Recent decades have seen increasing utilization of optimization packages, based on Operations Research and Mathematical Programming techniques, for effective management of the provision of goods and services in distribution systems. Large numbers of real-world applications, both in North America and Europe, have widely shown that the use of computerized procedures for distribution process planning produces substantial savings (generally from 5% to 20%) in global transportation costs. It is easy to see that the impact of these savings on the global economic system is significant. The transportation process involves all stages of production and distribution systems and represents a relevant component (generally from 10% to 20%) of the final cost of goods. The green vehicle routing problem (GVRP) is an emerging research field that attracts many researchers. This survey paper aims to classify and review the literature on GVRPs from various perspectives. This paper covers publications between 2006 and 2019 including 309 papers. To this end, a systematic literature review has been implemented in order to respond to corresponding questions related to this area and proposed an extensive structure compromising various aspects including variants of GVRPs, objective functions, uncertainty, and solutions approach to analyze GVRPs studies in different perspectives. Some new research areas have been drawn based on problem classification, uncertainties, solution methodologies, and finally, the objective function approaches for future research directions and the results of this study show that researches on GVRPs are relatively fresh and there is still a room for large improvements in several areas.

Cancer diagnosis is one of the most studied problems in the medical domain. Several researchers have focused in order to improve performance and achieve to obtain satisfactory results. Breast cancer is one of cancer killer in the world. The diagnosis of this cancer is a big problem in cancer diagnosis researches. In artificial intelligent, machine learning is a discipline which allows to the machine to evolve through a process. Machine learning is widely used in bio informatics and particularly in breast cancer diagnosis. One of the most popular methods is K-nearest neighbors (K-NN) which is a supervised learning method. Using the K-NN in medical diagnosis is very interesting. The quality of the results depends largely on the distance and the value of the parameter "k" which represent the number of the nearest neighbors. In this paper, we study and evaluate the performance of different distances that can be used in the K-NN algorithm. Also, we analyze this distance by using different values of the parameter "k" and by using several rules of classification (the rule used to decide how to classify a sample). Our work will be performed on the WBCD database (Wisconsin Breast Cancer Database) obtained by the university of Wisconsin Hospital.

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The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation and edge detection. Results obtained confirm the validity of the approach.