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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.
Liu et al. (Tue,) studied this question.