Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

Programming challenges and impediments to reform: identifying pragmatic solutions

Using two recently published essays by the current writer that assesses the dismal record of performance of Planning, Programming, and Budgeting System in enabling communist legacy defence institutions in Central and Eastern Europe to develop viable defence plans, this essay argues the need for deep reforms in the region’s defence institutions. To guide this reform effort, pragmatic solutions are suggested to improve the ability of these organisations to produce viable defence plans. Recommended reforms are: (1) conduct conceptual and cultural “audits,” (2) make operational and financial data central to decision-making, (3) change current organisational sociology, (4) examine planning methods and practices, and (5) stress the need to adopt policy frameworks to drive the operation of defence institutions.     

Heuristic algorithms for routing yard‐side equipment for minimizing loading times in container terminals

This paper discusses a method of routing yard‐side equipment during loading operations in container terminals. Both the route of yard‐side equipment (such as transfer cranes or straddle carriers) and the number of containers picked up at each yard‐bay is determined simultaneously. The objective of the problem in this paper is to minimize the total container‐handling time in a yard. The size of the search space can be greatly reduced by utilizing inherent properties of the optimal solution. An encoding method is introduced to represent solutions in the search space. A genetic algorithm and a beam search algorithm are suggested to solve the above problem. Numerical experiments have been conducted to compare the performances of the proposed heuristic algorithms against each other and against that of the optimal solution. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 498–514, 2003