在排程問題中，快速的完工時間已經不再是唯一的考量，如何平均分配機台 工作量，降低總作業時間等成本需求也是目標之一。發展一個演算法能同時求取 多目標的最佳化已然成為熱門。
本論文提出基因演算法搭配新的突變運算，交換關鍵製程和重新插入關鍵製 程，來求解多目標彈性零工式工廠排程問題。針對演算法無法拓展機台總工作量 較小的區域，我們將「隨機選擇關鍵製程然後隨機插入另一個機台」，改成「選 擇執行時間最長的關鍵製程並重新插入該製程中執行時間最小的機台」，透過減 少製程執行時間有效的降低非凌越解的機台總工作量。
接著我們針對新的突變運算的兩個缺點提出兩種改良方式。第一種改良是為 了解決新的突變運算太著重於減少機台總工作量而忽略另兩個目標的搜尋。我們 將每次都選擇「執行時間最長的關鍵製程」改為選擇「工作量最大的機台上的關 鍵製程」，並重新插入執行時間最小的機台，藉此保留非凌越解的拓展度並成功 的降低最大機台工作量。而另一個改良方法則是要解決當執行時間最長的關鍵製 程已經在執行時間最小的機台上，導致無法有效降低機台總工作量而造成計算資 源的浪費。我們不再選擇執行時間最長的關鍵製程，改為選擇確定可以降低執行 時間的關鍵製程，若沒有則隨機選擇一個製程，然後同樣重新插入到執行時間最 小的機台。實驗結果證明該改良方式能更加的拓展非凌越解的分布並提高非凌越
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解的品質。
實驗結果顯示，我們提出的演算法在非凌越解個數較多的問題上，能更新一 半以上的非凌越解。
在往後的發展中，可以試著不要每次都重新插入執行時間最小的機台。雖然 目前的演算法可以確實降低非凌越解的機台總工作量，然而該突變運算就只會降 低而不會提升。假如有一個非凌越解可以藉由增加一點點的機台總工作量就能大 幅降低最大完工時間，現在的突變機制就無法找到。另一方面，我們可以針對問 題的特性，執行投資報酬率較高的演算法；又甚至可以根據每個解的狀態去做適 合的變動來達到搜尋資源的有效利用。
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