In this paper we develop a new hybrid metaheuristic algorithm based on the SCATTER SEARCH approach to solve the well-known resource-constrained project scheduling problem. This algorithm combines two solutions from SCATTER SEARCH to build a set of precedence feasible activity lists and select some of them as children for the new population. We use the idea presented in the forward/backward improvement technique to define two types of schedule, direct and reverse, and the members of the sequential populations change alternately between these two types of schedule. Extensive computational tests were performed on standard benchmark datasets and the results are compared with the best available results. Comparative computational tests indicate that our procedure is a very effective metaheuristic algorithm.

In recent decades, high profits of extended warranty have led third-party firms considering it as a lucrative after-sales service. However, customers’ division in terms of risk aversion and effect of offering extended warranty on manufacturers’ basic warranty should be investigated through adjusting such services. Risk-averse customers welcome extended warranty, while the customers without taking on risk may remain at the level of basic warranty. In this paper, a multi-objective integer nonlinear programming model is presented for integrating the supply chain of after-sales services. In the suggested model, firstly the strategies used by the manufacturers in the basic warranty period and the third party’ s policy during the extended warranty period, including the development of a new imperfect maintenance approach, are regulated. The effects of these strategies on the desirability of customers with different levels of risk-taking are then analyzed. In order to optimize the model, the SCATTER SEARCH based approach was introduced for extracting set of non-dominated solutions. The results indicated that increasing level of customers’ risk-taking convinces manufacturers to diminish the basic warranty period and the third party can apply less costly preventive maintenance.

Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyberattack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of SCATTER SEARCHing Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94. 54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary algorithm (data mining and decision detection such as C4. 5).