Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Yu, Wenfang; Hou, Guisheng; Xia, Pengcheng; Li, Jingjing

doi:10.17559/tv-20190805123158

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…According to the above calculation formula, the load deviation rate takes a value in the range of 0 to 1. The smaller the value, the more balanced the load distribution of the cloud computing system, and the better the performance of the current system [ 16 ].…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm

2022

Computational Intelligence and Neuroscience

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The networking scale and traffic have exploded. At the same time, the rapid development of virtualization and cloud computing technologies not only poses a considerable challenge to the endurance of the network, but also causes more and more problems to the traditional network architecture with IP as the core. Cloud computing is a supercomputing model based on the Internet. With the rapid growth of network access and data traffic, the processing power and computing intensity will also increase, and a single server cannot afford the increase in business. In order to reduce network pressure and improve computing efficiency, load balancing for network computing is particularly important. This paper uses ant colony algorithm to design cloud computing load balance. The ant colony algorithm runs in the controller. According to the real-time network load situation provided by the controller, it calculates the link with the smallest load and provides a dynamic data stream forwarding strategy. The result of the experiments shows that the load-balanced ACO optimized technique can significantly provide an improved computational response. In the ACO algorithm, the average response time is about 30% lower than that in other algorithms. This shows that the use of the ant colony algorithm achieves a good optimization effect.

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Section: Ant Colony Algorithmmentioning

confidence: 99%

Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm

2022

Computational Intelligence and Neuroscience

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“…Traditional data mining techniques used for the analysis of flat vectorial data are inefficient for handling large-scale data with inherent relational dependencies, weights, edge directions, and heterogeneity between system elements [18]. Given its proximity to end users, low latency, and other advantages, EC has elicited considerable interest in the research community [19]. The smaller the value, the more balanced the load distribution of a cloud computing system, and the better the performance of the current system.…”

Section: Related Workmentioning

confidence: 99%

Design of A new Algorithm by Using Standard Deviation Techniques in Multi Edge Computing with IoT Application

2023

KSII TIIS

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The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT.In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

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“…This work positively influences the efficiency of the organizations' ability to manage inventory in SC. The work recommends using AI to manage inventory to improve SC [32]. X. Zhang et al developed ACO algorithm with MO using two different colonies to reduce the cost of the goods in the SC.…”

Section: R Sun Et Al Describes the Application Of Ant Colony With Mo ...mentioning

confidence: 99%

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Hassouna¹,

El-Henawy²,

Haggag³

2022

IJACSA

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Supply chain management seeks to solve the complex problems of transporting goods from the suppliers to the end customers. Improving the differentiation between different paths to reduce costs and time may require smart systems. This paper proposes two new algorithms for determining, with Multi-Objective Optimization, the least cost and the most appropriate path between two nodes. First: Ant colony optimization (ACO) algorithm, working alongside with Multi Objective Optimization (MOO), is adopted to determine the shortest path and time between two nodes to reach with the least cost. Multi-Objective intelligent Ant Colony (MOIAC) algorithm improves supply chain management to achieve the optimal and the most appropriate solutions. Second: Particle Swarm Optimization (PSO) algorithm, also working alongside MOO, is adopted to determine the least cost, time, and shortest path. Multi Optimization Intelligent Particle Swarm (MOIPS) algorithm improves supply chain management by determining the shortest path with the least cost. These two proposed algorithms seek the optimal solution by MOO using a JAVA Program. The experimental results show the excellence of the first algorithm in determining the optimal and the most appropriate path while getting throw risks inherent in transporting goods. It also demonstrates excellence in transporting goods in the shortest possible time and with the least cost. The second algorithm also shows excellence in transporting goods with the least possible cost via the shortest path and in the shortest time.

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Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Cited by 10 publications

References 38 publications

Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm

Cloud Computing Load Balancing Mechanism Taking into Account Load Balancing Ant Colony Optimization Algorithm

Design of A new Algorithm by Using Standard Deviation Techniques in Multi Edge Computing with IoT Application

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

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