太陽能是未來取代傳統能源需求最具有潛力的可再生能源，許多研究開始投入在使用太陽光產生照明之議題上。自然光照明系統可以使用固定式集光器來收集以及傳送，然而，學者尚未深入探討此類集光器大量應用在社區時，變動需求下之經濟性分析。因此，本研究提出一個隨機模型，用來建立一個可生產集光板的工廠之產能規劃與資源分配，兼考慮需求不確定下之利潤以及集光板效率最適化。即在最佳化太陽能集光系統效率的同時，權衡生產成本，以達到在社區能源需求不確定的條件下之利潤最大化。本研究先將此隨機模型轉換成確定模型，再進一步分別使用傳統遺傳基因演算法與優勢排列遺傳基因演算法求解。本研究使用柏拉圖邊界分析，探索雙目標式(照明亮度與獲利)的可行妥協解區域。研究結果顯示，本研究所提出的方法可以有效地同時提升集光板亮度表現以及工廠獲利表現。

Solar energy is one of the promising renewable energy options to substitute the increasing demand of conventional energy. Therefore, there are a number of research studies have focused on sunlight as a means of saving energy and creating healthy lighting. Natural light illumination systems have been collected and transmitted through static solar concentrators. However, these concentrators have not been manufactured significantly and then providing to community. Meanwhile, there is few research about manufacturing economics issue. Therefore, this study presents a model for a plant to fabricate these solar concentrators with considerations for both of uncertain demands and the energy saving for community. To be more specific, when we optimize the energy saving for community as consideration to brightness of such solar concentrator system, we also need to consider the tradeoff manufacturing cost needs for maximizing profit with uncertain solar concentrator demand from the community. Obviously, this model is stochastic model since it contains uncertain demand. Thereby, there are two approaches such as conventional genetic algorithm and a non-dominated sorting genetic algorithm with chance constraint technique to solve this stochastic model. Pareto frontier analysis for the dual objectives (brightness vs. profit) investigates the feasible, compromised solution region for plant manager making decisions. Experiments show that the proposed approach significantly improves the efficiency of brightness and earned profit simultaneously.

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