In this paper, a heuristic algorithm is proposed to solve the single-model stochastic assembly line balancing Type II problem. For a given number of workstations and a pre-specified assembly line reliability, which is the probability of the workload not exceeding the cycle time for the whole assembly line, the proposed algorithm tries to obtain a solution with the smallest cycle time. In the first stage, the tasks are assigned to workstations from the forward and backward directions alternatively. In the second stage, the workload is smoothed by swapping tasks among workstations. At last, the upper bound of the cycle time obtained in the second stage is reduced step by step until the smallest cycle time satisfies the pre-specified assembly line reliability. The performance of the proposed algorithm is compared with a modified version of Moodie and Young's algorithm by applying them to some literature problems. The computational results show that the proposed algorithm is efficient in minimizing the cycle time for the single-model stochastic assembly line balancing problem.
Understanding and anticipating the effects of surface roughness on subsurface stress in the design phase can help ensure that performance and life requirements are satisfied. One approach used to address this problem is to simulate contact between digitized real, machined surfaces, and then analyze the predicted subsurface stress field. Often, elastic-perfectly plastic contact models are used in these simulations because of their relative computational efficiency. Reported here is an analysis of the magnitude and location of maximum stress predicted using an elastic-perfectly plastic model. Trends are identified which then enable estimation of the upper bound of the simulation results based on surface discretization, operating conditions, and material properties. These estimations can be used as an effective and efficient tool for rapid prediction of maximum subsurface stress in real surface contact.
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