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Jianfeng Zhao and Wenghua Zeng; Simple Implementation of Parallel Genetic Algorithms Based on Cloud Computing INFORMATION JOURNAL £¬Vol.15, No.11(A), November, 2012 ¿É·ñ°Ñ¼ìË÷Ò³ÐÅÏ¢Õ³Ìù¹ýÀ´£¬Ö÷Òª¿´¼ìË÷·½Ê½ÊÇÆÚ¿¯ÂÛÎÄ£¬»¹ÊÇ»áÒéÂÛÎÄ£¿ |
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2Â¥2012-12-06 08:18:50
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gaoyanyan3
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gaoyanyan3
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dreamsprit
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gaoyanyan3: ½ð±Ò+5, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2012-12-06 11:04:25
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Study of Strategy Optimization for Facility Location of Reverse Logistic Network Based on Artificial Immune System ×÷Õß: Huang, QA (Huang Qian)1; Ren, ZG (Ren Zhigang)2; Li, SZ (Li Shuzhuo)3; Jiang, H (Jiang Hong)4; Xu, D (Xu Dong)1 À´Ô´³ö°æÎï: INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL ¾í: 13 ÆÚ: 3 Ò³: 523-535 ³ö°æÄê: MAY 2010 ±»ÒýƵ´Î: 1 (À´×Ô Web of Science) ÒýÓõIJο¼ÎÄÏ×: 19 [ ²é¿´ Related Records ] ÒýÖ¤¹ØÏµÍ¼ ÕªÒª: The facility location of the reverse logistic center, a realistic and challenging issue in the reverse logistic management, has been sufficiently studied in many fields such as management science and operational research. However, only a few studies have been done on the modeling and strategy optimization of the facility location of the reverse logistic center. This paper introduces a five-principal model of artificial immune optimization algorithm to the facility location strategy of the reverse logistic network, and systematically explores basic artificial immune operators, basic artificial immune rules, and convergence of the basic artificial immune algorithm. The model not only provides a novel solution to the facility location strategy optimization of the reverse logistic network, but also expands the applied field of artificial immune system and enriches the current artificial immune system models. The simulation studies show that the artificial immune optimization algorithm converges faster, obtains more precise solutions, and is more capable of preserving the diversity of the antibody group than the small-world genetic algorithm. Èë²ØºÅ: WOS:000281836000001 ÎÄÏ×ÀàÐÍ: Article ÓïÖÖ: English ×÷Õ߹ؼü´Ê: Reverse Logistic; Artificial Immune System; Artificial Immune Optimization KeyWords Plus: MODELS ͨѶ×÷ÕßµØÖ·: Huang, QA (ͨѶ×÷Õß),Xian Phys Educ Univ, Xian 710068, Shannxi, Peoples R China. µØÖ·: 1. Xian Phys Educ Univ, Xian 710068, Shannxi, Peoples R China 2. Xi An Jiao Tong Univ, Xian 710041, Shannxi, Peoples R China 3. Xi An Jiao Tong Univ, Sch Publ Policy & Adm, Xian 710068, Shannxi, Peoples R China 4. Xian Univ Architecture & Technol, Xian 710055, Shaanxi, Peoples R China ³ö°æÉÌ: INT INFORMATION INST, FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN Web of Science Àà±ð: Engineering, Multidisciplinary Ñо¿·½Ïò: Engineering IDS ºÅ: 650GA ISSN: 1343-4500 |
9Â¥2012-12-06 10:55:50













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