A Hybrid Linear Programming and Heuristic Approach for Production Scheduling—A Case Study in Automotive Part Manufacturing

Abstract

Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints.