半導體廠的廢溶劑貯存、清除與處理於目前的科學園區內已愈來愈複雜，關注的細節也愈來愈多，在目前現實的工作環境之下，此類安排廢溶劑清運的工作大多由業務工程師負責，派遣的狀況常依據經驗來安排，並根據處理商或處理廠的合約規範以及現有的資源進行派遣清運作業，如此決策者要在廠內各種規範下進行適當調度派遣並且降低成本時為重要之課題。本研究係探討多種化學物質之清運派遣，並在各種特殊限制條件下，求最小成本的處理商派遣為目標，進行資源指派與調度，以提供決策者派遣時之參考，除此之外，更依據各種參數進行敏感度變化之趨勢分析，進而提早發現各種無法預知的結果。 為證實本模式實用性與合理性，本研究以新竹某高科技半導體廠為範例，使用廠內各種運作條件及基本資料訂定參數，利用整數規劃方法建構一套數學最佳化模式，將資料以Microsoft Excel 2010軟體方式輸入LINGO套裝軟體求解，即可得化學物質清運處理後之各種處理商派遣總成本最佳化結果，除此，並以最佳化模式之派遣結果與一般廠內實務經驗指派結果進行分析比對，結果發現所需派遣總成本比人工經驗指派來的低且效率更好。故顯示本研究模式確實可靈活運用於半導體廠廢溶劑清運處理派遣規劃問題，除幫助決策者進行更有效率的規劃廢溶劑清運派遣問題外，更可預期並規劃未來所需之派遣預算，具有輔助決策者辦理規劃作業之實用性。 Waste solvent storage, removal and treatment operations for semiconductor plants have become more and more complex, and more and more attention is being paid to these types of operations. In the current real life working environment, arranging for waste solvent collection and transportation is mostly dealt with by business engineers. Dispatch arrangements are often based on experience, in accordance with business contracts the situation at the treatment plant and the current resources available for collection and transportation. It is important that the policymaker should dispatch vehicles moderately and reduce the operating costs in accordance with the regulations of the plant. This study discusses the nature of dispatching multiple chemical substances. A key point in this research is to look for the minimum cost for designating and dispatching resources and offer a reference of dispatch planning to the policymaker under different types of special limited conditions. Additionally, the analysis is based on multiple parameters so as to include the tendency of sensitive changes to find multiple unpredictable results in advance. To confirm the practicality and the rationality of this model, a high-tech semiconductor plant as our example. Using multiple operating conditions and basic data to set up parameters, we also utilize integer programming methods to set up a mathematical optimization model. We input data into the LINGO software run by Microsoft Excel 2010 to obtain our answers, and get the optimization results. In addition, we analyze the results of the optimization model and compare them with results from dispatchers using solely their own practical experience. We find that the total cost is less and the efficiency is better when using our model than dispatching based on the individual’s experience. It is shown that this model can be used flexibly in programming problems for the treatment of waste solvents in semiconductor plants. Not only does it help the policymaker solve dispatch problems more efficiently, but it also helps with predicting the future budget.
