摘要翻译：
本文提出了一种多目标下装箱问题的启发式逼近方法。除了传统的最小化垃圾箱数量的目标之外，每个垃圾箱中元素的异质性也被最小化，这导致了在垃圾箱数量和它们的异质性之间进行权衡的问题的双目标表述。对最优拟合近似算法进行了推广，解决了该问题。实验研究在不同规模的基准实例上进行，范围从100到1000个项目。得到了令人鼓舞的结果，表明了启发式方法对所描述问题的适用性。
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英文标题：
《Bin Packing Under Multiple Objectives - a Heuristic Approximation
  Approach》
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作者：
Martin Josef Geiger
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The article proposes a heuristic approximation approach to the bin packing problem under multiple objectives. In addition to the traditional objective of minimizing the number of bins, the heterogeneousness of the elements in each bin is minimized, leading to a biobjective formulation of the problem with a tradeoff between the number of bins and their heterogeneousness. An extension of the Best-Fit approximation algorithm is presented to solve the problem. Experimental investigations have been carried out on benchmark instances of different size, ranging from 100 to 1000 items. Encouraging results have been obtained, showing the applicability of the heuristic approach to the described problem.
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PDF链接：
https://arxiv.org/pdf/0809.0755