提升排程品質之實證研究：以某金屬製品觀光工廠為例

A STUDY ON IMPROVING SCHEDULING QUALITY: A CASE STUDY OF A SIGHTSEEING FACTORY OF METAL PRODUCT

10.6220/joq.201908_26(4).0003

黃允成(Yun-Cheng Huang)；丁儀安(Yi-An Ding)；張欣頤(Xin-Yi Chang)

排程 ； 派工法則 ； 多績效指標 ； 隨機亂數演算法 ； 批量生產 ； scheduling ； dispatching rule ； multi-performance indicator ； random number algorithm ； batch production

品質學報

26卷4期（2019 / 08 / 30）

252 - 272

繁體中文

本研究以某金屬製品觀光工廠為例，研發一套智慧型排程系統，其目的在減少延遲訂單數量、縮小生產總完工時間等，使用常見的派工法則當作初解，例如：最短流程時間法、先進先出法、最早交期法，再以隨機亂數演算法進行排程優化求解，同時考慮批量生產、交貨期限、整備時間、延遲懲罰成本等因素，決策者可依需求，在多目標下選擇單一績效指標，例如：總完工時間最小、總延遲時間最短、總延遲訂單數最少、總懲罰成本最小。在循序生產的假設前提下，進行多訂單多產品之生產排程最佳化研究，提升排程績效。結果證實本研究所開發之生產排程系統具有人性化操作介面，平均執行時間皆在17分鐘以內，即可獲得近似最佳解，亦可新增新產品之生產途程，並將最佳排程結果以甘特圖之方式呈現，符合實務之需求。

This study takes a metal product sightseeing factory as an example, and develops a set of intelligent scheduling system; the purposes of this study are decreasing the number of delay orders and minimizing the total production time and so on. This study uses the dispatching rules such as the methods of shortest processing time, first-in/first-out, and early due date as its initial solutions. Then, this study uses random number algorithm (RNA) to optimize the scheduling solutions. Meanwhile, this study also considers the factors of production batch, delivery deadline, set-up time, and the tardiness cost. The decision makers can choose one scheduling performance indicator according to their needs. For instance, the minimum of total completion time, the shortest of total tardiness, the least number of total delay orders, and the smallest of total punishment cost. This study conducts a study of multi-order and multi-product to optimize the production schedule and improve the scheduling performance. The results confirm that the production scheduling system developed by this study has a user interface, and it can get the near optimal solution within 17 minutes or less. Also, the new production route of new products can be added, and the optimal scheduling results would be presented in the form of Gantt chart. In this study, it meets the practical needs and has a good execution performance.

1. Yoo, M.,Gen, M.(2005).Multimedia tasks scheduling using genetic algorithm.Asia Pacific Management Review,10(6),373-380.
   連結：
2. Asadzadeh, L.(2015).A local search genetic algorithm for the job shop scheduling problem with intelligent agents.Computers & Industrial Engineering,85,376-383.
3. Gómez-Martín, C.,Vega-Rodríguez, M. A.,González-Sánchez, J.-L.(2016).Fattened backfilling: an improved strategy for job scheduling in parallel systems.Journal of Parallel and Distributed Computing,97,69-77.
4. Kundakcı, N.,Kulak O.(2016).Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem.Computers & Industrial Engineering,96,31-51.
5. Kurdi, M.(2016).An effective new island model genetic algorithm for job shop scheduling problem.Computers & Operations Research,67,132-142.
6. Lei, D.(2015).Simplified multi-objective genetic algorithms for stochastic job shop scheduling.Applied Soft Computing,11(8),4991-4996.
7. Liu, Z.,Zhang, Y.,Yu, M.,Zhou, X.(2017).Heuristic algorithm for ready-mixed concrete plant scheduling with multiple mixers.Automation in Construction,84,1-13.
8. Nguyen, V.,Bao, H. P.(2016).An efficient solution to the mixed shop scheduling problem using a modified genetic algorithm.Procedia Computer Science,95,475-482.
9. Qing-dao-er-ji, R.,Wang, Y.(2012).A new hybrid genetic algorithm for job shop scheduling problem.Computers & Operations Research,39(10),2291-2299.
10. Roychowdhury, S.,Allen, T. T.,Allen, N. B.(2017).A genetic algorithm with an earliest due date encoding for scheduling automotive stamping operations.Computers & Industrial Engineering,105,201-209.
11. Tang, D.,Dai, M.,Salido, M. A.,Giret, A.(2016).Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization.Computers in Industry,81,82-95.
12. Teekeng, W.,Thammano, A.(2012).Modified genetic algorithm for flexible job-shop scheduling problems.Procedia Computer Science,12,122-128.
13. Wari, E.,Zhu, W.(2016).Multi-week MILP scheduling for an ice cream processing facility.Computers & Chemical Engineering,94(2),141-156.
14. Xie, W.,Hicks, C.,Pongcharoen, P.(2014).A multiple criteria genetic algorithm scheduling tool for production scheduling in the capital goods industry.International Journal of Engineering and Technology Innovation,4(1),18-29.
15. 洪士程,楊逢羿(2010)。序的最佳化於隨機工程生產排程問題之應用。資訊科技國際期刊，4(2)，22-34。
16. 陳柔君(2005)。桃園，元智大學。
17. 黃允成,余政洋(2015)。隨選目標之智慧型排程與產品管理系統之研發。技術學刊，30(3)，241-251。
18. 駱景堯,張俊仁(1998)。遺傳基因演算法在彈性製造系統排程問題之探討。大葉學報，7(1)，79-90。