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A Modeling-Optimization-Validation Framework for Digital Transformation Decisions in Socio-Technical Systems:
Lugang Yu1, Dezhi Li2, Jinbo Song3
1China Academy of Engineering Physics, Science and Technology Information Center, Mianyang, China.
None:
Managers of socio-technical systems (STSs) lack quantitative tools to optimize digital technology (DT) selection. This study bridges this gap by developing a unified modeling-optimization-validation decision-making framework, positioned as an upstream, preplanning tool complementary to operational digital twins. First, a time-varying network model is constructed with explicit mathematical definitions for task-information dynamics. Second, DT selection is formulated as a multiple-choice knapsack problem (MCKP) and solved using a genetic algorithm (GA) with formally defined fitness functions and constraints. Third, numerical simulations validate the approach. A case study of a wastewater treatment project demonstrates that the optimized DT portfolio yields an average efficiency improvement of 27.9% (calculated based on node-level productive and cost efficiency). This framework provides a transferable, quantitative basis for designing digital transformation roadmaps, offering a lower-effort alternative for initial investment prioritization compared to high-frequency operational calibration in traditional digital twins.
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