在半導體產業中，封裝為半導體後端重要的階段。許多半導體公司基於專注核心專長以及降低製造成本與風險，多採用封裝外包的方式，因此半導體封裝外包是半導體供應鏈和製造策略中重要的一環。此外，考量降低封裝時間與生產風險，半導體公司在策略上往往選擇多個封裝外包商，和給予不同的策略伙伴關係和分配比例。因為封裝外包物料前置時間約一、二個月，故需規劃每月物料需求規劃配置，在基於每月物料需求規劃配置，每天需進行即時訂單指派計劃，每日即時訂單指派計畫需使用最新即時資訊來考量多個目標及限制問題，故其為一複雜的即時決策流程。

本研究目的係利用混合整數規劃與目標規劃來建構半導體封裝外包即時訂單指派模型，並發展決策支援系統實際應用於半導體封裝外包作業上。所提出之模型不僅考慮一般生產規劃之目標，亦考量了車次問題與大批量議題，由於本問題為一即時決策流程，本研究在求解前使用了資料預處理技巧，使得資料量減少以達到降低求解時間。在實證研究結果中可發現本研究所提出之模式方法在求解速度上比依據人工經驗法則的啟發式求解更有效率，並且能符合實際目標與限制，藉由此模式系統可提供半導體廠家在封裝外包上相關管理措施。在未來研究方向上，可探討從外包商的角度來分析即時指派問題與穩健系統變數，以進一步提升本研究之效能。

In the semiconductor industry, assembly is the important stage in backend operation. To focus on core competence and reduce capital expenditure, many semiconductor companies adopt assembly outsourcing for reducing the manufacturing cost and business risk. Thus, assembly outsourcing is a critical component involved in the semiconductor supply chains. Furthermore, semiconductor companies usually maintain multiple assembly vendors for reducing assembly time and production risk. Since lead time of assembly materials is about one or two months, monthly material requirement planning will be determine to allocate orders to the vendors. Based on monthly material requirement planning, daily real time order assignment planning is then needed to assign the orders to specific vendors in real time. Daily order assignment planning considers the multiple objectives and constraints based on real time information. It is a complex real time decision process on daily order assignment in semiconductor assembly outsourcing.

To address this need in real settings, this study aims to construct mixed integer linear programming (MILP) and goal programming (GP) model for real time order assignment problem and develop decision support system to implement in semiconductor assembly outsourcing. The proposed model not only considers the general objectives involved in production planning but also considers the shuttle status and huge lot issues. Since this problem is a real time decision process, we apply technology of data preprocessing to reduce the amount of data for reducing solving time before solving this problem. We conducted an empirical study in a semiconductor company for validation. The results showed the solving speed of proposed model is more efficient than heuristic method based on expert experiences and the results can also achieve the objectives under the limitations of constraints in real settings. Furthermore, we developed a daily assignment model and decision support system embedded with the proposed model to provide associated management functions to assist the decision makers in the semiconductor companies. Future research can be done to analyze real time assignment problem from vendor’s perspective.

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