提出了一种多周期枢纽定位与分配模型,并与多目标帝国主义竞争算法进行了比较

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tzu-Chia Chen, Iskandar Muda, Rabia Salman, Baydaa Abed Hussein, Khusniddin Fakhriddinovich Uktamov, Mohammed Yousif Oudah Al-Muttar
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引用次数: 0

摘要

近年来,由于人们对环境问题的反思,空气污染受到了广泛关注。因此,枢纽位置问题(HLP)寻求枢纽设施的最优位置,并为它们分配点以满足源-目的对之间的需求。因此,在本研究中,回顾了枢纽网络中与位置和分配相关的决策,并提出了一个多目标模型,用于在规划范围内的不同时间段定位和分配能力建设设施。本研究模型的目标函数为成本最小化、减少燃料消耗以减少空气污染、以及就业机会最大化。为了求解给定的模型,采用了通用代数建模系统(GAMS)和创新的算法。结果提出了一个全覆盖HLP的多目标可持续模型,并利用MOICA(多目标帝国主义竞争算法)和GAMS在每个规划范围内提供了一个运输成本、燃料消耗和温室气体排放最低、就业机会最大的枢纽网络。本研究还通过比较基于某些指标的元启发式算法得出的QM、MID、SM和NSP指标对所提出算法的性能进行了评估,证明了MOICA在所有情况下的效益和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm
Abstract Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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