sciences; Calibration; Exact sciences and technology; Expenditures; Linear programming; Machinery; Maintenance management; Manufacturing; Military problems; Operational research and scientific management; Operational research. Management science; Operations research; Optimization; Reliability theory. Replacement problems; Simulation; Skills; Specialization; Studies; Transportation services; Workforce; Military application; Manpower; Optimal strategy; Markov decision; Allocation; Milling machine; Linear programming; Maintenance; Queueing network; Aircraft
• ISSN: 0740-817X; 2472-5854; 1545-8830; 2472-5862
• DOI: 10.1080/07408179708966348

This article develops an analytical method for determining an optimal specialization strategy for a maintenance workforce. The method assumes that maintenance tasks are generated by a system of statistically identical machines that experience random malfunctions and require periodic service. The impact of alternative workforce structures on system performance is evaluated with a queueing network model. Markov decision analysis is employed to determine an optimal assignment of maintenance personnel to pending tasks as the network status varies over time. A linear programming algorithm is derived to enable simultaneous optimization of specific assignment decisions and the overall workforce structure. A manufacturing example demonstrates the applicability of the method to many industrial contexts. The method is also applied to the problem of maximizing military aircraft sortie generation subject to a constraint on maintenance personnel expenditure.