摘要翻译：
本文探讨了军事物流系统中网络拓扑结构与时间临界性之间的相互作用。这项工作（和以前的工作）的总目标是评估陆路运输需求，或者更具体地说，如何设计适当的军事总务车辆车队，负责向分散在行动区的军事单位提供补给和再补给。本文的重点在于更好地理解当目前具有固定运输特性的陆军车辆被新一代可针对特定任务配置的模块化车辆所取代时，后勤环境是如何变化的。实验工作是在一个完善的战略规划仿真环境中进行的，该环境包括一个用于自动采样补给和再补给任务的情景生成引擎和一个用于解决特定调度和路由问题的多目标元启发式搜索算法（即进化算法）。本文中提出的结果允许更好地理解模块化车队如何（以及在什么条件下）提供优于当前实施的系统的优势。
---
英文标题：
《Network Topology and Time Criticality Effects in the Modularised Fleet
  Mix Problem》
---
作者：
James M. Whitacre, Axel Bender, Stephen Baker, Qi Fan, Ruhul A.
  Sarker, Hussein Abbass
---
最新提交年份：
2009
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
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。
--

---
英文摘要：
  In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles that are tasked with the supply and re-supply of military units dispersed in an area of operation. The particular focus of this paper is to gain a better understanding of how the logistics environment changes when current Army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task-specifically. The experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re-supply missions and a multi-objective meta-heuristic search algorithm (i.e. Evolutionary Algorithm) for solving the particular scheduling and routing problems. The results presented in this paper allow for a better understanding of how (and under what conditions) a modularised vehicle fleet can provide advantages over the currently implemented system.
---
PDF链接：
https://arxiv.org/pdf/0907.0597