## 2 Literature Review  
This paper combined simulation modeling and the response surface methodology (RSM) to solve an order planning problem. A simulation and/or computational experiment are used to model a comprehensive supply chain network that corresponds with a number of supply chain operational elements and management levels [23]. Some studies that proposed simulation-based optimization models to explain supply chain systems are discussed in [24; 25]. Supply chain dynamics (response) are typically quantified in terms of order and inventory variance ratios. To experiment with the stochastic supply chain model, a simulation modeling approach was adopted. As a result of modifying the controllable variables, the simulation model was run to generate the various supply chain responses, and thus the functional relationship could be fitted and its response surface defined. RSM is a feasible alternative to modeling, on the other hand, which optimizes stochastic, dynamic, and complex systems such as supply chains. This approach is used to find a functional link between the complex responses of the supply chain and significant controllable variables that influence them [26]. [PERSON] et al. [27] used RSM for optimality analysis of the cost coefficients in mixed integer linear programming in facility location problems. The study implemented the design of experiments and applied least squares regression to determine cost coefficients that significantly affect the optimal total cost surface within setup coefficient ranges. In a problem of finding the suitable capacity for a factory, [PERSON] et al. [28] aimed at integrating simulation, Taguchi techniques, and RSM. Findings of this hybrid approach is a useful reference for businesses to analyze the dynamic relations among various factors, so as to determine the best combination of their levels that optimizes the impact of demand uncertainty on the performance of the supply chain. [PERSON] et al. [29] developed a hybrid model of a process-based simulation ProC/B toolset and RSM for the optimization of the process chain models. In this paper, the experiment of the central composite design was applied to find the optimal values of response variables. The \"bullwhip\" effect, known also as order variance amplification, is a major cause of supply chain deficiencies. [PERSON] et al. [30] studied a three-stage simulation with a single retailer, a single wholesaler, and a singer-producer under both centralized and decentralized chains, in order to analyze the causes of the bullwhip effect from two dimensions of order and inventory variance using RSM. As an extension, a multiobjective minimization problem in which the order and inventory variance ratios are separated into two objective functions was developed by [PERSON] et al. (2018). RSM was used in a hybrid evolutionary approach to analyze interactions between the variance ratios and their corresponding effects, in order to optimize supply chain systems. For measuring design parameters on the bullwhip effect and dynamic responses, [PERSON] et al. (2018) analyzed its influences on the supply chain's performance using a hybrid Taguchi and dual RSM. This paper offered practical solutions to the supply chain managers and designers in the trade-off between customer service level and inventory holding cost under an uncertain environment. To provide an overview of recent contributions, several studies that presented relevant problem characteristics are shown in Table 1. Concerning order planning problems, many studies approached exact methods such as NLP ([PERSON] et al., 2018), MOLP ([PERSON] et al., 2018; [PERSON] et al., 2018), and mathematical models ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). The widely used method is applying inventory policies such as the economic order quantity (EOQ), economic production quantity (EPQ), (S, T), or (R, Q) model ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018).