• 沒有找到結果。

In this research, a sPSO algorithm is proposed to approach a combinatorial optimal solution to the vehicle routing problem with Cross-docking system. The primary objectives of this work included the integration of a Cross-docking operation and optimal vehicle routing schedule into a supply chain optimization design. A significant development involves on the synchronization between upstream suppliers and downstream retailers, where both sides of the supply chain are simultaneously considered to collaborate the physical flow of goods in the inbound and outbound processes. With the establishment of this model, the desirable scenario of no customer order delay and no inventory stocking in the central warehouse can be practically achieved.

The computational results show that the sPSO model is effective at solving the VRPCD. The effectiveness of the method comes from the two-phase mechanism. In the initial route-generating phase, a high-quality initial solution is generated by the sweep method before inputting to the routes optimization phase with the particle optimizer functions. The combination of the two phases ensures that the sPSO method yieldd quality solutions.

18

Sixty benchmark problems were used to investigate the applicability of the proposed particle optimizer. The experimental results showed that the sPSO method is able to produce significant improvements over the GA, surpassing it with an improvement rate of 0.75% for the total average cost generated. In addition, the sPSO method is able to find a better solution than the GA method in 54 instances out of the 60 benchmark problems. Moreover, the sPSO method converges faster to a high quality solution than the GA in 5000 iterations.

REFERENCES

Apte, U.M., Viswanathan S. (2000). Effective cross docking for improving distribution efficiencies. International Journal of Logistics Research and Applications, 3, 291–302.

Bell, J.E., McMullen, P.R. (2004). Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics, 18(1), 41–48.

Celik, M., Kahraman, C., Cebi, S., Er, I.D. (2009). Fuzzy axiomatic design-based performance evaluation model for docking facilities in shipbuilding industry: The case of Turkish shipyards. Expert Systems With Applications, 36(1), 599–615.

Hu, X., Shi, Y., & Eberhart, R.C. (2004). Recent Advances in Particles Swarm.

Proceedings of IEEE congress on evolutionary computation, 1, 90–97.

Kennedy, J., Eberhart, R.C. (1995). Particle Swarm Optimization, Proc. IEEE International Conference on Neural Networks; IEEE Service Center, Piscataway, NJ, 4, 1942–1948.

LaLonde, B.J., Zinszer, P.H. (1976). Customer Service: Meaning and Measurement.

National Council of Physical Distribution Management, USA.

Lee, Y.H., Jung, J.W., & Lee, K.M. (2006). Vehicle routing scheduling for Cross-docking in the supply chain. Computers & Industrial Engineering, 51(2), 247–256.

Lai, M., Cao E. (2010). An improved differential evolution algorithm for vehicle routing problem with simultaneous pickups and deliveries and time windows.

Engineering Applications of Artificial Intelligence, 23, 188–195.

Marinakis, Y., Marinaki, M. (2010). A hybrid genetic – Particle Swarm Optimization Algorithm for the vehicle routing problem. Expert Systems With Applications, 37(2), 1446–1455.

MirHassani, S.A., Abolghasemi, N. (2011). A particle swarm optimization algorithm for open vehicle routing problem. Expert Systems With Applications, 38(9), 11547–11551.

Mosheiov, G. (1998). Vehicle Routing With Pick-up and Delivery: Tour Partitioning

19

Heuristics. Computers & Industrial Engineering, 34(3), 669–684.

Rohrer, M. (1995). Simulation and Cross Docking. Proceeding of the 1995 Winter Simulation Conference, 846–849.

Shi, Y., Eberhart, R.C. (1998). A Modified Particle Swarm Optimizer. Proceedings of the IEEE Congress on Evolutionary Computation, 69–73.

Shi, Y., Eberhart, R.C. (1998). Parameter Selection in Particle Swarm Optimization.

1998 Annual Conference on Evolutionary Programming, 591–600.

Song, S.H., Sung, C.S. (2003). Integrated service network design for a Cross-docking supply chain network. Journal of the Operational Research Society, 54, 1283–

1295.

Toth, P., Vigo, D. (2001). The vehicle routing problem. Monographs on Discrete Mathematics and Applications, Philadelphia, PA: SIAM.

Yu W., Egbelu P.J. (2006). Scheduling of inbound and outbound trucks in cross docking system with temporary storage. European Journal of Operational Research, 177, 377–396.

Zachariadis, E.E., Tarantilis, C.D., Kiranoudis, C.T. (2010). An adaptive memory methodology for the vehicle routing problem with simultaneous pick-ups and deliveries. European Journal of Operational Research, 202, 401–411.

20

國科會補助專題研究計畫成果報告自評表 國科會補助專題研究計畫成果報告自評表 國科會補助專題研究計畫成果報告自評表 國科會補助專題研究計畫成果報告自評表

請就研究內容與原計畫相符程度、達成預期目標情況、研究成果之學術或應用價 值(簡要敘述成果所代表之意義、價值、影響或進一步發展之可能性)、是否適 合在學術期刊發表或申請專利、主要發現或其他有關價值等,作一綜合評估。

1. 請就研究內容與原計畫相符程度、達成預期目標情況做一綜合評估

■ 達成目標

□ 未達成目標(請說明,以 100 字為限)

□ 實驗失敗

□ 因故實驗中斷

□ 其他原因 說明:

2. 研究成果在學術期刊發表或申請專利等情形:

論文:□已發表 ■■■■未發表之文稿 □撰寫中 □無 專利:□已獲得 □申請中 ■■■■無

技轉:□已技轉 □洽談中 ■■■■無 其他:(以 100 字為限)

已投稿 SCI 期刊審查中。

3. 請依學術成就、技術創新、社會影響等方面,評估研究成果之學術或應用 價值(簡要敘述成果所代表之意義、價值、影響或進一步發展之可能性)(以 500 字為限)

本年度專題研究計畫延續前一年之專題研究計畫主題,在相同研究主題之 下,改良了前一年度所提最佳化演算法之效率,有效提升最佳化演算法在 物流問題(供應鏈管理領域)中之求解強韌度與搜尋最佳解的能力。經由 這兩年之研究,我們將可站在此基礎之上,未來將提出更聰明的智慧型演 算法,在車輛運途問題中,提出更有效率的運輸架構,對於未來立足台灣、

放眼中國,建立全球運籌網路的企業全球競爭力,做出相當的貢獻。

國科會補助計畫衍生研發成果推廣資料表

日期:2012/09/30

國科會補助計畫

計畫名稱: 改良式粒子群最佳化求解具接駁式轉運之車輛運途問題研究 計畫主持人: 羅士哲

計畫編號: 100-2410-H-011-029- 學門領域: 交通運輸

無研發成果推廣資料

100 年度專題研究計畫研究成果彙整表

其他成果 (無法以量化表達之成 果如辦理學術活動、獲 得獎項、重要國際合 作、研究成果國際影響 力及其他協助產業技 術發展之具體效益事 項等,請以文字敘述填 列。)

國際期刊論文投稿中

成果項目 量化 名稱或內容性質簡述

測驗工具(含質性與量性) 0

課程/模組 0

電腦及網路系統或工具 0

教材 0

舉辦之活動/競賽 0

研討會/工作坊 0

電子報、網站 0

目 計畫成果推廣之參與(閱聽)人數 0

國科會補助專題研究計畫成果報告自評表

請就研究內容與原計畫相符程度、達成預期目標情況、研究成果之學術或應用價 值(簡要敘述成果所代表之意義、價值、影響或進一步發展之可能性)、是否適 合在學術期刊發表或申請專利、主要發現或其他有關價值等,作一綜合評估。

1. 請就研究內容與原計畫相符程度、達成預期目標情況作一綜合評估

■達成目標

□未達成目標(請說明,以 100 字為限)

□實驗失敗

□因故實驗中斷

□其他原因 說明:

2. 研究成果在學術期刊發表或申請專利等情形:

論文:□已發表 ■未發表之文稿 □撰寫中 □無 專利:□已獲得 □申請中 ■無

技轉:□已技轉 □洽談中 ■無 其他:(以 100 字為限)

已投稿 SCI 期刊審查中。

3. 請依學術成就、技術創新、社會影響等方面,評估研究成果之學術或應用價 值(簡要敘述成果所代表之意義、價值、影響或進一步發展之可能性)(以 500 字為限)

本年度專題研究計畫延續前一年之專題研究計畫主題,在相同研究主題之下,改良了前一 年度所提最佳化演算法之效率,有效提升最佳化演算法在物流問題(供應鏈管理領域)中 之求解強韌度與搜尋最佳解的能力。經由這兩年之研究,我們將可站在此基礎之上,未來 將提出更聰明的智慧型演算法,在車輛運途問題中,提出更有效率的運輸架構,對於未來 立足台灣、放眼中國,建立全球運籌網路的企業全球競爭力,做出相當的貢獻。

相關文件