利用模糊模拟法解决一般不确定性情景下物流和运输中的 NP-Hard 挑战

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2023-12-16 DOI:10.3390/a16120570
Angel A. Juan, Markus Rabe, Majsa Ammouriova, Javier Panadero, David Peidro, Daniel Riera
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引用次数: 0

摘要

在物流与运输(L&T)领域,本文回顾了利用模拟算法解决随机不确定性条件下的 NP 难优化问题的情况。然后,本文通过引入模糊层来探索模拟概念的扩展,以解决涉及随机和模糊不确定性的复杂优化问题。这种混合方法结合了模拟、元启发式和模糊逻辑,为解决一般不确定性情况下的大规模 NP 难问题提供了一种可行的方法。这些情景通常会在 L&T 优化挑战中遇到,如车辆路由问题或团队定向越野问题等。所提出的方法允许将各种问题组件(包括旅行时间、服务时间、客户需求或电动电池的持续时间)建模为确定项、随机项或模糊项。我们对几个计算实验进行了跨问题分析,以验证模糊模拟法的有效性。作为一种灵活的方法,它允许我们在一般不确定性情景下解决 NP 难度的挑战,模糊模拟法也可应用于 L&T 以外的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics
In the field of logistics and transportation (L&T), this paper reviews the utilization of simheuristic algorithms to address NP-hard optimization problems under stochastic uncertainty. Then, the paper explores an extension of the simheuristics concept by introducing a fuzzy layer to tackle complex optimization problems involving both stochastic and fuzzy uncertainties. The hybrid approach combines simulation, metaheuristics, and fuzzy logic, offering a feasible methodology to solve large-scale NP-hard problems under general uncertainty scenarios. These scenarios are commonly encountered in L&T optimization challenges, such as the vehicle routing problem or the team orienteering problem, among many others. The proposed methodology allows for modeling various problem components—including travel times, service times, customers’ demands, or the duration of electric batteries—as deterministic, stochastic, or fuzzy items. A cross-problem analysis of several computational experiments is conducted to validate the effectiveness of the fuzzy simheuristic methodology. Being a flexible methodology that allows us to tackle NP-hard challenges under general uncertainty scenarios, fuzzy simheuristics can also be applied in fields other than L&T.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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