第四章、 範例測詴
5.2 建議
(1)本研究方法是採用隨機亂數之方式產生起始粒子，因此起始粒子之品質 不一，若採用產生起始解之方式來產生，像是最遠插入法(以最遠之節點開始插 入)、最近插入法(以最近之節點開始插入)或是根據顧客需求之多寡插入法等等，
起始解之品質可能較隨機亂數所產生之起始解來的好，在相同的迭代數下，其較 易能獲得品質不差的解。
(2)本研究在測詴參數時，對於不同之參數，皆事先提出欲測詴之參數組，
若能待測完某一參數後，再提出新的參數組，其在測詴上亦較精確。舉例來說，
本研究在測詴完粒子數後決定用 40 作為本研究之粒子數，若能在測詴迭代數與
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學習因子時，粒子數皆設定為 40，對於本研究而言，將比粒子數設為 30 來的精 確。
(3)本研究方法僅透過 2-OPT 進行交換法改善，改善之效率有限，若未來能 配合更多交換法或是結合其他啟發式解法進行改善，求解品質將可能獲得提升。
(4)本研究在 MDPDP 的限制上包含了場站之車輛數相同之限制以及車輛需 回到同個場站之限制，未來可將此一限制剔除，讓問題更接近實務。
(5)本研究之目標式為最小總旅行成本，未來可將總使用車輛數一併考慮進 去，較符合現今之情況。
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六、參考文獻
[1] Bigang, H. and Changshi, L., “A Two-Stage Hybrid Heuristic for Vehicle Routing Problem with Pickups and Deliveries”, International Conference on Information Management, Innovation Management and Industrial Engineering, Vol. 1, pp. 92-97, Xi’an ,DEC26-27, 2009.
[2] Catay, B., “A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery”, EXPERT SYSREMS WITH
APPLICATION, Vol.37, No. 10, pp. 6809-6817, OCT 2010.
[3] Cortes, C.E., Saez, D., Nunez, A., et al., “Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem”, Transportation Science, Vol. 43, No. 1, pp.
27-42, 2009.
[4] Goksal, F.P., Altiparmak, F., Karaoglan, I., “A hybrid particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery”, 40th International Conference on Computers and Industrial Engineering(CIE), pp. 1-6, Awaji, JUL 25-28, 2010.
[5] Irnich, S., “A multi-depot pickup and delivery problem with a single hub and heterogeneous vehicles”, European Journal of Operational Research, Vol. 122, No. 2, pp. 30-328, APR 16 2002.
[6] Kennedy, J., Eberhart, R.C., “Particle Swarm Optimization”, 1995 IEEE
International Conference on Neural Networks, pp. 1942-1948, Univ W Austraia Perth Australia, 1995.
[7] Kennedy, J., Eberhart, R.C., “Swarm intelligence”, Morgan Kaufmann division of Academin Press, 2001.
[8] Laporte, G., Nobert, Y., Taillefer, S., “Solving a Family of Multi-depot
Vehicle-Routing and Location-Routing Problems”, Transportation Science, Vol. 22, No. 3, pp. 161-172, AUG 1998.
[9] Lee, C.G., Epelman, M.A., White, C.C., et al., “A shortest path approach to the multiple-vehicle routing problem with split pick-ups”, Transportation Research Part B-Methodological, Vol. 40, No. 4, pp. 265-284, MAY 2006.
[10] Nagy, G., Salhi, S., “Heuristic Algorithms for Single and Multiple Depot Vehicle Routing Problems with Pickups and Deliveries”, European Journal of Operation Research, Vol. 162, No. 1, pp. 126-141, APR 1 2005.
48
[11] Parragh, S.N., Doerner, K.F. and Hartl, R.F., “A survey on pickup and delivery problems Part I: Transportation between pickup and delivery locations”, Journal fürBetriebswirtschaft, Vol 58, No.1, pp. 21-51, April 2008.
[12] Parragh, S.N., Doerner, K.F. and Hartl, R.F., “A survey on pickup and delivery problems Part II: Transportation between pickup and delivery locations”, Journal fürBetriebswirtschaft, Vol 58, No.2, pp. 81-117, June 2008.
[13] Pepin, A.S., Desaulniers, G., Hertz, A., et al., “A comparison of five heuristics for the multiple depot vehicle scheduling problem”, Journal of Scheduling, Vol. 12, No. 1, pp. 17-30, 2009.
[14] Renaud, J., Laporte, G., Boctor, F.F., “A tabu search for the multi-depot vehicle routing problem”, COMPUTERS & OPERATIONS REASEARCH, Vol. 23, No. 3, pp. 229-235, MAR 1996.
[15] Ropke, S. and Pisinger, D., “An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows”, Transportation Science, Vol. 40, No. 4, pp. 455-472, NOV 2006.
[16] Savelsbergh, M. W. P. and Solomon, M., “The General Pickup and Delivery Problem”, Transportation Science, Vol. 29, No. 1, pp. 17-29, 1995.
[17] Shen, H., Zhu, Y.L., Liu, T., et al., “Particle Swarm Optimization in Solving Vehicle Routing Problem”, 2nd International Conference on Intelligent Computation Technology and Automation, pp. 287-291, Changsha Peoples R China, 2009.
[18] Shi, Y. and Eberhart, R.C., “Empirical study of particle swarm optimization”, Proceedings of the 1999 congress on Evolutionary Computation, Vol. 3, Washington DC, 1999.
[19] Sombuntham, P. and Kachitvichayanukul, V., “A Particle Swarm Optimization Algorithm Multi-depot Vehicle Routing Problem Pickup and Delivery Requests”, International MultiConference of Engineering and Computer Scientists 2010, pp.
1998-2003, Kowloon, Hong Kong China, 2010.
[20] Song, M.P., Gu, G.C., “Research on Particle Swarm Optimization: A Review”, International Conference on Machine Learning and Cybernetics, pp. 2236-2241, Shanghai Peoples R China, 2004.
[21] Thangiah, S.R., Salhi, S., “Genetic clustering: An adaptive heuristic for the multidepot vehicle routing problem”, APPLIED ARTIFICIAL INTELLIGENCE, Vol.
15, No. 4, pp. 361-383, APR 2001.
49
[22] Toth, P. and Vigo, D., The Vehicle Routing Problem, SIAM, Philadelphia, 2002.
[23] Trelea, IC., “The particle swarm optimization algorithm: convergence analysis and parameter selection”, INFORMATION PROCESSING LETTERS, Vol. 85, No. 6, pp. 317-325, MAR 2003.
[24] Wang, S.X., Wang, L.Z., Wang, D.W., “Particle Swarm Optimization for Multi-Depots Single Vehicle Routing Problem”, 20th Chinese Control and Decision Conference, pp. 4659-4661,Yantai Peoples R China, 2008.
[25] Wang, S.X., Wang, L.Z., Yuan, H.L., et al., “study on Multi-Depots Vehicle Scheduling Problem and Its Two-Phase Particle Swarm Optimization”, 5th
International Conference on Intelligent Computing, pp. 748-756, Ulsan South Korea, 2009.
[26] Wang, X.B., Sun, J.Y., and Ren, C.Y., “Study on hybrid heuristic algorithm for multi-depot vehicle routing problem with hybrid picking-delivery strategy ”, 2009 Eighth International Conference on Machine Learning and Cybernetics (ICMLC), pp.
1451-1456, Baoding China, 2009.
[27] Wen, L., Meng, F.H., “An Improved PSO Multi-Depot Vehicle Routing Problem with Time Windows”, Pacific-Asia Workshop on Computation Intelligence and Industrial Application, pp. 820-824, Wuhan Peoples R China, 2008.
[28] Wu, B., Wang, W.L., Zhao, Y.W., et al., “A Novel Real Number Encoding Method of Particle Swarm Optimization for Vehicle Routing Problem”, 6th World Congress on Intelligent Control and Automation, pp.
3271-3275, Dalian Peoples R China, JUN 21-23 2006.
[29] Z. Li-Ping, Y. Huan-jun, and H. Shang-Xu, “Optimal choice of parameters for the particle swarm optimization”, Journal of Zhejiang University: science, Vol. 6, pp.
528-534, 2005.
[30] Zhang, R., Wu, C., “A Divide-and-Conquer Strategy with Particle Swarm Optimization for the Job Shop Scheduling Problem”, Vol. 42, No. 7, pp. 641-670, 2010.
[31] 李旺蒼,「以粒子群最佳化為基礎之混合式全域搜尋演算法求解含凹型節線 成本最小成本轉運問題之研究」，國立中央大學土木工程學系碩士論文，民國九 十五年六月。
[32] 林至康,「汽車客運多場站車輛排程問題之研究」，國立交通大學運輸科技 與管理學系博士論文，民國九十八年三月。
50
[33] 黃瓊萱, 「多場站具時窗限制並配合外包策略之車輛路線問題探討」，國 立東華大學全球運籌管理系碩士論文，民國九十六年六月。
[34] 許哲斌, 「在需求變動下具有軟時窗限制之同時收、送貨車輛途程問題」，
國立雲林科技大學工業工程與管理學系碩士論文，民國九十七年六月。
[35] 葉珮婷,「應用變數產生法求解有時間窗限制的收送貨問題」，國立交通大 學運輸科技與管理學系碩士論文，民國九十八年七月。
[36] 業思緯, 「應用粒子群最佳化演算法於多目標存貨分類之研究」，元智大 學工業工程與管理系碩士論文，民國九十三年六月。
[37] 萬芸萍, 「國際企業供應鏈營運面匯率避險之研究」，國立成功大學交通 管理科學系碩士論文，民國九十一年六月。