A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

Abstract. A mathematical model and heuristic method for solving multi-depot and multi-product vehicle routing problem (MD-MPVRP) with heterogeneous vehicles have been proposed in this article. Customers can order eclectic products and depots are supposed to deliver customers' orders before the lead time, using vehicles with diverse capacities, costs and velocities. Hence, mathematical model of multi-depot vehicle routing problem has been developed to mirror these conditions. This model is aimed at minimizing the serving distances which culminates in a reduction in prices and also serving time. As the problem is so complex and also solving would be too time-taking, a heuristic method has been offered. The heuristic method, at first, generates an initial solution through a three-step procedure which encompasses grouping, routing and vehicle selection, scheduling and packaging. Then it improves the solution by means of simulated annealing. We have considered the efficiency of offered algorithm by comparing its solutions with the optimum solutions and also during a case study. [V. Mahdavi Asl, S.A. Sadeghi, MR. Ostadali Makhmalbaf. A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

Deep learning for estimating energy savings of early-stage facade design decisions

The selection of high-performance building facade systems is essential to promote building energy efficiency. However, this selection is highly dependent on early-stage design decisions, which are extremely challenging considering numerous design parameters with early-stage uncertainties. This paper aims to evaluate the applicability of deep learning networks in estimating the energy savings of different facade alternatives in the early-stage design of buildings. The energy performance of two competing façade systems (i.e., Ultra-High-Performance Fiber-Reinforced-Concrete and conventional panels) was estimated for different scenarios through building energy simulations using EnergyPlus™. Three deep learning networks were trained using the collected data from the simulation of fourteen buildings in fourteen different locations to estimate the heating, cooling, and total site energy savings. The accuracy of trained deep networks was compared with the accuracy of three common data-driven prediction models including, Gradient Boosting Machines, Random Forest, and Generalized Linear Regression. The results showed that the deep learning network trained to predict building total site energy savings had the highest accuracy among other models with a mean absolute error of 1.59 and a root mean square error of 3.48, followed by Gradient Boosting Machines, Random Forest, and last Generalized Linear Regression. Similarly, deep networks trained to predict building cooling and heating energy savings had the lowest mean average error of 0.20 and 1.17, respectively, compared to other predictive models. It is expected the decision support system developed based on this methodology helps architects and designers to quantify the energy savings of different facade systems in early stages of design decisions

Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources

Automated Generation of Parametric BIMs based on Hybrid Video and Laser Scanning Data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing