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On the effectiveness of Monte Carlo simulation and heuristic search for solving large-scale block layout problems

机译:蒙特卡罗模拟和启发式搜索在解决大规模块布局问题上的有效性

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摘要

The effectiveness of Monte Carlo simulation relative to intelligent search strategies for solving block layout problems is investigated. For testing purposes, 810 block layout problems are constructed to span a wide range of problem sizes, material flow variation levels, work centre space requirements distributions, and work centre shape distributions. Contrary to preliminary results reported in earlier studies, greedy search and simulated annealing consistently outperform Monte Carlo Simulation across the full range of test problems and sample sizes. This divergence is explained through a comparison based on probabilistic derivations between the proportion of good solutions sampled by the Monte Carlo method and those found by the heuristic search methods. Conditions for the superiority of either method are identified. Therefore, the current study complements earlier studies by providing analytical arguments and additional experimental evidence for the effectiveness of simple Monte Carlo method and intelligent search heuristics on solving layout problems.
机译:研究了相对于智能搜索策略的蒙特卡洛模拟解决块布局问题的有效性。出于测试目的,构造了810个块布局问题,以涵盖范围广泛的问题大小,物料流变化级别,工作中心空间需求分布和工作中心形状分布。与早期研究报告的初步结果相反,贪婪搜索和模拟退火在所有测试问题和样本量方面均始终优于Monte Carlo模拟。通过基于概率推导的比较来解释这种差异,该概率推导的结果是:通过蒙特卡罗方法采样的良好解决方案的比例与通过启发式搜索方法发现的良好解决方案的比例。确定了两种方法优越性的条件。因此,本研究通过提供分析论点和其他实验证据来补充早期研究,以证明简单的蒙特卡洛方法和智能搜索启发式方法在解决布局问题方面的有效性。

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