A learning-based schedulıng system wıth continuous control and update structure
Author(s)
Advisor
Sabuncuoğlu, İhsanDate
2005Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
In today’s highly competitive business environment, the product varieties of firms
tend to increase and the demand patterns of commodities change rapidly. Especially
for high tech industries, the product life cycles become very short and the customer
demand can change drastically due to the introduction of new technologies in the
market (i.e., introduction by the competitors). These factors increase the need for
more efficient scheduling strategies. In this thesis, a learning-based scheduling system
for a classical job shop problem with the average tardiness objective is developed.
The system learns on the manufacturing environment by constructing a learning tree
and selects a dispatching rule from the tree for each scheduling period to schedule the
operations. The system also utilizes the process control charts to monitor the
performance of the learning tree and the tree as well as the control charts is updated
when necessary. Therefore, the system adapts itself for the changes in the
manufacturing environment and survives in time. Also, extensive simulation
experiments are performed for the system parameters such as monitoring (MPL) and
scheduling period lengths (SPL). Our results indicate that the system performance is
significantly affected by the parameters (i.e., MPL and SPL). Moreover, simulation
results show that the performance of the proposed system is considerably better than
the simulation-based single-pass and multi-pass scheduling algorithms available in the
literature
Keywords
Scheduling,Dispatching Rules
Dispatching Rules
AI
Job Shop Scheduling
Control Charts
Data Mining
Machine Learning