## 3 Methodology
### Agent Description  
Derived from this mapping in the framework of the SCOR model, the identified agents and activities for the green coffee SC are listed in Table 1. The simulation model implements the Parchment coffee refinement process at the focal company that interprets historical data from 2016 to 2018 fed by the green coffee SC actors under investigation. The decision support agents serve the purposes of deriving useful knowledge to accomplish the decisions: (i) for the Cherry-coffee producer Production Agent (CP Agent), the decision regarding the estimation of Cherry coffee yield obtained at the coffee plantation; (ii) for the Green-coffee producer Source Agent (GS Agent), the Parchment coffee sample verification decision; (iii) for the Green-coffee producer Production Agent (GP Agent), the production plan establishment decision, and (iv) for the Green-coffee producer Reschedule Agent (GR Agent), the plan production update \begin{table}
\begin{tabular}{c c} \hline \hline
**Agent** & **Activities** \\ \hline Cherry-coffee producer Production Agent (CP Agent) & Production \\ Cherry-coffee producer Delivery Agent (CD Agent) & Satisfy market demand \\ Parchment-coffee producer Source Agent (PS Agent) & Source Products \\ Parchment-coffee producer Delivery Agent (PD Agent) & Satisfy market demand \\  & Source Products \\ Green-coffee producer Source Agent (GS Agent) & Verify Products \\  & Plan Production \\ Green-coffee producer Production Agent (GP Agent) & Production \\ Green -coffee producer Inventory Agent (GI Agent) & Update inventory \\ Green-coffee producer Reschedule Agent (GR Agent) & Update Plan Production \\ Green-coffee producer Delivery Agent (GD Agent) & Satisfy market demand \\ Wholesale Market Agent (WM Agent) & Place order \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Agents and activities for the green coffee SC. Figure 4: Level three process elements comprising the make-to-order process at the focal company. decision. The decision-making rule approach for the CP Agent and the GS Agent is rule-based representation using Fuzzy Logic involving an inference engine with IF-THEN type rules. For the GP Agent, a decision-making rule approach based upon the coupling of IF-THEN fuzzy inference rules and equation-based representation using mixed integer programming with the aim to maximize customer service level is used. Finally, the decision-making rule perspective used for the GR Agent is equation-based representations using mathematical equations once the customer service level falls below the expected level. In this study, the decision-making rule approach using fuzzy logic is used to generate a knowledge base for the CP Agent, the GS Agent, and the GP Agent. The diverging opinions of the experts are represented as blurred triangular and trapezoidal numbers, which describe the coded knowledge of the expertise of the green coffee SC actors. Accordingly, a Mamdani model codifies the decision criteria related to (i) agricultural practices for growing Cherry coffee in coffee plantations (CP Agent); (ii) the process of sensorial evaluation of coffee beans from which gustatory and olfactory characteristics of the grain are evaluated for quality scoring of a Parchment coffee sample (GS Agent); and (iii) the sorting process scheduling of Parchment coffee entries based on coffee entry quality grading, the percentage of defective coffee beans percentage in the coffee lot (GP Agent). In the Mamdani type model, multiple inputs and outputs represent information using Fuzzy Logic; each input and output variable is represented through a Linguistic variable. The rule base representation is developed according to the IF-THEN type, which constitutes the inference method based on the knowledge base and consequent inference engine. The defuzzification process uses the centroid calculation method. Fuzzy IL Logic is used to mimic the knowledge and expertise of the agricultural field dynamics for coffee growth.