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Cloud computing has provided new dimensions and opportunities to telecom providers to enhance their ability in creating new revenue streams, reduce operational costs and increase customer satisfaction. In recent years, the progression of evolving cloud computing systems is huge. Conversely, it also encountered a lot of new challenges to the institutions utilizing cloud systems. It is critical to evaluate the change in organizational risk profile before deciding the level of cloud adoption. The conventional risk assessment framework of telecom providers may be unsuitable for cloud computing owing to the difficulties in the configuration. This paper aims to propose a system to quantify the influences of cloud adoption in telecom risk profile. This study also provides a comparison of overall risk profile change between clouds enabled infrastructure, traditional on-premises information technology solutions, and a combination of both in the telecom sector. The result encourages the selective adoption of the hybrid cloud model which demonstrated a relatively low organizational risk score. This work contributes to cloud computing business research by providing a holistic risk comparison model which reflects the distinctiveness of telecom service provider's assets and capabilities. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered. [ABSTRACT FROM AUTHOR]

This paper considers the sequencing of jobs that arrive in a flow shop in different combinations over time. Artificial neural network (ANN) uses its acquired sequencing knowledge in making the future sequencing decisions. The paper focuses on scheduling for a flow shop with ‘m’ machines and ‘n’ jobs. The authors have used the heuristics proposed by Campbell et al.(1970, A heuristic algorithm for n-jobs m-machines sequencing problem) to find a sequence and makespan (MS). Then a pair wise interchange of jobs is made to find the optimal MS and total flow time (TFT). The obtained sequence is used for giving training to the neural network and a matrix called neural network master matrix (NNMM) is constructed, which is the basic knowledge of the neurons obtained after training. From the matrix, interpretations are made to determine the optimum sequence for the jobs that arrive in the future over a period of time. The results obtained by the ANN are compared with a constructive heuristics and an improvement heuristics. The results show that the quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics. It is found that the system’s efficiency (i.e., obtaining the optimal MS and TFT) increases with increasing numbers of training exemplars. [ABSTRACT FROM AUTHOR]