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SUMMARY The breakdown of trade barriers among countries led to an enormous increase in the volume of international trade. Consequently, the amount of bulk cargo carried in containers and transported overseas exploded because of flexibility and reliability of this type of transportation. So, the efficiency in container terminals has emerged as a major problem. In this work, we deal with an operational problem in container terminals. We try to develop strategies to minimize the total time required to retrieve the containers from a bay in a predetermined sequence. The problem is solved optimally using a branch and bound‐based algorithm, and alternative heuristics that give near‐optimal solutions are proposed. Copyright © 2012 John Wiley & Sons, Ltd.

We consider the multi-period location planning problem of emergency medical service (EMS) stations. Our objective is to maximize the total population serviced by two distinct stations within two different response time limits over a multi-period planning horizon. Our aim is to provide a backup station in case no ambulance is available in the closer station and to develop a strategic plan that spans multiple periods. In order to solve this problem, we propose a Tabu Search approach. We demonstrate the effectiveness of the proposed approach on randomly generated data. We also implement our approach to the case of Istanbul to determine the locations of EMS stations in the metropolitan area.

Stochastic boolean function evaluation (SBFE) is the problem of determining the value of a given boolean function f on an unknown input x, when each bit x i of x can only be determined by paying a given associated cost c i . Further, x is drawn from a given product distribution: for each x i , Pr[x i = 1] = p i and the bits are independent. The goal is to minimize the expected cost of evaluation. In this paper, we study the complexity of the SBFE problem for classes of DNF formulas. We consider both exact and approximate versions of the problem for subclasses of DNF, for arbitrary costs and product distributions, and for unit costs and/or the uniform distribution.