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The present paper introduces a grey dynamic programming (GDP) method by incorporating concepts of grey systems and grey decisions within a dynamic programming framework as a means for decision making under uncertainty. The grey dynamic programming approach improves upon previous dynamic programming methods by allowing uncertain information to be directly communicated into the optimization process and resulting solutions, such that decision alternatives could be generated through the interpretation and analysis of the grey solutions according to projected applicable system conditions. The method also does not lead to more complicated intermediate models, and thus has reasonable computational requirements and is applicable to practical problems. Application of the method to a hypothetical problem of waste management facility expansion/ use planning within a municipal‐solid‐waste management system indicates that reasonable solutions have been generated. Comparisons between grey dynamic programming and other dynamic programming methods are also provided.
Huang et al. (Thu,) studied this question.