Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms |
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Authors: | Gang Quan Garrison W. Greenwood Donglin Liu Sharon Hu |
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Affiliation: | 1. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA;2. Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, USA;3. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA |
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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. |
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Keywords: | Evolutionary computations Scheduling Utility theory Preventive maintenance Multi-objective optimization |
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