Project selection and scheduling with multiplicative enhancement effects and delay risk: An application in intelligent manufacturing technologies
针对智能制造中技术选择与排序问题，建立考虑乘性增强效应和不确定延迟的随机混合整数规划模型，集成CVaR风险度量，并用分支定界法求解，案例表明考虑乘性效应可避免短视偏差。
In the realm of intelligent manufacturing, driven by the push towards smart factories and Industry 4.0, optimizing technology selection and sequencing is paramount for intelligent transformation. This complex decision-making process resembles a novel project selection and scheduling problem, characterized by multiplicative enhancement effects (MEEs) where the collective benefits of multiple projects can exceed their individual contributions. Additionally, the uncertain duration of projects further adds complexity. Motivated by these practical challenges, a stochastic mixed-integer programming model that incorporates MEEs and uncertain delays is established. A conditional value-at-risk (CVaR)-based risk measure is integrated to control delay risk. The solution approach leverages an efficient branch and bound (B&B)-based approach with efficient cutting strategies, integrating a sample average approximation (SAA) framework and a backward labeling algorithm to handle stochastic elements. A real-world technology implementation case study highlights the advantages of considering MEEs, uncovers myopic biases in technology selection, and identifies implementation patterns centred around core Industry 4.0 technologies. This research enhances our understanding of intelligent manufacturing decision-making, offering valuable insights and practical implications for technology-driven transformations.