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There are a large number of historical buildings in the world, but many of them are seriously damaged. A fundamental task to revitalize them is to record their details and redraw them, but it is a highly inefficient task. This paper aimed to create three-dimensional (3D) models automatically for those damaged historical buildings while respecting the 1964 Venice Charter, the ICOMOS recommendations, and local ordinances. After reviewing the advantages and disadvantages of various measurement tools, we implemented a machine learning algorithm, the residual 3D convolutional neural network (CNN) method, to combine heterogeneous data from multiple sources including a 3D laser scanner, an unmanned aerial vehicle, a total station, and a camera, in a historical building information model (HBIM) to increase its accuracy. Those data, preprocessed by four steps including data registration, reducing noises, filtering data, and data integration, were trained and fused in a 3D CNN model, which consisted of one input layer, five 3D convolutional and pooling layers, and two connected layers. A Shanghai historical building conservation project was taken as a pilot case to verify its productivity and accuracy in developing the HBIM, on which the architectural features were thoroughly recorded, and the damaged details were recovered. [ABSTRACT FROM AUTHOR]

After reviewing the problems of supply disruption caused by worldwide public health events, a two-echelon dual-marketing-channel supply model is presented in this paper, whose participants are one pharmaceutical company with disruption risks, one pharmaceutical company without disruption risks (NDPC), and one pharmaceutical retailer (PR). Their marketing consists of online and offline marketing channels. The PR signs an option contract with the NDPC to secure an adaptive and continuous pharmaceutical supply. The model is divided into three sub-models: single coalition structure, double coalition structure, and multi-party coalition structure, whose coalitional strategies and profits are investigated respectively. Their optimal decisions under disruption risks also are compared, and their pharmaceutical quality and process resilience are discussed. The merit of the model is to combine option contracts, government double subsidies, and business interruption insurance in the supply chain. [ABSTRACT FROM AUTHOR]

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

The time, quality, and cost are three important but contradictive objectives in a building construction project. It is a tough challenge for project managers to optimize them since they are different parameters. This paper presents a time-cost-quality optimization model that enables managers to optimize multiobjectives. The model is from the project breakdown structure method where task resources in a construction project are divided into a series of activities and further into construction labors, materials, equipment, and administration. The resources utilized in a construction activity would eventually determine its construction time, cost, and quality, and a complex time-cost-quality trade-off model is finally generated based on correlations between construction activities. A genetic algorithm tool is applied in the model to solve the comprehensive nonlinear time-cost-quality problems. Building of a three-storey house is an example to illustrate the implementation of the model, demonstrate its advantages in optimizing trade-off of construction time, cost, and quality, and help make a winning decision in construction practices. The computational time-cost-quality curves in visual graphics from the case study prove traditional cost-time assumptions reasonable and also prove this time-cost-quality trade-off model sophisticated.