## 5 Conclusions and Future Work
The second is related to the negotiation in the coffee commercialization among the actors in the chain. The Cherry coffee purchasing parameters include the region of the crop, height and average annual temperature of the coffee plantation, the variety of coffee, the percentage of mature grains, the Parchment coffee yield and the time elapsed since the cut. Parameters for trade-in Parchment coffee include dry Cherry percentage; humidity percentage; yield to green coffee; uniformity of color; the number of defective coffee beans with respect to the weight of the sample analyzed; coffee beans significantly free of improper aromas; and defective beans percentage in the coffee lot. [PERSON] was in charge of project administration and the research related to the application of artificial intelligence techniques in the supply chain. [PERSON] was in charge of the research project related to the application of the agent modeling approach in the supply chain. [PERSON] was an advisor for the supply chain management of the case study. [PERSON] proof-read the paper. [PERSON] was a collaborator in the use of the simulation platform. [PERSON] was a collaborator in the fieldwork and analysis of the collected data. This research received no external funding. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sorting Process Scheduling for Not-washed Robust Coffee Entries**} \\ \hline \hline \multicolumn{4}{c}{**Input**} & \multicolumn{4}{c}{**Output**} \\ \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Fuzzy Set**} & \multirow{2}{*}{**Knowledge Base**} & \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Fuzzy Set**} \\ \cline{2-2} \cline{5-6}  & **Definition** & **Interval** & & & **Definition** & **Interval** \\ \hline \multirow{3}{*}{Serious defects (SD)} & Normal & [-13.5, -5.58, 9.17, 10.53] & \multirow{3}{*}{} & Not-washed robust & Mix & [0.0, 16.0, 0.33] \\  & Regular & [10.3, 10.5, 13.3, 13.5] & \multirow{3}{*}{} & Not-washed robust & Pneumatic & [0.16, 0.33, 0.5] \\  & Many & [13.3, 13.5, 22.2] & & Schedule & Optical & [0.33, 0.5, 0.66] \\ \cline{2-2} \cline{5-6}  & Normal & [-7.2, -0.8, 9.2] & 216 inference & 1 (nwwS1) & Sitt & [0.5, 0.66, 0.83] \\ \cline{2-2} \cline{5-6}  & Many & [9.9, 9.2, 20.23, 20.23] & \multirow{2}{*}{} & Not-washed & Pneumatic & [0.0, 0.2, 0.4] \\ \cline{2-2} \cline{5-6}  & Normal & [-17.9, 0.106, 3.32, 3.5] & & Not-washed & Pneumatic & [0.0, 0.2, 0.4] \\ \cline{2-2} \cline{5-6}  & Regular & [3.2, 3.68, 4.5] & \multirow{3}{*}{} & Robust & Optical & [0.2, 0.4, 0.6] \\ \cline{2-2} \cline{5-6}  & Many & [4.3, 4.64, 11.9, 11.9] & & Schedule & Sitt & [0.4, 0.6, 0.8] \\ \cline{2-2} \cline{5-6}  & Appropriate & [-7.2, -0.8, 138. 14.9] & \multirow{3}{*}{} & \multirow{3}{*}{} & Mix & [0.0, 1.6, 0.33] \\ \cline{2-2} \cline{5-6}  & Low & [14.7, -2.57, 15.8] & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\ (GA) & Very low & [15.5, 16.4, 16.86] & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\ Null-minimum & [16.2, 17.01, 20.2] & & & & & \\ Little & [-1.7 \(\times\) 10\({}^{4}\), -7400, 9080, 9180] & & & & & \\ \cline{2-2} \cline{5-6}  & Normal & [7250, 8250, 1.44\(\times\) 10\({}^{4}\)] & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\ Much & \([1.38\times\) 10\({}^{4}\), 1.48 \(\times\) 10\({}^{4}\)] & \(2.33\times\) 10\({}^{5}\), & & & & \\ \cline{2-2} \cline{5-6}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 4: Fuzzy model for process scheduling of not-washed Robust coffee entries: fuzzy sets and operating intervals.