Abstract This paper proposes a novel approach for generating decision rules to exercise flexibility in capacity expansion. The proposed approach differs from other decision rule generation methods by integrating gene expression programming. This approach allows parameters to be automatically selected from a database and optimally combined to form decision rules, allowing both the structure and parameters of the decision rules to evolve. The generated decision rules support capacity expansion activities by clearly providing guidance to adjust the expansion level and timing according to the changing environment. The proposed approach was applied to a waste-to-energy system, to flexibly expand capacity under uncertainty. The empirical results demonstrate that the decision rules generated by our proposed approach improved system performance in terms of expected net present value, relative to decision rules generated by a method based on differential evolution algorithm. A sensitivity analysis was also conducted to investigate the effectiveness of the proposed approach under changes to the major assumptions, and results indicated that the generated decision rule can guide capacity expansion under different situations.

中文翻译：

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通过基因表达编程生成灵活扩容问题的决策规则

摘要 本文提出了一种生成决策规则的新方法，以在容量扩展中发挥灵活性。所提出的方法通过集成基因表达编程不同于其他决策规则生成方法。这种方法允许从数据库中自动选择参数并优化组合以形成决策规则，从而允许决策规则的结构和参数发展。生成的决策规则通过明确提供指导以根据不断变化的环境调整扩展级别和时间来支持容量扩展活动。所提出的方法被应用于垃圾发电系统，以在不确定的情况下灵活地扩展容量。实证结果表明，相对于基于差分进化算法的方法生成的决策规则，我们提出的方法生成的决策规则在预期净现值方面提高了系统性能。还进行了敏感性分析以研究在主要假设发生变化的情况下所提出方法的有效性，结果表明生成的决策规则可以指导不同情况下的容量扩展。