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題名：作業排程之現勢分析與新型態問題研究
作者：余東翰
作者(外文)：YU, TUNG-HAN
校院名稱：輔仁大學
系所名稱：商學研究所博士班
指導教授：黃榮華
學位類別：博士
出版日期：2018
主題關鍵詞：零工式工廠；啟發式演算法；批量分割；身障員工；供應鏈；Job shop scheduling；Metaheuristic；Lot-splitting；Disable workers；Supply chain
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現今的企業身處於高度競爭且瞬息萬變的市場中，不僅產品生命週期大幅縮短，對於大量客制化的需求也日漸頻繁及常見。因此，改善生產效率，同時維持高度生產彈性是相當必要的。除此之外，公司也不能再僅專注於自身的目標上，也需要瞭解其供應商及顧客的需求，並更進一步的將目光放到整體社會的利益範疇上，考量諸如氣候變遷及社會企業責任等新興議題。
緣此，本論文將聚焦於三個企業經常遭遇之實務排程問題，從宏觀至微觀，這些排程問題分別是：契約式滾動生產供應鏈排程問題、考慮身障員工之多目標零工式工廠排程問題及考量批量分割之多目標零工式排程問題。這些問題均根據實務目標與限制定義。
本論文建構一個求解模型來處理以上三個實務排程問題。首先以數學模型定義目標排程問題，同時以數學模型求得問題最佳解作為實驗比較基準。由於數學模型求解在問題規模及複雜性提升時，難以維持時效，為了快速求解這些複雜的實務排程問題，本論文將針對問題特性發展新型啟發式演算法，用以在最短時間內求得最佳解或近似最佳解。本論文提出三個創新的啟發式演算法，包括多費落蒙蟻群演算法(Multi-pheromone Ant Colony Optimization，MACO)、二階段蟻群演算法(Two-stage Ant Colony Optimization，TACO)及改良式基因演算法(Improved Genetic Algorithm，IGA)。透過實驗設計及大量模擬及實務資料測試以驗證所提出之演算法有效性，結果證實本論文所提出之三個創新啟發式演算法皆能在具良好時效的前提下求取最佳解或近似最佳解。
本論文所提出之求解模型在三個實務排程問題中皆能獲致良好結果，在求解契約式滾動生產供應鏈排程問題上，在未來需求不穩定變動時，亦能夠取得97%的近似完全資訊解。求解考慮身障員工之多目標零工式工廠排程問題時，雖然在研究中假設身障員工處理每一件工作的時間是一般員工的150%，但透過最佳化模型求解，在小規模問題中雇用身障員工的效率損失僅為5.89%，在大規模問題中雇用身障員工的效率損失僅為9.06%。求解考量批量分割之多目標零工式排程問題時，求解模型能夠在各種問題規模及樣態下，短時間取得最佳解或近似最佳解，不僅表現的比其他演算法更好，也證實能幫助一間實際存在的印刷廠提升15%生產效率。
綜上，本論文的貢獻在於針對複雜且不同範疇的實務排程問題，提出有效的求解模型及創新啟發式演算法。未來研究可以本篇為基礎，繼續發展及改良啟發式演算法，處理更複雜的實務排程問題。實務工作者亦可應用本論文之成果，改善企業之生產效率。
Modern firms are now challenged by highly competitive and ever-changing markets. The life cycle of products is greatly shortened, in the meantime, demands for mass customization become common and frequent. Improving manufacturing efficiency, while maintain high flexibility along the process is imperative. Moreover, firms cannot focus only on its individual goals, but also the goals of their suppliers and customers, which requires appropriate planning and scheduling on supply chain management. Furthermore, firms have to consider the benefits of the society as a whole. Emerging issues such as climate change and corporate social responsibility should be added.
Therefore, this dissertation discusses three realistic scheduling problems firms frequently have. Descending from macro to micro problem scope, these problems are a make-to-contract rolling supply chain scheduling problem, a multi-objective job shop scheduling problem with disabled workers, and a multi-objective job shop scheduling problem with lot-splitting. These problems are defined with focuses on realistic objectives and constraints.
This dissertation develops a model to solve the three realistic scheduling problems. First, the targeted scheduling problems are formulated as mathematical programming models, which also provides optimal solutions for the targeted problem. Second, novel metaheuristic algorithms are developed specifically to tackle these complex scheduling problems. In this dissertation, three innovative metaheuristics are developed, namely Multi-pheromone Ant Colony Optimization (MACO), Two-stage Ant Colony Optimization (TACO) and Improved Genetic Algorithm (IGA). Extensive data tests and experiment are conducted with problem-specific simulated data and real-world data. The results of the proposed metaheuristics are compared with the mathematical programming solutions and solutions returned by other effective metaheuristics referred in the literature.
The proposed model is able to provide satisfactory results. For the make-to-contract rolling supply chain scheduling problem, 97% of proximity to perfect information is achieved. For multi-objective job shop scheduling problem with disabled workers, the efficiency loss of hiring disabled workers is maintained at only 5.89% in small scale problems and 9.06% in large scale problems, where disabled workers take 50% time more than normal workers to process a product. For the multi-objective job shop scheduling problem with lot-splitting, the proposed model produces solutions that are equal or very close to optimal solutions. It also outperforms four other metaheuristics and provides up to 15% efficiency improvement rate for a real-world printing company case.
The contributions of this dissertation are innovation in metaheuristics and addressing realistic and complex scheduling problems in different problem scope. Future studies can build base on what this dissertation has established. Practitioners can apply the proposed model to improve the manufacturing efficiencies of their firms.
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