An effective evolutionary algorithm for the multiple container packing problem

作     者：Sang-Moon Soak Sang-Wook Lee Gi-Tae Yeo Moon-Gu Jeon 

作者机构：Information Systems Examination Team Korean Intellectual Property Office (KIPO) Gov. Complex Daejeon Building 4 920 Dunsandong Seogu Daejeon Republic of Korea School of Information and Mechatronics Gwangju Institute of Science and Technology 261 Cheomdan-gwagiro Buk-gu Gwanju Republic of Korea College of Humanities and Social Sciences Woosuk University 490 Hujung-Ri Samrye-Eup Wanju-Gun Jeollabuk-Do Republic of Korea School of Information and Mechatronics Gwangju Institute of Science and Technology 261 Cheomdan-gwagiro Buk-gu Gwanju Republic of Korea c 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2008年第18卷第3期

页      面：337-344页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 070105[理学-运筹学与控制论] 0701[理学-数学] 0702[理学-物理学] 

基　　金：Gwangju Institute of Science and Technology GIST 

主　　题：ALA-EA Evolutionary algorithm Heuristic The multiple container packing problem 

摘      要：This paper focuses on a new optimization problem, which is called The Multiple Container Packing Problem (MCPP) and proposes a new evolutionary approach for it. The proposed evolutionary approach uses Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the bench- mark instances (with the same container capacity) and the modifled benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.