Resource-Constrained Parallel Machine Scheduling for Tire Manufacturing

This study aims to optimize the scheduling process in tire manufacturing, with a particular focus on the scheduling of tread, one of the key tire components. The problem is formulated as a resource-constrained parallel machine scheduling problem, with the objective of minimizing total tardiness and setup time. One of the main challenges in this problem is managing carts, which are renewable resources required from tread production through to the completion of tire assembly. After the assembly process is completed, the carts are released for reuse in tread production, further complicating the scheduling process. To tackle this NP-hard problem, we propose a novel iterated greedy algorithm incorporating the block-split method in the construction phase. This effectively reduces the solution space and facilitates escaping from local optima, as the block size is dynamically adjusted during the construction phase. In the experiments, we validate the proposed methodology by comparing it with constraint programming, demonstrating that the proposed algorithm achieves optimal performance for small-sized instances. Furthermore, the proposed method outperforms other heuristic approaches, particularly in solving large-scale problems. Currently, this algorithm is being implemented in a real-world tire manufacturing plant operated by one of the largest companies in South Korea.