Nie Xianghui, Huang Guo H, Li Yongping
Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada.
J Air Waste Manag Assoc. 2009 Nov;59(11):1317-30. doi: 10.3155/1047-3289.59.11.1317.
This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.
本研究将区间数和模糊集的概念整合到动态规划优化分析中，以此来考虑系统的不确定性。所开发的区间模糊鲁棒动态规划（IFRDP）模型改进了先前的区间动态规划方法。通过引入模糊边界区间的概念，并为嵌入式线性规划问题提供区间参数模糊鲁棒规划方法，它能使高度不确定的信息有效地融入优化过程。因此，可以增强优化过程和解决方案的鲁棒性。该建模方法应用于一个假设问题，即城市固体废物（MSW）管理系统内的废物流分配规划和处理/处置设施扩建。生成并解释了废物管理设施容量扩展和相关废物流分配的区间解，以提供有用的决策方案。结果表明，可以获得稳健且有用的解决方案，并且所提出的IFRDP方法适用于与高度复杂和不确定信息相关的实际问题。
