Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2023 Mar 3;23(5):2808. doi: 10.3390/s23052808.
Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal-Wallis test ( = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle.
由于子批的数量和处理时间不同，与现有研究中将子批的生产顺序固定不同，将子批相互混合是实现批量流车间的更实用方法。因此，研究了具有一致和混合子批的批量混合流水车间调度问题（LHFSP-CIS）。建立了混合整数线性规划（MILP）模型，并设计了一种基于启发式的自适应迭代贪婪算法（HAIG），其中包含三项改进。具体来说，提出了一种两层编码方法来解耦基于子批的连接。在解码过程中嵌入了两种启发式算法来减少制造周期。在此基础上，提出了基于启发式的初始化方法来改善初始解的性能；构建了具有四个特定邻域和自适应策略的自适应局部搜索，以提高探索和开发能力。此外，改进了劣解的接受标准以提高全局优化能力。实验和非参数克鲁斯卡尔-沃利斯检验（ = 0）表明，与五种最先进的算法相比，HAIG 在有效性和鲁棒性方面具有显著优势。一个工业案例研究验证了混合子批是提高机器利用率和缩短制造周期的有效技术。
