Research on Kruskal Crossover Genetic Algorithm for Multi-Objective Logistics Distribution Path Optimization

Zhang, Yan; Wu, Xing-yi; Kwon, Oh-kyoung

doi:10.14257/ijmue.2015.10.8.36

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…For instance, distance clustering analysis algorithms have been developed to optimize clustering results for customer orders and minimize the unused volume of containers in logistics orders (Zhang et al, 2016). Additionally, research has focused on the application of genetic algorithms for multi-objective logistics distribution path optimization, highlighting the significance of algorithmic approaches in addressing complex supply chain challenges (Zhang et al, 2015).…”

Section: Supply Chain Optimizationmentioning

confidence: 99%

AI in supply chain optimization: A comparative review of USA and African Trends

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Int. J. Sci. Res. Arch.

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The integration of Artificial Intelligence (AI) in supply chain management has emerged as a critical driver of efficiency and competitiveness in global markets. This paper provides a comparative review of AI trends in supply chain optimization between the United States and African regions, shedding light on the unique challenges and opportunities faced by each. In the United States, AI adoption in supply chain optimization has been robust, with a focus on predictive analytics, machine learning, and advanced automation technologies. American companies leverage AI to enhance demand forecasting, optimize inventory management, and streamline logistics processes. The integration of AI-driven solutions has allowed U.S. businesses to achieve higher accuracy in demand predictions, reduce lead times, and minimize operational costs. Furthermore, the use of AI algorithms in route optimization has significantly improved delivery efficiency, leading to enhanced customer satisfaction. Contrastingly, African countries are experiencing a more gradual but steadily increasing adoption of AI in supply chain optimization. Limited access to advanced technology infrastructure, coupled with resource constraints, has posed challenges for African businesses. However, innovative approaches are being explored, such as the use of mobile technologies and cloud-based solutions to overcome infrastructure limitations. AI applications in African supply chains focus on improving visibility, minimizing waste, and ensuring timely delivery. The continent's diverse supply chain landscape, encompassing agriculture, mining, and manufacturing, presents a unique set of challenges that AI aims to address. Both the United States and African nations recognize the potential of AI to transform supply chain management. While the U.S. is at the forefront of AI implementation, Africa is forging ahead with tailored solutions that align with its specific context. Collaboration and knowledge exchange between these regions could pave the way for a globalized approach to AI in supply chain optimization. This review underscores the importance of understanding regional nuances in adopting AI technologies, fostering collaboration for mutual benefit, and advancing the global evolution of AI-driven supply chain management.

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Section: Supply Chain Optimizationmentioning

confidence: 99%

AI in supply chain optimization: A comparative review of USA and African Trends

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Int. J. Sci. Res. Arch.

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“…Reasonable distribution planning can not only maximize the efficiency of resource utilization, better meet customer needs, reduce logistics costs and ensure the timeliness of logistics. Meanwhile, logistics terminals also need to adopt corresponding methods and measures to meet consumers' requirements on distribution methods and efficiency, so as to further improve consumers' satisfaction with online shopping activities [14]. On the basis of fully integrating the existing logistics resources, optimizing the terminal distribution logistics network system can greatly improve logistics efficiency and promote the development of logistics intelligence.…”

Section: Introductionmentioning

confidence: 99%

Optimization of logistics terminal distribution under the background of intelligent logistics

Gao,

Chu,

Cui

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…It is the comprehensive product of modern market economy system, modern science and technology, and modern logistics thought. [1] One of the most important concepts of logistics distribution is timely and accurate supply of goods. Delivery is the last link from order to delivery and also the link to cause time delay.…”

Section: Introductionmentioning

confidence: 99%

Clustering Control of Multi-Objective Problems with Application to E-commerce

Liang

Wang

Shi

2018

IJRC

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In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.

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Research on Kruskal Crossover Genetic Algorithm for Multi-Objective Logistics Distribution Path Optimization

Abstract: Abstract

Cited by 7 publications

References 7 publications

AI in supply chain optimization: A comparative review of USA and African Trends

AI in supply chain optimization: A comparative review of USA and African Trends

Optimization of logistics terminal distribution under the background of intelligent logistics

Clustering Control of Multi-Objective Problems with Application to E-commerce

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