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The article briefly discusses research papers that are contained within the section, titled "Algorithm Design and Scheduling Techniques (Realistic Platform Models) for Heterogeneous Clusters," of this issue. The purpose of these papers is to explore the impact of platform heterogeneity on the design and evaluation of new algorithms, such as, distributed algorithms and scheduling algorithms. One paper by Ligang He and his colleagues discusses two new workload allocation strategies for large-scale heterogeneous platforms. The paper "On the Distribution of Sequential Jobs in Random Brokering for Heterogeneous Computational Grids," by Vandy Berten and colleagues, focuses on resource brokering for scheduling sequential jobs onto a grid platform. In their article, "Multiple Job Scheduling in a Connection-Limited Data Parallel System," authors Alessandro Amoroso and Keith Marzullo describe a new method for scheduling jobs in a distributed system.

This paper addresses sustainable workforce scheduling and routing problem (WSRP), in which technicians with different levels of competence are paired into teams to serve customers in different geographic regions-districts within predetermined time windows. We formulate the WSRP as a mixed integer linear programming formulation that covers the decisions such as scheduling, routing, state of charge, and visiting of the charging stations. Furthermore, we developed different objective functions minimizing not only total travelled distance, but also the number of electrical vehicles employed. The performance of the proposed heuristic is tested by using real-world data. In addition, the developed variable neighbourhood search algorithm is compared with the exact solution method and the computational results indicates that the proposed heuristic can yield high quality of solutions. [ABSTRACT FROM AUTHOR]

Semi-supervised data classification is one of significant field of study in machine learning and data mining since it deals with datasets which consists both a few labeled and many unlabeled data. The researchers have interest in this field because in real life most of the datasets have this feature. In this paper we suggest a collective method for solving semi-supervised data classification problems. Examples in R1 presented and solved to gain a clear understanding. For comparison between state of art methods, well-known machine learning tool WEKA is used. Experiments are made on real-world datasets provided in UCI dataset repository. Results are shown in tables in terms of testing accuracies by use of ten fold cross validation.