mahdi mokhtarzadeh

Proper transportation and distribution of commodities plays a pivotal role in the expenditures of supply chains. In this paper, a clustered vehicle routing problem with pick-up and delivery is studied. A fleet of distinct vehicles is concurrently responsible for distribution of medicines and collection of their wastes. Collected wastes should be sent to a waste center. To solve the problem, a bi-objective mathematical model is presented. Fairness of travelled distances among drivers and transportation expenses are two objective functions considered in the model. Since the proposed problem is NP-hard, a three-step hybrid approach is developed to solve the problem. First, K-medoids clustering algorithm allocates customers to subsets based on their coordinates. Second, a mathematical model is used for routing vehicles within each cluster. Third, NSGA-II is used to produce final result using the outcome of step 2. Extensive numerical results indicate the superiority of the proposed approach against the NSGA-II.

Nowadays, produced wastes in urban areas are growing exponentially all over the world. On the other hand, the environment and natural resources are on the way to destruction. One way to deal with increasing waste generation and protecting the environment is proper management of municipal solid wastes. One aspect of municipal solid waste management is locating the various facilities and the routing between them. In this study, a new mathematical model is developed for location-routing problem in MSWM system. Considering the integrity of MSWM facilities is the strength of this study. The proposed model meets two objectives including minimization of system costs and environmental impacts. In this model, the location of waste collection centers and reverse logistics centers are determined. In order to improve the efficiency and practicality of the proposed model, a solution method based on the NSGA-II is proposed. Also, a new method based on best worst approach developed to parameter tuning of NSGA-II. As a result, it observed that the total costs of the system increases exponentially as a result of increase in the volume of waste in sources. Numeral experiments indicate the efficiency of proposed algorithm in achieving approximate optimum solution in an acceptable time.

High accuracy and huge density of 3D points cloud acquired by airborne Lidar makes them as a good and suitable tool in order to analyze of terrain surface. In this procedure, points cloud clustering is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper a novel method is proposed for supervised classification of LiDAR points cloud based on contextual analysis on LiDAR points. The proposed method consists of three main steps. In the first step, a set of contextual features are produced for each points in LiDAR data. In second step, optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted to a simple k-means clustering on the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluation of the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients was 93.15% and 0.89 respectively.

Importance of location reference information in all matters civil and research uncovered. Among the methods for the production of spatial data، photogrammetry method by quickly،economically and، above all، the need to direct the human factors in place، the place is unique. The photogrammetric method، based on observation platforms، as the primary means of providing input data and the operating ring key role in the success and achievements in other areas of play. Today، technological progress leads to high-quality digital camera that promises to provide a location for the camera photogrammetric method with accuracy، speed and efficiency are high. According to the key role that digital aerial cameras in modern photogrammetry، ensure the accuracy of the camera as a prerequisite for ensuring that spatial data will be obtained. In other words، any technical weaknesses in the use of aerial digital cameras lead to inaccurate and unreliable location information that may be the wrong decision by the beneficiaries it is. Due to high costs and administrative difficulties in calibration laboratories، as one of the solutions to the equations self-calibration useful in this context is known. For this purpose، the term optimal Fourier equation of the proposed genetic algorithm based on simulated data were compared with previous models. Based on the results، the model is able to multiple aberrations with minimal dependence on the model. Precision modeling of multiple aberrations in digital camera images simulated Ultra Cam compared to other modeling parameters added to improve the accuracy of approximately 30%، with minimal dependence on the show.