A Study of Optimal System for Multiple-Constraint Multiple-Container Packing Problems

  • Jin-Ling Lin
  • Chir-Ho Chang
  • Jia-Yan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


The proposed research focuses on multiple-container packing problems with considerations of multiple constraints. The space utilization, stability, load bearing, and loading sequence of objects are also considered in order to make results more practicable. Clustering technology and genetic algorithm are combined to solve the proposed problems. At the beginning, clustering algorithm is applied to classify data objects into different groups with varied characteristics, such as dimension of objects, unloading sequence of objects, and capacity of containers. Then, genetic algorithm combines with heuristic rules is used to pack data objects into containers respectively. The stable packing, space utilization, unhindered unloading, and load bear limitation are the major considerations in this stage. A computer system based on the proposed algorithm was developed. Thousands of cases were simulated and analyzed to evaluate the performance of the proposed research and prove the applicability in real world.


Genetic Algorithm Packing Problem Load Bearing Container Cost Heuristic Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin-Ling Lin
    • 1
  • Chir-Ho Chang
    • 2
  • Jia-Yan Yang
    • 3
  1. 1.Department of Information ManagementShih-Hsin UniversityTaiwan
  2. 2.Dept. of Industrial ManagementLungHwa University of Science and TechnologyTaiwan
  3. 3.Department of Industrial Engineering & Management InformationHuafan University, 1Taiwan

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