Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.contributor | Department of Computing | en_US |
| dc.creator | Lee, Miu-ling Rosa | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/5147 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Solving labour scheduling problems in line maintenance using genetic algorithms | en_US |
| dcterms.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. | en_US |
| dcterms.extent | vi, [280] p : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Aircraft industry -- Management -- Data processing | en_US |
| dcterms.LCSH | Scheduling -- Data processing | en_US |
| dcterms.LCSH | Genetic algorithms | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15321605.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 9.86 MB | Adobe PDF | View/Open |
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