Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic algorithm is proposed based on the elitist archive and the K-means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants' computational requirements in a distributed environment.
This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0-1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.
Material handling has become one of the major challenges in modern production management. Consequently, this paper intends to investigate the part delivery of mixedmodel assembly lines with decentralized supermarkets and tow trains. Besides, uncertain exception disturbances, including tow train failures and adjustments of the production sequence, are also considered. To solve this problem, a heuristic-based dynamic delivery strategy is proposed, which dynamically schedules the route, departure time, quantities and types of loaded parts for each tour. To evaluate the performance of this strategy, it is used to solve an instance in comparison with the periodic delivery strategy, experimental results are reported and their performances are compared under different metrics. Moreover, a multi-scenario analysis is employed to determinate the long-term decisions, including the number of tow trains and the route layout. Finally, the critical storage is suggested to be set for each station to avoid part starvation resulting from disturbances, and its effect on the delivery performance is investigated. Int. J. Model. Simul. Sci. Comput. Downloaded from www.worldscientific.com by MONASH UNIVERSITY on 10/09/15. For personal use only. 1550038-4 Int. J. Model. Simul. Sci. Comput. Downloaded from www.worldscientific.com by MONASH UNIVERSITY on 10/09/15. For personal use only.
This paper introduces a CPS application for intelligent aeroplane assembly. At first, the CPS structure is presented, which acquires the characteristics of general CPS and enables "simulation-based planning and control" to achieve high level intelligent assembly. Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling, respectively. The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation, and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration, it can also directly link to data, and the error is smaller. This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control. A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness. At the same time, an outer collaborative trajectory planning is also designed. All the experiment results show the feasibility of above proposed methods.
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