Operational level production scheduling by genetic algorithm | |
| Author | Hirun Duangurai |
| Call Number | AIT Thesis no. ISE-01-09 |
| Subject(s) | Production scheduling Genetic algorithms |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-01-09 |
| Abstract | Production scheduling is one of the important paiis in the factory management process. Without systematic scheduling, resources may not be utilized efficiently. A good scheduling system is a good beginning toward better on-time delivery to customers. This thesis paper is aims to improve the scheduling performance. Several algorithms: genetic algorithm (GA) with two fitness functions: minimizing mean flow time, and minimizing mean tardiness in forward and backward direction of scheduling, genetic algorithm with minimizing completion time in forward direction of scheduling, and non-delay heuristic scheduling (NDHS) by Giffler and Thomson algorithm with shmiest processing time (SPT) rule and most work remaining (MWR) in forward and backward direction of scheduling; are considered for implementation in the production process of the company. These algorithms are compared and chosen for applications under different conditions. The conditions concerned in the experiment are number of paii in the scheduling, number of workstation, and level of complexity of the product structure. The results from the experiment are shown that in forward direction of scheduling, GA with completion time fitness function and NDHS with SPT rule are suitable for scheduling with the objective to minimize completion time, GA with mean flow time fitness function and GA with mean tardiness fitness function are suitable for scheduling with the objective to minimize mean tardiness. In backward direction of scheduling, GA with mean flow time fitness function is the proper algorithm in scheduling with the aim to minimize lateness, mean flow time, and number of tardy job. GA with mean tardiness fitness function is good for scheduling with the objective to minimize mean tardiness and to minimize number of tardy job. |
| Year | 2001 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-01-09 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Voratas Kachitvichyanukul; |
| Examination Committee(s) | Manukid Parnichkun;Ullah, A.M.M. Sharif; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2001 |