The green vehicle routing problem (GVRP) is a variant of the vehicle routing problem (VRP), which increasingly attracts many researchers in recent years due to the growing global environmental issues. As the transportation of the products grows, the number of vehicles in fleets and the pollutants caused by these vehicles also grow, which in turn negatively affects human health. In this paper, a biobjective GVRP was studied. The two objectives are minimizing the total distance and minimizing the total fuel consumption of all vehicle routes. As a solution method, an adaptive large neighborhood search was hybridized with two new local search heuristics. The proposed method was applied to two well‐known benchmark problem sets for VRPs and new approximate Pareto fronts were obtained for these benchmark sets.
Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content, specifying relations among them, and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed‐forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition, the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state‐of‐the‐art algorithms have been made from the viewpoint of a typical classification problem.
ÖzBu çalışmada, Çok Bölmeli Araç Rotalama Problemi (ÇB-ARP) ele alınmıştır. Günlük hayatta marketler, firmalar ve kurumlar bazı ürünleri müşterilerine teslim ederken ya da belirli noktalardan toplarken, bu ürünleri araç içinde farklı bölmelere koymaları gerekmektedir. Bazı ürünlerin oda sıcaklığında, bazılarının soğuk olarak taşınması gerekmektedir. Bazı atıkların, kimyasal ürünlerin ya da yakıtların diğer ürünlerle karıştırılmadan taşınması gerekmektedir. Bu yüzden dağıtım ya da toplama yapan araç filosundaki her bir aracın birden fazla bölmeye sahip olması ve dağıtılan ya da toplanan ürünlerin ilgili bölmelerde taşınması gerekmektedir. Bu makalede çalışılan ÇB-ARP, bir, iki ve üç bölmeli araç senaryoları dahilinde ayrı ayrı ele alınmıştır. Çözüm yöntemi olarak melez bir Genetik Algoritma (GA) kullanılmış ve bu algoritma Araç Rotalama Problemi (ARP) literatüründe sıklıkla kullanılan bir problem örnek seti üzerinde uygulanmıştır. Sonuç olarak bu çalışmadaki ÇB-ARP modeli için yeni referans sonuçları üretilmiş ve sonuçlar yorumlanmıştır.
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