ABSTRACT
In fact, the decision maker cannot obtain the perfect information of each parameter in the decision systems. Sometimes, the probability distribution function of the random variable may be partially known by estimation. In such environment, the
1 INTRODUCTION
The production and distribution plan of agricultural products, as an overall research field comprising of cultivation, harvest, storage, processing and distribution, plays an important role in the architecture of advanced planning systems (Akkerman et al., 2010; Shukla & Jharkharia, 2013). Recently, the activities from production to distribution has attracted many researchers’ attention lately as a consequence of several reasons, such as the national focus on recent cases of agricultural produce contamination, the changing attitudes of a more health conscious and the preference of the better informed consumers (Ahumada & Villalobos, 2009). For example, Ahumada and Villalobos (2011) presented a mixed integer linear programming model used for production and distribution of agricultural produce with the objective of maximizing the revenues of a producer. Widodo et al. (2006) designed a dynamic programming approach to integrating the production, harvesting and inventory planning of flowers through the use of growth and loss functions for maximizing the demand satisfied.
