多站點生產規劃與排程－ 以電子化學品產業實證研究為例

在近年台灣高科技產業蓬勃發展下，電子產業製程所需高純度少雜質的電子化學品需求增加，電子化學品產業擁有大量生產限制，其製程需考量包含機台產能、人力、原料、包材等限制，且由於高科技產業對於產品達交的重視，使達交成為電子化學品產業排程的指標。本研究之目的是建立電子化學品產業生產流程之多站點排程最佳化模型，其中包含以時間間隔為基底所建立的數學規劃模型，利用該模型釐清電子化學品廠商的生產限制，並開發結合區域搜尋之遺傳演算法求得排程之最佳化，以達到訂單達交的目的。本研究以桃園某電子化學品供應商進行實證案例，並透過12組不同的情境設定，得到各種情境下本研究方法之排程結果，並利用產業所重視的達交指標分析排程狀況，可得到本研究方法在生產情況較為困難之情境下依然具有有效性，能夠在產能接近極限時完成99.8%的訂單，並至少達交87.7%之訂單。

關鍵字

生產規劃與排程；遺傳演算法；電子化學產業；時間間隔

並列摘要

The electronics industry in Taiwan is flourishing in recent years, and it is increased demand for electronic chemicals which are high degree of purity, few-particle and required in the manufacturing process in the electronics industry. There are complex production constraints in electronic chemicals production, including production capacity, manpower, raw materials, packaging materials and other restrictions. Because of the electronics industry puts a high premium on product delivery, the electronic chemicals be delivered on time or not would be a important target of scheduling in the electronic chemical industry. The study aims to develop a multi-stage production planning and scheduling model for the electronic chemical industry. It concludes a mixed-integer linear programming based on time slot to clarify the production constraints in electronic chemical industry, and a genetic algorithm with local search to find the optimal solution which has the most jobs delivered on time of scheduling problem. The study cooperates with an electronic chemical industry in Taoyuan for an empirical research. We use model of this study to get the schedulings in the different 12 scenarios, and evaluate schedulings by the targets which electronic chemical industry attaches importance to the scheduling. And we can also find the model of this study is useful in all of the scenarios and ensure the validity of the study. Even in the hard production situation, the model can produce 99.8 percents of jobs and at least 87.7% of jobs would be delivered on time in the planning horizon.

並列關鍵字

production planning and scheduling ； genetic algorithm ； chemical industry ； time slot

參考文獻

Batres, R. (2013), “Generation of operating procedures for a mixing tank with a micro genetic algorithm,” Computers & Chemical Engineering, Vol. 57, pp. 112-121.

Chien, C.-F., Tseng, F.-P., Chen, C.-H. (2008), “An evolutionary approach to rehabilitation patient scheduling: A case study,” European Journal of Operational Research, Vol. 189, No. 3, pp. 1234-1253.

Fang, N., Zhou, J., Zhang, R., Liu, Y., and Zhang, Y. (2014), “A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling,” International Journal of Electrical Power & Energy Systems, Vol. 62, pp. 617-629.

Ferreira, D., Morabito, R., and Rangel, S. (2009), “Solution approaches for the soft drink integrated production lot sizing and scheduling problem,” European Journal of Operational Research, Vol. 196, No. 2, pp. 697-706.

Gen, M., Cheng, R., and Lin, L. (2008), Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer, New York.

國際替代計量

多站點生產規劃與排程－以電子化學品產業實證研究為例

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