透過控制生產過程的環境參數，植物工廠能在任何季節穩定地為市場供給多種類且高品質的蔬菜。為了滿足市場對不同蔬菜的需求，如何將耗時不同的栽培作業有效率地指派予工廠中的生產單位是相當困難的議題。本研究將此問題描述成整數規劃模型，在考量滿足需求、產能限制及生產限制下追求具最小收成距離之生產配置。本研究以AMPL介面於NEOs server上利用CPLEX solver進行最佳化之求解。研究大量的小型範例後，我們歸納出數個重要發現，並利用這些發現提出一啟發式演算法。此演算法分有兩階段：在第一階段中，將檢驗現行的最大產能是否能夠滿足客戶的要求，並在第二階段中產生一個用來指派栽培作業予生產單位的配置計畫，且經由此計畫配置的作物在成熟時只需要極小的途程便能收集完畢。在隨機產生的62組中型大小情境中，演算法將透過與最佳解進行比較、分析並進行驗證與討論。最後在大型例子裡，演算法將再各種情境下與NEOs server運算8小時的結果進行比較以驗證演算法的一致性。

To supply high-quality and consistent vegetables to meet various customer demands, plant factories face a challenge in the allocation problem when considering different growth times for different types of crops. This thesis develops integer programming formulations to minimize harvest travel distance as well as consider customer demand requests, factory capacity and system configuration. Several observations are found based in the optimal solutions in small-sized examples, and heuristic algorithms are proposed according to these optimal solutions. Comparisons of the heuristic algorithms and optimal solutions are provided in 62 randomly generated medium-sized examples. For larger-scale examples, heuristic algorithm results are better than the solution run in NEOs server by AMPL and CPLEX solver after 8 hours. Phase 1 of the algorithm provide feasibility check to determine if the customer requirements can be fulfilled in the current capacity. Phase 2 proposes searching methods to find allocation with short travel distance in harvest.

方煒，台灣植物工廠發展現況與展望，台灣大學生物機電系
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