Purpose: Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this neglect has resulted in delays and cost overruns. Careful planning that considers all of the factors affecting construction logistics can ensure project success. The purpose of this paper is to examine the potential for using genetic algorithms (GAs) to derive logistics plans for materials production, supply and consumption. Design/methodology/approach: The proposed GA model is based on the logistics of precast components from the supplier’s production yard, to the intermediate warehouse and then to the construction site. Using an activity-based costing (ABC) approach, the model not only considers the project schedule, but also takes into account the production and delivery schedule and storage of materials. Findings: The results show that GAs are suitable for solving time-cost trade-off problems. The optimization process helps to identify the activity start time during construction and the delivery frequency that will result in the minimal cost. What-if scenarios can be introduced to examine the effects of changes in construction logistics on project outcomes. Originality/value: This paper presents a method for using GAs and an ABC approach to support construction logistics planning decisions. It will help construction planners and materials suppliers to establish material consumption and delivery schedules, rather than relying on subjective judgment.

The building envelope plays a significant role in the energy performance of buildings and windows are a key element in transmitting heating and cooling between the indoor and outdoor environment, and hence an adequate window system is one of the most important retrofit strategies of existing buildings for energy conservation. Therefore, this study presents a method with a theoretical case study to examine the improvement of energy efficiency in a typical high-rise residential building through window retrofitting. A building energy design model in Designbuilder along with a building information modeling (BIM) model in Revit are developed, with 20 common potential glazing alternatives being analyzed to predict the potential energy savings in the same case building with identical orientation located in a variety of climate zones in China. Based on different parameters and considerations, the results demonstrated that the currently relatively expensive low-e window glazing has the best energy performance in all climate zones, but is sufficiently close to conventionally glazed windows in its energy efficiency to discourage its adoption at present, and that, instead, a single dark conventional glazed window is preferred in a hot summer/warm winter climate, double dark traditional glazing in a hot summer/cold winter climate, and a double clear conventional window in a cold climate. Based on the simulated results, an indicative suggestion was provided to select an adequate window system for residential building retrofitting in the studied climates or similar climatic regions.