Abstract On-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be well investigated. In this paper, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setup. The first stage provides optimal schedules to minimize the total completion time. The second stage determines the energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. The power demand of the production is met by on-site renewable generation, supply from the main grid, and energy storage system. An epsilon-constraint algorithm integrated with L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers. Our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy saves energy costs significantly. While, utilizing solar energy can reduce more.

中文翻译：

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基于美国案例的现场可再生能源发电流水车间调度的随机优化

摘要 现场可再生能源为制造工厂在面临时变电价时降低能源成本提供了巨大的机会。为了有效地利用现场可再生能源发电，需要对生产计划和能源供应决策进行深入研究。在本文中，我们为具有序列相关设置的流水车间提出了一个两阶段、多目标的随机程序。第一阶段提供最佳时间表以最小化总完成时间。第二阶段确定能源供应决策，以在分时电价方案下将能源成本降至最低。生产的电力需求由现场可再生能源发电、主电网供电和储能系统满足。针对该问题，提出了一种结合L型方法的epsilon约束算法。为决策者提供了多组帕累托最优解。我们的结果表明，设置操作的能源成本相对较高，因此不容忽视。此外，使用太阳能或风能可显着节省能源成本。而利用太阳能可以减少更多。