Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36663220201101Performance analysis of multi computer system consisting of active parallel homogeneous clients12411305610.22121/aotp.2020.239383.1032ENIbrahimYusufDepartment of Mathematical Sciences, Bayero University, Kano, Nigeria0000-0002-4849-0163AbdullahiSanusiSchool of Continuing Education, Bayero University, Kano, NigeriaAbdulkareem LadoIsmailDepartment of Mathematics College of Arts and Science, Kano, NigeriaMuhammad SalihuIsaDepartment of Mathematics, Yusuf Maitama Sule University, Kano, Nigeria0000-0001-5993-3823SuleimanKDepartment of Mathematics, Yusuf Maitama Sule University, Kano, NigeriaShehuBalaDepartment of Mathematical Sciences, Bayero University, Kano, NigeriaU.AAliDepartment of Mathematics, Federal University, Dutse, NigeriaJournal Article20200713Reliability is among the performance factors applied to multi computer systems consisting of active parallel hosts (clients) and a central server. For reliability evaluation and system performance, this study analyzed a multi computer system consisting of a three hosts (clients) connected to a central server. The system is configured as series-parallel system consisting of two subsystems A and B. Subsystem A consist of three clients working in parallel with each other while subsystem B consist of a central server. Both clients and server failure and repair time are to be exponentially distributed The system is analyzed using first order differential difference equations to derive the expressions for availability, mean time to failure, probability of busy period of repairman due partial or complete failure. The results are presented in tables and graphs. Reliability characteristics such as availability, MTTF, profit function as well as sensitivity analysis have been discussed. Some particular cases have also been derived and examined to see the practical effect of the model. The computed results are demonstrated by tables and graphs.Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36663220201101Optimal redundancy allocation for the problem with chance constraints in fuzzy and intuitionistic fuzzy environments using soft computing technique254711883010.22121/aotp.2020.250508.1042ENNabaranjanBhattacharyeeDepartment of Mathematics, Sidho-Kanho-Birsha University, Purulia0000-0001-9453-6698RajeshParamanikDepartment of Mathematics, Sidho-Kanho-Birsha University, PuruliaSanat KumarMahatoDepartment of Mathematics, Sidho-Kanho-Birsha University, Purulia0000-0001-5455-5173Journal Article20200928In some reliability optimization problem the constraints relations have probabilistic nature. These constraints are called the chance constraints and are difficult to handle up to some extent. The aim of this paper is to solve the reliability-redundancy allocation problem involving chance constraints in precise and imprecise environments. The component reliabilities of the system are imprecise numbers and further the constraints are stochastic type i.e., chance constraints. The genetic algorithm incorporated with stochastic simulation approach is implemented to optimize the system reliability. We introduced the fuzzy and intuitionistic fuzzy numbers to consider the impreciseness. In particular, component reliabilities are assumed to be triangular fuzzy numbers and triangular intuitionistic fuzzy numbers in two different environments. The simulation technique known as Monte Carlo Simulation is used to find the deterministic constraints from the stochastic ones. To transform the constrained optimization problem into unconstrained one we make use of the effective Big-M penalty approach. The problems are coded with real coded genetic algorithm. We have taken up some numerical examples to show the performance of the proposed method and the sensitivities of the GA parameters are also presented graphically.Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36663220201101Decision making under intuitionistic fuzzy metric distances496411883110.22121/aotp.2020.250749.1043ENKousikBhattacharyaDepartment of Mathematics, Midnapore College ( Autonomous), 721101, West Bengal, IndiaSujit KumarDeDepartment of Mathematics, Midnapore College ( Autonomous), 721101, West Bengal, IndiaJournal Article20200930This article deals with qualitative difference between two intuitionistic fuzzy sets with the help of standard pseudo metric and metric spaces. Some definitions over metric spaces, pseudo metric spaces, intuitionistic fuzzy sets, indeterminacy and the formula of measuring metrices have been incorporated. Numerical illustrations, graphical illustrations, area of applications and ranking for decision making are discussed to show the novelty of this article. Finally, conclusions and scope of future works are mentioned.Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36663220201101On the shortest path calculation time in the large-scale dynamic post-disaster environment658011656010.22121/aotp.2020.243077.1038ENSeyed MahdiShavaraniDepartment of Industrial Engineering, Eastern Mediterranean University, Famagusta, TurkeyBelaVizvariDepartment of Industrial Engineering, Eastern Mediterranean University, Famagusta, TurkeyJournal Article20200809There are many metropolitan cities where serious disasters are expected. The disaster, especially if it is an earthquake, damages the roads structure. Thus, the shortest path between two points can be changed. Emergency vehicles, including ambulance, fire-engine, police car, and technical aid, must get the temporary shortest path in real time to work effectively. The shortest path algorithm available in MATLAB, Dijkstra, is tested on two metropolitan cities of San Francisco and Tehran. The road systems of both cities are represented by high number of nodes and connecting arcs. The conclusion is that the algorithm is suitable for finding shortest path for emergency vehicles. However, it is too slow for being a subroutine of a solver for vehicle routing problem.Firouzabad Institute of Higher EducationAnnals of Optimization Theory and Practice2588-36663220201206Automatic breast thermography images classification based on deep neural networks717912006710.22121/aotp.2020.251387.1046ENAzzaMahmoudDepartment of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt.Journal Article20201005Breast thermography is a screening tool which is capable of detecting cancer at an early stage. The main objective of this work is using the full power of deep neural network (DNN) and exploring its ability to learn the discriminative features of input data. The transfer learning and data augmentation are performed to solve the problem of lack of labled data. To improve the accuracy, the support vector machine (SVM) classifier will hybrid with the convolutional neural network (CNN) instead of using the deep model as end-to-end. The performance is verified by the k-fold cross-validation. The proposed techniques are trained and evaluated on DMR-IR dataset to classify the thermographic images to normal and abnormal groups. The proposed technique of employing AlexNet hybrid with SVM achieves the best performance, producing 92.55% accuracy, 95.56% sensitivity, 89.80% precision, 92.63% F1 score.