Scheduling and Logistics Optimization for Batch Manufacturing Processes With Temperature Constraints and Alternative Thermal Devices

Batch scheduling problems are NP-hard and often coupled with logistics optimization problems in industrial manufacturing scenarios, further increasing the challenge of decision-making. This work focuses on a stainless steel hot-rolling production process, where it is essential to maintain the temperature of the products from the previous process beyond a certain threshold. Our work diverges from typical hot-rolling production processes by necessitating the utilization of alternative thermal devices, thereby intricately linking batch scheduling with logistics optimization and resulting in a novel lexicographical dual-objective optimization problem. To address this complex problem, we first introduce a mathematical model formulated as a mixed integer program, providing exact solutions of the concerned problem but taking much computation time. To provide effective and efficient solutions, we then propose an enhanced simulated annealing algorithm, which integrates destruction and construction methods inspired by iterated greedy algorithms. This algorithm is tailored to the specific characteristics of the problem, incorporating specialized encoding-decoding mechanisms, neighborhood search operators, and a Metropolis acceptance criterion. Our experimental results highlight the effectiveness of proposed approaches, demonstrating their superiority over competitive peers. Thus, this research contributes valuable insights and innovative solutions to the scheduling and optimization challenges inherent in batch manufacturing processes with temperature constraints and thermal devices.