## 1 Introduction  
In nowadays, as a result of globalization and technological developments, products have become more complex structure; the numbers and varieties of the components in products have increased, the number of suppliers has enlarged, and production systems have been transformed into a more complex system to be flexible. On the other side, distribution of products on time and at required quantity to the customer, who are located in different geographic areas in the world, became an important issue for the companies to improve the customer satisfaction level and profitability. Therefore, managers need the support of efficient approaches and methodologies to make right decisions in dynamic and complex market conditions. Production-Distribution Planning (PDP), which enables optimizing the supplying, manufacturing, and distributing process simultaneously, is one of the techniques to support the managers. PDP aims to determine the varieties and quantities of raw materials that will be supplied and go on making decisions related to the production planning and distribution of final products to the customers [1]. It can be operated by two different approaches in supply chain systems (SCS); centralized and decentralized. In centralized approaches, a single member of the chain makes decision on PDP for the whole system. In contrary to the first approach, all the members of the SCS manage their own PDP in decentralized approach [2]. PDP can be used for different decision problems. [PERSON] and [PERSON] [3], [PERSON] and et al. [4] and [PERSON] and et al. [5] adopted PDP to the supply network design problem. [PERSON] and et al. [6] and [PERSON] and et al. [7] developed PDP models for capacity and resource planning problems. [PERSON] and et al. [8] proposed a PDP model for scheduling and routing problems. There are two challenging issues to solve the PDP: size and complexity of the SCS and type of the parameters. The number of echelons, which represents the components in SCS such as suppliers, plants, warehouses, and retailers, the number of transportation path between components, number of products and number of time periods affect the problem size and complexity. [PERSON] and et al. [9], [PERSON] and [PERSON] [10], [PERSON] and et al. [11], and [PERSON] and et al. [12] considered single product models. [PERSON] and [PERSON] [13] and [PERSON] and et al. [14] developed multi products and single plant models. [PERSON] [15] proposed multiple products, plants, and single (or no) warehouse models. [PERSON] and et al. [16] handled multi products, plants, warehouses, and single (or no) end-user models. [PERSON] and [PERSON] [17], and [PERSON] and et al. [18] focused on multi products, plants, warehouses, end-users, and single transport path models. [PERSON] and et al. [19], and [PERSON] and [PERSON] [20] considered multiple products, plants, warehouses, end-users, transport paths, and no time period models. [PERSON] and et al. [21], and [PERSON] and et al. [22] handled multi products, plants, warehouses, end-users, time periods and single transport path models. [PERSON] [1] proposed multi suppliers, products, plants, warehouses, retailers, transport paths, and time periods model. PDP models can include different type of parameters such as deterministic [23; 24], stochastic [25; 26], fuzzy [27; 28], and fuzzy stochastic [29; 30]. On the other side, the numbers of echelons, products, transportation paths, customers and time periods define the size and complexity of SCS [1]. The PDP, which is small size and less complex, can be solved optimally by using analytic techniques whatever the type of the parameter is such as linear programming [24; 31], mixed integer programming [32], fuzzy programming [33; 34], stochastic programming [35; 36]. When the parameters are stochastic and the SCS is larger and more complex, simulation is an efficient technique to analyze the PDP performance. However, as indicated by [PERSON] [37], simulation is expensive and needs more effort to evaluate the results.