ارين ديندارمهر

The development of advanced technologies, increasing customer deman‎d for high-quality products at competitive prices, an‎d intensified competition among automotive manufacturers have significantly increased the importance of production planning in discrete manufacturing systems within the automotive industry. Moreover, the growing complexity of production processes, high product an‎d component variety, an‎d structural dependencies among them have transformed production planning into a multidimensional problem under uncertainty. One of the most critical challenges faced by automotive parts manufacturers is dealing with various sources of uncertainty, including deman‎d fluctuations, production capacity limitations, supply chain disruptions, an‎d environmental changes. In this research, a production planning model for discrete manufacturing systems in the automotive parts industry is proposed, in which deman‎d uncertainty is explicitly taken into account. The model structure incorporates the dependency between finished products an‎d subcomponents through the bill of materials (BOM), an‎d production decisions are determined over a multi-period planning horizon. To address uncertainty, a robust scenario-based planning approach is employed. The objective function is formulated in a composite manner, aiming to simultaneously minimize order shortages an‎d prevent customer line stoppages, balance inventory levels relative to target values, an‎d reduce production plan instability. The proposed model is formulated as a mixed-integer linear programming (MILP) model an‎d is eva‎luated using real operational data from an automotive parts manufacturing company in Iran. The results demonstrate that the model is capable of generating a more stable an‎d efficient production plan under uncertain conditions, thereby improving productivity, reducing costs, an‎d enhancing the service level in similar companies. The validation of the proposed model is conducted using real-world data by comparing its results with the company’s actual 12-day production planning an‎d routine production plan. The comparison results indicate that the objective function value of the proposed model is reduced by approximately 98.73% an‎d 98.39%, respectively, compared to the actual 12-day planning an‎d the routine production plan, reflecting a significant improvement in reducing shortages, controlling inventory levels, an‎d increasing production stability. Finally, the proposed models are implemented in a real operational environment, an‎d the obtained results confirm substantial improvements in achieving the defined objectives.

برنامه ريزي توليد سيستم هاي گسسته , صنعت قطعات خودرو , عدم قطعيت , بهينه سازي , برنامه ‌ريزي استوار سناريو محور , متعادل سازي سطوح موجودي

Discrete Production Planning Systems , Automotive Parts Industry , Uncertainty , Optimization , Robust Scenario-Based Planning , Inventory Level Balancing

Aryan Dindarmehr

Dr. Ahmad Makui