植物工廠是一種新式的農作物生產方式，透過對生產環境因素，如溫度、溼度、光線、水分及營養物的完全控制，加速植物生長的效能及提升作物品質。如何選擇適合的作物並進行產能的排程規劃以提升植物工廠的營利成為植物工廠經營的重要課題。本研究考量數個植物工廠營運上的課題，如農作物的收成價格、環控限制、清潔維護等，進行農作物排程規劃。本研究將農作物排程問題描述成一個混合整數規劃問題，其目標式為欲求利潤最大化，而限制式分別考量(1)作物坪效價格、(2)同家族作物以不相鄰方式種植、(3)植物工廠清洗和保養維修及(4)太陽光照射所受限的環境條件，進而以最佳化模式求解。在實證研究上，本研究設定了多個不同的植物工廠模型，並利用AMPL求解器進行分支界線演算法（Branch-and-Bound Algorithms）求解。當實驗模型的層架規模擴大時，AMPL求解的計算時間將呈現指數增長。因此，本研究使用R語言發展一排程演算法名為Heuristic Plant Factory Scheduler (HPFS)，來快速求解植物工廠混合整數規劃排程問題。此研究所提出之演算法利用遞迴(Recursive)的方法將每個層架之空排程，依二元數方式分開求解，求解過程亦依照坪效價格及相關限制進行作物篩選及比較。其結果不但可以快速求解，且其求解品質並不亞於最佳解。此演算法將可廣泛地運用於求解植物工廠之作物排程問題。

Plant factory is an environmental controlled facility which can sustain the stable crop cultivation with fast production and better quality by controlling temperature, humidity, lighting, nutrient supply, and other cultivating factors. Due to the relatively high cost of energy consumption, how to select crops and conduct the cultivation planning to enhance the profitability is an important issue. In this research, the plant factory scheduling problem was formulated as a mixed integer programming (MIP) problem. The objective function is to seek the maximum revenue of the plant factory by considering several practical operating conditions such as (1) crop market value per unit and time, (2) adjacent planting issue across botanical categories, (3) cleaning and maintenance, and (4) environmental constraints due to the limitation of sunlight supply. The operating conditions are formulated as constraints, and the MIP problem was solved by AMPL programming model to obtain the optimal crop selection and schedule. The numerical study shows that the computation time of AMPL solver is exponentially increasing when the problem domain is larger. Therefore, the heuristic algorithm named as Heuristic Plant Factory Scheduler (HPFS) which employs the recursive technique was proposed to provide the fast solver for the plant factory scheduling problem. The experimental result shows that HPFS is able to dramatically accelerate the computational performance by only taking ~1% of AMPL computation time when the problem space is large. Although the solution quality of the proposed algorithm has ~20% revenue discrepancy against the optimal solutions, this result is acceptable because HPFS considers the initialization issue while the MIP program does not. In term of the practical implementation, the proposed algorithm HPFS can be applied to solve the plant factory scheduling problem with the better crop selection and revenue under difference cultivation constraints.

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