隨著訂單少量多樣的情況日益增長，產品種類的增加導致製程的限制與工單指派的難度有所提高，因此，製造業的生產規劃、排程與工單的派工對於推動工業3.5的聰明生產顯得非常重要。在染整產業中瓶頸站點為染色製程，與其他製程相比染色製程需要花更長的處理時間和整備時間，由於工廠中擁有多個不同的染色機群具有不同的生產能力與限制，然而大部分工單僅能由特定的染色機群所生產且效率有所不同。染整產業過去仰賴人工的方式進行作業，操作人員通常根據自己過往的工作經驗以及領域知識來決定工單的分配，然而這樣的作業模式不僅耗時且會受到操作人員的處理影響決策品質並缺乏訂單指派的整體考量。為了在傳統染整產業推動工業3.5聰明生產，本研究提出以非凌越型排序基因演算法解決非等效平行機台的指配問題，確保所有工單與染機都滿足其限制的狀況下，同時考量最小化總生產時間以及機台最大生產時間的雙目標問題，使得染色機台的工作負荷量能夠達到平衡。本研究以台灣的一家紡織公司進行了實證研究以驗證效度與可行性，透過系統化的導入，解決工單的最佳化分配進一步優化染色製程的生產規劃，同時提升生產管理與作業效率加強企業的競爭能力。

With the increasing complexity of small orders and high-mix product types, the variety of product mixs has increased the difficulty of for smart production inlcuding process restrictions, setups, and the assignment of work orders. Therefore, the integration of production planning, scheduling and dispatching of the manufacturing system is important to empower smart production for Industry 3.5. Dyeing process requires longer processing time and setup time comparing with other processes in the dyeing industry. There are many dyeing machine groups in the factory with different production capacities and restrictions. However, most work orders can only be produced by specific dyeing machine groups with different performance. In practice, the dyeing industry mainly relied on manual operation to perform operations, in which operation managers assign the work orders based on their experience and domain knowledge. However, such operation mode is not only time-consuming but also lacks of overall consideration. To empower smart production for traditional industry as an empirical study for Industry 3.5, this study aims to develop a non-dominated sorting genetic algorithm (NSGA) to solve the unrelated parallel machine assignment and conduct an empirical study for validation. While ensuring all work orders and dyeing machines satisfy their constraints, the objectives are minimizing total production time and maximum machine production time. For validation, an empirical study is conducted using practical data from textile company in Taiwan. The results have shown practical viability that the system with the developed solution can be implemented to optimize the allocation of work orders and support the production planning in dyeing process to improve production management and operational efficiency and thus strengthen the competitiveness of the enterprise.

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