Browsing by Subject "Dynamic scheduling"

Now showing 1 - 2 of 2

Open Access
Dynamic resource scheduling of biomanufacturing projects
(Elsevier, 2020-05) Limon, Yasemin; Krishnamurthy, A.
We consider the scheduling problem for biomanufacturing projects that involve multiple tasks and “no-wait” constraints between some of these tasks. The aim is to create schedules that ensure timely delivery of products while enabling schedule revisions to accommodate additional constraints realized during the execution of these projects. We formulate the problem as a mixed-integer linear programming model with the objective of minimizing total tardiness, and propose a dynamic scheduling approach that solves a series of modified mixed-integer linear programming models to revise and improve the schedule. We conduct numerical studies to investigate the performance of this approach and compare its performance to a traditional proactive scheduling approach. In collaboration with biomanufacturing companies, we create a scheduling tool and validate the approach with implementation in an industry setting.
Open Access
Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining
(Taylor & Francis, 2010) Metan, G.; Sabuncuoglu, I.; Pierreval, H.
A new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.