Abstract: Managers within manufacturing plants confront increasingly intricate scenarios, necessitating efforts to minimize manufacturing lead times amidst the destabilizing impact of random failures. Concurrently, they must also endeavor to curtail energy costs within time-of-use tariffs, thereby bolstering the price competitiveness of products. Focusing on the discrete flow shop, this study incorporates considerations of energy consumption costs within the framework of TOU tariff policies and the stochastic nature of equipment failures. Through the integration of production scheduling and equipment maintenance, this study aims to devise a cohesive modeling approach that enables comprehensive planning for both activities. The devised integrated optimization scheme outlined in this paper is poised to significantly aid enterprises in achieving peak shaving and valley filling, alongside cost reduction and efficiency enhancements under time-sharing tariff policies. Furthermore, it offers valuable insights for workshop managers seeking to formulate judicious and effective production plans within complex and uncertain production environments.
   This study focuses on multiple interrelated dimensions within the manufacturing shop, establishing a mathematical model that encompasses three decision dimensions: production scheduling, equipment maintenance, and energy allocation. A two-layer algorithm is devised to tackle this model effectively. Firstly, a method based on surrogate measures is designed to evaluate the performance of solutions. Then, a metaheuristic algorithm is designed combining the NSGA-II framework and the constructive-heuristic rules to search the Pareto curve of this multi-objective problem. Data are acquired through Monte Carlo simulation. Under the assumption that random faults follow an exponential distribution, random numbers are generated by sampling iteratively to simulate the system, followed by conducting several tests.
   In the algorithm’s validation phase, Monte Carlo sampling simulation is employed to compute the expected value of the objective function within the inner algorithm. Subsequently, an appropriate proxy index function is devised to effectively approximate the expected target value, significantly reducing operational time while maintaining a certain level of precision. Comparative analysis of VEGA reveals substantial enhancements in the robustness index of the NSGA-II algorithm, formulated within the outer algorithm, along with improvements in product delivery time and electricity cost. The intricate strategy proposed herein for model validation effectively mitigates electricity costs. In essence, the incorporation of buffer time insertion and the algorithm outlined in this study enhances system performance concerning stability and electricity cost indices when encountering random faults. Our findings demonstrate the efficacy of buffer time insertion: it mitigates fault impacts on subsequent processes, ensuring current process stability, and diminishes the proportion of processing time subject to peak electricity prices, thus economizing on electricity costs. Additionally, our investigation indicates that an increase in buffering time leads to a gradual reduction in peak power, further curtailing total electricity costs. However, practical implementation may be constrained by extended delivery times, rendering this approach less advisable in practice.
   In future research, the problem can be extended by addressing the following two aspects: (1)Assessing the influence of renewable energy power supply modes on all facets of the system. (2)Investigating pertinent issues within alternative production layouts, such as flexible assembly line shops, open shops, and others.

Key words: flowshop, production scheduling, maintenance, energy demand, multi-objective

摘要： 当今车间管理者不仅需要在最小化制造工期的同时尽可能应对设备随机故障等因素引起的不稳定性,更需要节省分时电价模式下的能耗成本以提高产品价格竞争力。本研究以考虑设备随机故障的流水线车间为研究对象,以工件交货期、系统鲁棒性、能耗成本为目标,建立了涉及生产调度、设备维护、能量分配三个维度的模型。首先,以代理指标方法结合蒙特卡洛仿真验证解决了不确定环境下的可行解评估问题;其次以NSGA-II框架为基础结合启发式解码方法以搜索此多目标问题的帕累托曲线。数值实验验证了所提目标评估方法的有效性以及集成方案相较于传统规则的优越性;在各工序间插入合适的缓冲时间,既可以吸收故障冲击以提高系统鲁棒性,又可以调整设备开机时段以降低总能耗成本。

关键词: 流水线, 生产调度, 设备维护, 能耗需求, 多目标优化