設施葉菜生長排程決策輔助系統建置之研究

本研究旨在規劃建置一個農場智慧接單與生產排程管理系統，通過專家深度訪談歸納出建置農場生產排程決策輔助中9種要素組合與原理依據，並嘗試以層級分析法(Analytic Hierarchy Process, AHP)，建構前開要素指標應具備的主要指標與次要指標，再經專家問卷調查兩兩指標之間的優判所得結果，得到各主要與次要評估指標之權重，據以設計系統所需功能、表單、輔助決策功能與決策流程，期望透過此研究設計，藉由物聯網技術與大數據分析技術，與日後規劃之系統運作能夠結合，成為一套農場訂單排程生產策輔助系統。實證分析結果發現，在系統運作主導之下，預計移苗盤數與實際盤數之正確率佔94.8%；預計採收日與實際採收日之正確率達92.7%，顯示此系統具有決策輔助之效果，除了可確保契約產量並提升接單率外，進而可優化資源配置，並提升經營效益，實現設施葉菜農場生產的智能化管理。

關鍵字

設施葉菜；生產排程；決策輔助系統

並列摘要

The purpose of this research is to plan and build a smart order receiving and production scheduling management system for greenhouse-grown leafy vegetables. Through in-depth interviews with experts, it summarizes the combinations and principles of nine elements in the establishment of farm production scheduling decision support system.The hierarchical analysis method (Analytic Hierarchy Process, AHP) is adopted to analyze the main indicators and secondary indicators that should be possessed before the construction of the factor indicators. The weight of each main and secondary evaluation indicator is obtained through the pairwise comparison results of the expert questionnaire survey. The results of AHP are then used in designing the functions, forms, auxiliary decision-making functions and decision-making process required by the system. It is expected that through this research and design, it can be combined with the system operation plan in the future to become a farm order scheduling production decision-making auxiliary system. Through the Internet of Things technology and big data analysis, this application realizes the intelligent management of greenhouse production. The results of the empirical analysis found that the accuracy rate of system dominance was 94.8% between the predicted number of transplanted seedling trays and the actual number of transplanted seedling trays; the accuracy rate of the estimated harvest date and the actual harvest date was 92.7%. These results suggest that the system not only can ensure contracted output to increase order acceptance rate, but also can optimize resource allocation to improve operating efficiency.