Decision Sciences Journal
Volume 31, Number 4
Fall 2000
Workforce-constrained Preventive Maintenance Scheduling Using
Evolution Strategies
Sanjay Ahire
Department of MIS and Decision Sciences, School of Business Administration,
University of Dayton, 300 College Park, Dayton, OH 45469-2130,
email: ahire@notes.udayton.edu
Garrison Greenwood
Department of Electrical and Computer Engineering, Portland State
University, Portland, OR 97207-0751, email: greenwd@ee.pdx.edu
Ajay Gupta
Department of Computer Science, Western Michigan University,
1903 W. Michigan Ave., Kalamazoo, MI 49008-5371, email: ajay.gupta@wmich.edu
Mark Terwilliger
Department of Mathematics and Computer Science, Lake Superior
State University, MI 49783, email: mterwilliger@lakers.lssu.edu
ABSTRACT. Heavy equipment overhaul facilities such
as aircraft service centers and railroad yards face the challenge
of minimizing the makespan for a set of preventive maintenance
(PM) tasks, requiring single or multiple skills, within workforce
availability constraints. In this paper, we examine the utility
of evolution strategies to this problem. Comparison of the computational
efforts of evolution strategies with exhaustive enumeration to
reach optimal solutions for 60 small problems illustrates the
ability of evolution strategies to yield optimal solutions increasingly
efficiently with increasing problem size. A set of 852 large-scale
problems was solved using evolution strategies to examine the
effects of task-related problem characteristics, workforce-related
variables, and evolution strategies population size (m) on CPU
time. The results empirically supported practical utility of
evolution strategies to solve large-scale, complex preventive
maintenance problems involving single- and multiple-skilled workforce.
Finally, comparison of evolution strategies and simulated annealing
for the 852 experiments indicated much faster convergence to
optimality with evolution strategies.
Subject Areas: Evolution Strategies, Preventive Maintenance,
and Workforce Scheduling. |