A Hierarchical Production Planning and Finite Scheduling Framework for Part Families in Flexible Job-shop (with a case study)
محورهای موضوعی : Design of Experiment
Davod Abachi
1
(
Graduated from QIAU
)
fariborz jolai
2
(
teacher
)
hasan haleh
3
(
teacher
)
کلید واژه: production planning, Finite Scheduling, Part Families, Flexible Job-shop,
چکیده مقاله :
Tendency to optimization in last decades has resulted in creating multi-product manufacturing systems. Production planning in such systems is difficult, because optimal production volume that is calculated must be consistent with limitation of production system. Hence, integration has been proposed to decide about these problems concurrently. Main problem in integration is how we can relate production planning in medium-term timeframe with scheduling in short-term timeframe. Our contribution creates production planning and scheduling framework in flexible job shop environment with respect to time-limit of each machine in order to produce different parts families in automotive industry. Production planning and scheduling have iterative relationship. In this strategy information flow is transformed reciprocative between production planning and scheduling for satisfying time-limit of each machine. The proposed production planning has heuristic approach and renders a procedure to determine production priority of different part families based on safety stock. Scheduling is performed with ant colony optimization and assigns machine in order of priority to different part families on each frozen horizon. Results showed that, the proposed heuristic algorithm for planning decreased parts inventory at the end of planning horizon. Also, results of proposed ant colony optimization was near the optimal solution .The framework was performed to produce sixty four different part families in flexible job-shop with fourteen different machines. Output from this approach determined volume of production batches for part families on each frozen horizon and assigned different operations to machines.
