Solving labour scheduling problems in line maintenance using genetic algorithms

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Solving labour scheduling problems in line maintenance using genetic algorithms

 

Author: Lee, Miu-ling Rosa
Title: Solving labour scheduling problems in line maintenance using genetic algorithms
Degree: M.Sc.
Year: 2000
Subject: Aircraft industry -- Management -- Data processing
Scheduling -- Data processing
Genetic algorithms
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: vi, [280] p : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1532160
URI: http://theses.lib.polyu.edu.hk/handle/200/5147
Abstract: Labour scheduling in service organizations is tough because demands are fluctuating and uncertain. In most of the cases, the scheduling will involve tour scheduling and task assignment for labour of different capabilities. The problem presented in this dissertation is in the context of the aircraft line maintenance, but it applies equally well to a diverse set of round-the-clock service operations. The primary scheduling objective is the minimization of overstaffing in the face of significant hourly and daily fluctuations in staffing requirements of different task types. Contributing to scheduling complexity are constraints on the structure of work tours, including minimum and maximum shift lengths, shift start/end times, minimum rest time between shifts. A goal programming formulation of a representative problem is shown to be too large to be solved optimally. Existing heuristic procedures related to this area are either focusing on the homogeneous work force or on aggregated task-type assignment. Subsequently, the individual task assignment will be left behind to the schedule planner. This is not practical if the number of tasks is large. In view of this, this dissertation intends to propose, as much as possible to the schedule planner, the tours as well as individual task assignments so that he/she can focus on only a small number of tasks. Two genetic algorithms are formulated in this paper. One is for the work tour learning while the other is for the task assignment after the work tours are formulated. The test result is quite promising. When comparing to the existing tour-assignment, both the excess minutes and the uncovered minutes are reduced in just 300 iterations and a few minutes execution time. Even though the uncovered minutes cannot be totally eliminated, they are mainly caused by the tour constraints. Hence, GA could be an easy and encouraging approach in tackling the large-scale labour scheduling problem in line maintenance.

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