製造單元分群問題一職都是彈性製造系統規劃中一個重要問題，本研究提出一結合粒子群演算法與群體漸進學習法之啟發式演算法並將其運用於製造單元規劃之問題。該演算法藉著機台編碼及比較工件途程之相似性將工件分類及機台分群以達到將製造單元系統功效最佳化之目的。本研究並運用十個測試問題與其他演算法比較。結果顯示本研究所提出之整合試驗算法在六個問題中表現最佳。尤其是在較小的機台工件問題中，本研究所提出之方法表現出大幅度改善。

The development of meta-heuristics has received great interest in recent years. Several methods have been implemented in cell formation. In this thesis book, the problem of grouping parts into families; and machines into cells are considered with the objective of maximizing the grouping efficacy. A hybrid of particle swarm optimization and population-based incremental learning (PBILSO) is proposed and used for solving this problem. The proposed algorithm uses a modification of permutation with separator encoding scheme and similarity measure is used to evaluate similarity between parts as an index for assigning parts to cells. A set of 10 test problems with various sizes from literatures are used to test the performance of both algorithms. The Taguchi method is used for determining the initialization parameters. Numerical experiments have proved that the proposed method can increase the grouping efficacy for 60% cases and 10% near optimal than other algorithms in several references. It is also proved that the hybrid can generate more convergence result so that the number of hits increasing and able to group the machine and part into cells simultaneously.

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