Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms

@article{quan07, abstract = {Heavy industry maintenance facilities at aircraft service centers or railroad yards must contend with scheduling preventive maintenance tasks to ensure critical equipment remains available. The workforce that performs these tasks are often high-paid, which means the task scheduling should minimize worker idle time. Idle time can always be minimized by reducing the workforce. However, all preventive maintenance tasks should be completed as quickly as possible to make equipment available. This means the completion time should be also minimized. Unfortunately, a small workforce cannot complete many maintenance tasks per hour. Hence, there is a tradeoff: should the workforce be small to reduce idle time or should it be large so more maintenance can be performed each hour? A cost effective schedule should strike some balance between a minimum schedule and a minimum size workforce. This paper uses evolutionary algorithms to solve this multiobjective problem. However, rather than conducting a conventional dominance-based Pareto search, we introduce a form of utility theory to find Pareto optimal solutions. The advantage of this method is the user can target specific subsets of the Pareto front by merely ranking a small set of initial solutions. A large example problem is used to demonstrate our method.}, author = {Quan, Gang and Greenwood, Garrison W. and Liu, Donglin and Hu, Sharon }, citeulike-article-id = {2945402}, doi = {http://dx.doi.org/10.1016/j.ejor.2005.12.015}, journal = {European Journal of Operational Research}, keywords = {theory, utility}, month = {March}, number = {3}, pages = {1969--1984}, posted-at = {2008-06-30 16:54:30}, priority = {4}, title = {Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms}, url = {http://dx.doi.org/10.1016/j.ejor.2005.12.015}, volume = {177}, year = {2007} }

TY - JOUR ID - quan07 L3 - citeulike-article-id:2945402 TI - Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms JF - European Journal of Operational Research VL - 177 IS - 3 SP - 1969 EP - 1984 N2 - Heavy industry maintenance facilities at aircraft service centers or railroad yards must contend with scheduling preventive maintenance tasks to ensure critical equipment remains available. The workforce that performs these tasks are often high-paid, which means the task scheduling should minimize worker idle time. Idle time can always be minimized by reducing the workforce. However, all preventive maintenance tasks should be completed as quickly as possible to make equipment available. This means the completion time should be also minimized. Unfortunately, a small workforce cannot complete many maintenance tasks per hour. Hence, there is a tradeoff: should the workforce be small to reduce idle time or should it be large so more maintenance can be performed each hour? A cost effective schedule should strike some balance between a minimum schedule and a minimum size workforce. This paper uses evolutionary algorithms to solve this multiobjective problem. However, rather than conducting a conventional dominance-based Pareto search, we introduce a form of utility theory to find Pareto optimal solutions. The advantage of this method is the user can target specific subsets of the Pareto front by merely ranking a small set of initial solutions. A large example problem is used to demonstrate our method. KW - theory KW - utility AU - Quan, Gang AU - Greenwood, Garrison AU - Liu, Donglin AU - Hu, Sharon PY - 2007/03/16/ UR - http://dx.doi.org/10.1016/j.ejor.2005.12.015 ER -