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The purpose of this paper is to achieve intelligent superstore site selection. Yonghui Superstores partnered with Cardinal Operations to incorporate a tremendous amount of site-related information (e.g. points of interest, population density and features, distribution of competitors, transportation, commercial ecosystem, existing own-store network) into its store site optimization.

This paper showcases the integration of regression, optimization and machine learning approaches in site selection, which has proven practical and effective.

The result was the development of the “Yonghui Intelligent Site Selection System” that includes three modules: business district scoring, intelligent site engine and precision sales forecasting. The application of this system helps to significantly reduce the labor force required to visit and investigate all potential sites, circumvent the pitfalls associated with possibly biased experience or intuition-based decision making and achieve the same population coverage as competitors while needing only half the number of stores as its competitors.

To our knowledge, this project is among the first to integrate regression, optimization and machine learning approaches in site selection. There is innovation in optimization techniques.

To meet the rapidly increasing demand for medical treatment during the outbreak of COVID-19, Huoshengshan and Leishenshan Hospital are rapidly built (9–12 days) in Wuhan. These two urgent emergency projects are unprecedented. In general, substantial literature suggests that the possibility of shortening a schedule by more than a quarter of its original duration is implausible. By contrast, the two projects had successfully compressed the schedules from months and years to about ten days. This study aims to investigate how this was done and provide references for future projects.

The study uses qualitative case study techniques to analyze the project practices in two urgent emergency projects. Data were gathered through semi-structured interviews and archival research. During interviews, interviewees were asked to describe the project practices adopted to overcome the challenges and freely share their experiences and knowledge.

The results illustrate that a high degree of schedule compression is achievable through tactful crashing, substitution and overlapping applications. The successful practices heavily rely on the high capacity of participants and necessary organization, management and technology innovations, such as three-level matrix organizational structure, reverse design method, site partition, mock-up room first strategies and prefabricated construction technology. For instance, the reverse design method is one of the most significant innovations to project simplification and accelerate and worthy of promotion for future emergency projects.

The empirical findings are significant as they evoke new thinking and direction for addressing the main challenges of sharp schedule compression and provide valuable references for future emergency projects, including selecting high-capacity contractors and replacing the conventional design methods with reverse design.

Substantial studies indicate that the maximum degree of schedule compression is highly unlikely to exceed 25%, but this study suggests that sharp compression is possible. Although with flaws in its beauty (i.e. compressing schedule at the expense of construction cost and quality), it is also a breakthrough. It provides the building block for future research in this fertile and unexplored area.