In this paper, we give two new optimal PARALLEL ALGORITHMS for matrix multiplication which are designed to run on a Fibonacci hypercube structure. At first, we present a broadcast algorithm on Fibonacci hypercube with O (n) time complexity. We use this broadcast algorithm and ALGORITHMS presented by Gunnels, Lin, Morrow and Geijn on a mesh structure, in order to present two optimal PARALLEL matrix multiplication ALGORITHMS on a Fibonacci hypercube structure. We show these ALGORITHMS are cost-optimal. The performance of the algorithm has been tested on a simulative PARALLEL system.

Two new and efficient ALGORITHMS for evaluating the terminal reliability of PARALLEL computer interconnection networks have been proposed. Both the ALGORITHMS are based on multiple decomposition approach. The former is meant for reliability evaluation of multi computers while the latter is meant for multi stage interconnection networks. Using the first algorithm the reliability of some important multi computer networks based on cubic architecture have been evaluated. The second algorithm is a modified version of the first one, which takes into account the topological advantages of multi stage interconnection networks during the decomposition process. The terminal reliability of some important fault-tolerant multi-stage interconnection networks has been evaluated using the second algorithm for the purpose of comparison. The complexities of the proposed ALGORITHMS are found to be highly polynomial in nature. The proposed method of multiple decomposition is compared with some similar decomposition methods. The simulated results confirm that the proposed decomposition method is much better than its counter parts. The generality of the proposed method is assured by applying the same to evaluate the terminal reliability of Alternating group graph which is also an important interconnection network apart from cube like interconnection networks.

Proper arrangement of facility layout is a key issue in management that influences efficiency and the profitability of the manufacturing systems. PARALLEL Row Ordering Problem (PROP) is a special case of facility layout problem and consists of looking for the best location of n facilities while similar facilities (facilities which has some characteristics in common) should be arranged in a row and dissimilar facilities should be arranged in a PARALLEL row. As PROP is a new introduced NP-hard problem, only a mixed integer programming model is developed to formulate this problem. So to solve large scale instances of this problem, heuristic and meta-heuristic ALGORITHMS can be useful. In this paper, two strategies based on genetic algorithm (GA) and a novel population based simulated annealing algorithm (PSA) to solve medium and large instances of PROP are proposed. Also several test problems of PROP in two groups with different sizes that have been extracted from the literature are solved to evaluate the proposed ALGORITHMS in terms of objective function value and computational time. According to the results, in the first group of instances, both ALGORITHMS almost have equal performances, and in the second group PSA shows better performance by increasing the size of test problems.

This paper tackles the problem of batching scheduling on a multi-operational PARALLEL machine with precedence constraint. Based on the literature, batching accelerates the production process and decreases the transportation costs. Therefore, it is mentioned as a very important subject in scheduling. On the other hand, most of researches consider the processing time as a predefined and constant parameter that is not reasonable in real industries.In this paper a mathematical model is presented in order to minimize the makespan in which the processing times of jobs are considered as a function of their batch size. The proposed model is known to be NP-hard, therefore two Mehta-heuristic ALGORITHMS including genetic algorithm (GA) and simulated annealing (SA) are offered so as to find near optimal solutions in reasonable run time. In order to check the verification of proposed model, the problem is solved optimally for small scales and the results are compared with the solutions of GA and SA. Computational study demonstrates that the proposed methods can found the solutions with very low gap in a suitable run time.

The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).

This paper considers the problem of scheduling N jobs on M unrelated PARALLEL machines with sequence-dependent setup times. To better comply with industrial situations, jobs have varying due dates and ready times and there are some precedence relations between them. Furthermore sequence-dependent setup times and anticipatory setups are included in the proposed model. Our objective is to determine a schedule that minimizes makespan and number of tardy jobs. The problem is NP-hard, so for obtaining an optimal solution, in reasonable computational time, we propose two multi objective genetic ALGORITHMS (MOGA). To evaluate the proposed ALGORITHMS, random test problems are produced in medium and large sizes with tight due dates. After setting the parameters, the performances of these ALGORITHMS are evaluated using the concept of data envelopment analysis (DEA), distance method, and a number of non-dominated solutions.

Simulation of the four degree of freedom PARALLEL robot (Quattrotaar) is subjective of this paper. The mathematical model of the PARALLEL robot is obtained too. The workspace is optimized for Non-singular kinematic type-2. Artificial Bees Colony algorithm and Particle Swarm Optimization algorithm as overall exploring ALGORITHMS are implemented and the results are compared to each other. In spite of any intrinsic complexity of the optimization problems, the result shows the capability of both methods for this robot parameters design. Comparison of the results indicates the Particle Swarm Optimization algorithm runs faster than Artificial Bees Colony algorithm. The exploring volume consists of a plan with 500 mm x 500 mm dimension which move in a vertical direction from 500 mm to 1000 mm. One of the important hints of the paper is a 90-degree rotation of end effector around vertical axis Z. This rotation is caused more flexibility and dexterity for the robot. A 3-D model of Quattrotaar PARALLEL robot is created by Computer Aided Design software and finally Quattrotaar is fabricated in Human and Robot Interaction Laboratory (Taarlab).