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W e consider a class of the subset selection problem in ranking and selection. The objective is to identify the top m out of k designs based on simulated output. Traditional procedures are conservative and inefficient. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correctly selecting all of the top-m designs subject to a constraint on the total number of samples available. For an approximation of this correct selection probability, we derive an asymptotically optimal allocation and propose an easy-to-implement heuristic sequential allocation procedure. Numerical experiments indicate that the resulting allocations are superior to other methods in the literature that we tested, and the relative efficiency increases for larger problems. In addition, preliminary numerical results indicate that the proposed new procedure has the potential to enhance computational efficiency for simulation optimization.

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This paper studies two tactical level decision problems arising in transshipment hubs: berth template planning that is concerned with allocating berths and quay cranes to arriving vessels, and yard template planning that is concerned with assigning yard storage locations to vessels. These two tactical level decisions interact with each other. A mixed-integer programming model is proposed to integrate the berth template and the yard template planning with the aim to minimize the service cost that is incurred by the deviation from vessels' expected turnaround time intervals, and the operation cost that is related to the route length of transshipment container flows in yard. Moreover, a heuristic algorithm is developed for solving the problem in large-scale realistic environments. Numerical experiments are conducted to prove the necessity of the proposed model and also validate the efficiency of the proposed heuristic algorithm. For a set of real-world like instances, the heuristic algorithm can obtain good berth and yard templates within a reasonable time.