双目标双层走廊分配问题的路径重链接策略

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nicolás R. Uribe, Alberto Herrán, J. Manuel Colmenar
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引用次数: 0

摘要

双目标双层走廊分配问题是一个运筹学问题,其目标是在两层楼中有两条走廊的布局中找到最佳的设施安排,以最大限度地降低材料处理成本和走廊长度。在本文中,我们提出了一种基于路径重联策略组合的新方法。为此,我们提出了两种贪婪算法,以生成一组非主导解的初始集。在第一阶段,我们采用内部路径重链接,目的是改进这组解决方案;在第二阶段,我们采用外部路径重链接,以获得第一阶段无法获得的解决方案。我们的大量实验分析表明,在自动优化参数后,我们的方法完全超越了之前的基准测试,花费的计算时间也更短。此外,我们还提供了新实例的详细结果,包括多目标问题的标准指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Path relinking strategies for the bi-objective double floor corridor allocation problem
The bi-objective Double Floor Corridor Allocation Problem is an operational research problem with the goal of finding the best arrangement of facilities in a layout with two corridors located in two floors, in order to minimize the material handling costs and the corridor length. In this paper, we present a novel approach based on a combination of Path Relinking strategies. To this aim, we propose two greedy algorithms to produce an initial set of non-dominated solutions. In a first stage, we apply an Interior Path Relinking with the aim of improving this set and, in the second stage, apply an Exterior Path Relinking to reach solutions that are unreachable in the first stage. Our extensive experimental analysis shows that our method, after automatic parameter optimization, completely dominates the previous benchmarks, spending shorter computation times. In addition, we provide detailed results for the new instances, including standard metrics for multi-objective problems.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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