半导体晶圆节能分布式制造与预维护联合优化

针对半导体晶圆节能分布式制造与预维护联合优化问题，构建了同时考虑制造阶段和检测修复阶段，以最小化最大完工时间、总碳排放和总预维护成本为优化目标的两阶段绿色调度模型，提出了改进的混合多目标灰狼优化（improved hybrid multi-objective grey wolf optimization，IHMGWO）算法，设计了工厂分配策略、机器分配策略以及考虑维修工人柔性的同步调度维护策略的解码方案。通过设计初始化种群融合策略、捕食行为搜索策略、子种群变异策略，提高了算法的寻优性能。360个测试算例的对比实验表明，所提出的IHMGWO算法针对SP指标能够实现大部分占优，针对IGD和\mathrm{\Omega }指标能够实现全部占优，对于解决该类问题具有显著的优势和竞争力。

Research on Joint Optimization of Energy-Saving Distributed Manufacturing and Preventive Maintenance for Semiconductor Wafers

Aiming at the joint optimization problem of energy-saving distributed manufacturing and preventive maintenance for semiconductor wafers, a two-stage green scheduling model considering both the manufacturing stage and the inspection and repair stage is established to minimize the makespan, the total carbon emissions and the total preventive maintenance cost. An improved hybrid multi-objective grey wolf optimization (IHMGWO)algorithm is proposed. The decoding schemes of factory allocation strategy, machine allocation strategy and synchronous scheduling maintenance strategy considering the flexibility of maintenance workers are designed in IHMGWO. By designing the initial population fusion strategy, predation behavior search strategy, and sub-population mutation strategy, the optimization performance of the algorithm is improved. The comparative experiments of 360 test cases show that the IHMGWO algorithm proposed in this paper can achieve most dominance for SP indicator and all dominance for IGD and \mathrm{\Omega } indicator, which has certain advantages and competitiveness for solving such problems.