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首页> 外文会议>2017 16th RoEduNet Conference: Networking in Education and Research >A comprehensive study: Ant Colony Optimization (ACO) for facility layout problem
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A comprehensive study: Ant Colony Optimization (ACO) for facility layout problem

机译：综合研究：针对设施布局问题的蚁群优化（ACO）

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摘要

In context of manufacturing, numerous models are designed to appropriately represent the facility layout problem (FLP) and a variety of optimization methods have been applied to solve these models. The ultimate goal of these methods is to find optimal solutions, In regard to Swarm Intelligence (SI), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are regarded as the most important SI techniques of our time. In this paper, a brief introduction for the so far most promising approaches to facility layout related topics, are provided. The succeeding paper will then illustrate some of those, in more detail. Moreover, we examine ACO modifications and extensions that could contribute to optimization methods in FLP; mostly conform to NP-hard combinatorial problems. future research areas are identified in Construction Site Facility Layout Problems, Multi-Criteria Facility Layout Problems and Dynamic Facility Layout Problems.
机译：在制造方面，设计了许多模型以恰当地表示设施布局问题（FLP），并且已应用各种优化方法来解决这些模型。这些方法的最终目标是找到最佳解决方案。就群智能（SI），蚁群优化（ACO）和粒子群优化（PSO）而言，它们被认为是当今时代最重要的SI技术。在本文中，简要介绍了迄今为止与设施布局相关的主题中最有希望的方法。随后的论文将更详细地说明其中的一些。此外，我们研究了可能对FLP中的优化方法有所帮助的ACO修改和扩展。大部分符合NP-hard组合问题。建筑工地设施布局问题，多标准设施布局问题和动态设施布局问题中确定了未来的研究领域。

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