The Impacts of the Cross-Chain Collaboration Center Model on Transportation Performance: A Case Study of a Bulk Transportation Network in Thailand

Maneengam, Apichit; Udomsakdigool, Apinanthana

doi:10.1109/access.2022.3180050

Cited by 2 publications

(6 citation statements)

References 48 publications

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“…Although it is different from the traditional degree index in [5,7,8,10], it can help us evaluate the network's robustness. The higher the redundancy rate R Σ (t) is, the stronger the connectivity of the logistics network is, meaning more connections between different nodes and a stronger robustness.…”

Section: The Robustness Measurement Of Logistics Networkmentioning

confidence: 96%

“…Hence, the robustness of a logistics network can be measured by the space dimension and time dimension. In the space dimension, graph theory has been widely used to describe the interactions and interdependencies between topology nodes and edges, which can directly evaluate the importance of nodes or edges and is useful to measure the logistics network's robustness [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on this model, the expected traffic loss originating from a disruption can be calculated by the network's links and be used to depict the network's vulnerability cost. Most of the logistics network is a centralized network around one or more central nodes, so the degree method can help us find the vulnerable nodes quickly [8]. Furthermore, the logistics connectivity and the network's complexity will be increased in reverse logistics, which is ubiquitous in the steel industry [17], plastic wastes recycling [18], end-of-life vehicles [19] and electronic products [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…where d ij is the length of the edge v ij , that is d ij = v ij . The node degree can directly give us a reference for the node connectivity, and the definition is as follows [5,7,8,10].…”

Section: The Robustness Measurement Of Logistics Networkmentioning

confidence: 99%

“…Hence, logistics business sustainability should be studied in case of a topology robustness, such as disjoint paths and survivable networks, or greatly increased efforts will be put into topology management, contingency planning, traffic optimization and so on. Kulkarni et al [7] proposed a hyperconnected parcel delivery network design under the disruption risks, while Maneengam et al [8] considered the impacts of the cross-chain collaboration center model on the transportation performance and made a case study of a bulk transportation network in Thailand. The robustness of the logistics network will decrease the lack of reliability and bring an additional cost for users.…”

Section: Introductionmentioning

confidence: 99%

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Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

et al. 2022

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The robust optimization of logistics networks can improve the ability to provide sustainable service and business sustainability after uncertain disruptions. The existing works on the robust design of logistics networks insisted that it is very difficult to build a robust network topology, and this kind of optimization problem is an NP-hard problem that cannot be easily solved. In nature, Physarum often needs to build a robust and efficient topological network to complete the foraging process. Recently, some researchers used Physarum to build a robust transportation network in professional biological laboratories and received a good performance. Inspired by the foraging behavior of natural Physarum, we proposed a novel artificial Physarum swarm system to optimize the logistics network robustness just on a personal computer. In our study, first, the robustness optimization problem of a logistics network is described as a topology optimization model based on graph theory, and four robustness indicators are proposed to build a multi-objective robustness function of logistics network topology, including the relative robustness, the betweenness robustness, the edge robustness and the closeness robustness. Second, an artificial Physarum swarm system is developed to simulate the foraging behavior of a natural Physarum swarm to solve this kind of complex robust optimization problem. The proposed artificial Physarum swarm system can search for optimal solutions by expansion and contraction operations and the exchange of information with each other through a self-learning experience and neighbor-learning experiences. The plasmodium of Physarum forms the edges, and the external food sources simulate the logistics nodes. Third, an experimental example is designed on the basis of Mexico City to verify the proposed method, and the results reveal that the artificial Physarum swarm system can help us effectively improve the logistics network robustness under disruptions and receive a better performance than natural Physarum. The article may be helpful for both theory and practice to explore the robust optimization in logistics operation and provide engineers with an opportunity to resist logistics disruptions and risk loss by a novel artificial intelligence tool.

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Section: The Robustness Measurement Of Logistics Networkmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…where d ij is the length of the edge v ij , that is d ij = v ij . The node degree can directly give us a reference for the node connectivity, and the definition is as follows [5,7,8,10].…”

Section: The Robustness Measurement Of Logistics Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

et al. 2022

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Oligopolistic Markets Employing an Intelligent Physarum Solution for Supply Chain Networks

Gupta,

Usman,

Thethi

et al. 2024

Utilization of AI Technology in Supply Chain Management

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This chapter delves into a multifaceted journey. The authors commenced with an introduction that provided a brief overview of the topic, emphasizing the importance of addressing profit maximization in oligopolistic markets. Additionally, they introduced the innovative concept of the Physarum solver and its potential application in the realm of supply chain networks. Subsequently, they embarked on an in-depth analysis of oligopolistic markets. This analysis entailed an examination of key players, market structures, and the dynamics of competition in these markets.

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The Impacts of the Cross-Chain Collaboration Center Model on Transportation Performance: A Case Study of a Bulk Transportation Network in Thailand

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 2 publications

References 48 publications

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

Oligopolistic Markets Employing an Intelligent Physarum Solution for Supply Chain Networks

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