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Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.

A fuzzy two-stage stochastic program for managing water resources under uncertainty is presented. The program integrates discrete intervals in the solution to the problem.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2005.02.008 Byline: Gordon H. Huang (a), Jonathan D. Linton (b), Julian Scott Yeomans (c), Reena Yoogalingam (d) Abstract: Evolutionary simulation-optimization (ESO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. Grey programming (GP) methods have been previously applied to numerous environmental planning problems containing uncertain information. In this paper, ESO is combined with GP for policy planning to create a hybrid solution approach named GESO. It can be shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from the regional municipality of Hamilton-Wentworth in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning problems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty. Author Affiliation: (a) Faculty of Engineering, University of Regina, Regina, SK S4S 0A2, Canada (b) Lally School of Management, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA (c) Management Science Area, Schulich School of Business, York University, Toronto, ON M3J 1P3 Canada (d) Department of Management, Faculty of Business, Brock University, St. Catherines, ON L2S 3A1 Canada Article History: Received 30 July 2004; Revised 30 December 2004; Accepted 14 February 2005

The problem of finding the optimal balanced schedule for mixed-model, multi-level just-in-time facilities operating under a pegging assumption is considered. The pegging involves dedicating the output of the production level to a specific item into which it will be assembled at the assembly level. To this end, solutions to the weighted single-level version are modified and interpolated to the multi-level case.

The efficient grey programming (GP) procedure is used to create the starting points for the policy generation process in order to improve the solution time performance quality of evolutionary simulation-optimization (ESO), while readily exploiting all of the benefits attributable to GP procedures. The resulting hybrid procedure would be referred to as Grey Evolutionary Simulation-Optimization (GESO).