Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Xiang, Xu; Chang, Wei-hong; Liu, Jing

doi:10.1155/2017/4587098

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…He proposed a genetic simulated annealing mixed intelligence algorithm to solve it. Numerical examples show that the method has strong robustness and convergence, and it can realize the rationalization and optimization of resource allocation in collaborative logistics network [ 3 ]. Aiming at the common distribution methods of urban logistics in the e-commerce environment, Yang considers the dynamic uncertainty of the urban road network based on the optimization of the distribution path [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

Optimal Design of International Trade Logistics Based on Internet of Things Technology

Wang

2022

Computational Intelligence and Neuroscience

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With the global industrial integration, trade exchanges between countries are becoming more and more frequent and the competition for related international trade logistics is becoming more and more intense. This paper aims to study the optimization design of international trade logistics based on Internet of Things technology. For this reason, this paper proposes an RFID method, through which it is possible to master a journey in the actual international trade logistics process. And it optimizes and improves its related paths, shortens the time delayed on the road, and achieves the purpose of mentioning the carrying capacity. And for the actual use effect, the relevant experiments and analysis parts are designed to simulate it. The experimental results of this paper show that the improved and optimized international trade logistics shortens the carrying time by 28.6% and increases the carrying capacity by 34.2%, which greatly promotes the development of the relevant international trade logistics industry.

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Section: Related Workmentioning

confidence: 99%

Optimal Design of International Trade Logistics Based on Internet of Things Technology

Wang

2022

Computational Intelligence and Neuroscience

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“…At Amazon, for example, ML is used to select the ideal packaging size for each shipment (Stevens and Phillips, 2017). AI is also used to optimize how many items of each product should be kept in different warehouses, as this varies greatly depending on the warehouse, region, season and major cities nearby (Xu et al, 2017). The AI adjusts the optimal stock quantities accordingly.…”

Section: Transporting Goodsmentioning

confidence: 99%

State-of-the-art and adoption of artificial intelligence in retailing

Weber

Schütte

2019

DPRG

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Purpose In the most abstract way, artificial intelligence (AI) allows human work to be shifted toward technological systems that are currently not fully capable. Following this, the domain of retail can be sketched as a natural fit for the application of AI tools, which are known for their high proportion of human work and concurrent low profit margins. This paper aims to explore the current dissemination of the application of AI within the industry. The value-added core tasks of retail companies are examined to determine the possible utilization and the market adoption within the globally largest retail companies is given. Design/methodology/approach The paper uses two different approaches to identify the scientific state-of-the-art: a search on the major scientific databases and an empirical study of the ten largest international retail companies and their adoption of AI technologies in the domains of wholesale and retail. Findings The application within the different value-added core tasks varies greatly depending on the area. In summary, there are numerous possible applications in all areas. Especially, in areas where future forecasts are needed within the task areas (such as marketing or replenishment), the use of AI, today, is both scientifically and practically highly developed. In contrast, the market adoption of AI is highly variable. The pioneers have integrated extensive applications into everyday business, while the challengers are investing heavily in new initiatives. Some others, however, show neither active use nor any effort to adopt such technology. Originality/value To the best of the author’s knowledge, this is one of the first research contributions to analyze the areas of application and the impact of AI structured along the value-added core processes of retail companies.

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“…Amazon, for example, uses AI to select the ideal packaging size for each shipment [54]. The AI is also used to optimize how many items of each product should be stored in different warehouses, as this varies greatly depending on the warehouse, region, season and major nearby cities [55]. The AI adjusts the optimal stock quantities accordingly.…”

Section: Transporting Goodsmentioning

confidence: 99%

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

Weber

Schütte

2019

BDCC

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Information technologies in general and artifical intelligence (AI) in particular try to shift operational task away from a human actor. Machine learning (ML) is a discipline within AI that deals with learning improvement based on data. Subsequently, retailing and wholesaling, which are known for their high proportion of human work and at the same time low profit margins, can be regarded as a natural fit for the application of AI and ML tools. This article examines the current prevalence of the use of machine learning in the industry. The paper uses two disparate approaches to identify the scientific and practical state-of-the-art within the domain: a literature review on the major scientific databases and an empirical study of the 10 largest international retail companies and their adoption of ML technologies in the domain are combined with each other. This text does not present a prototype using machine learning techniques. Instead of a consideration and comparison of the particular algorythms and approaches, the underling problems and operational tasks that are elementary for the specific domain are identified. Based on a comprehensive literature review the main problem types that ML can serve, and the associated ML techniques, are evaluated. An empirical study of the 10 largest retail companies and their ML adoption shows that the practical market adoption is highly variable. The pioneers have extensively integrated applications into everyday business, while others only show a small set of early prototypes. However, some others show neither active use nor efforts to apply such a technology. Following this, a structured approach is taken to analyze the value-adding core processes of retail companies. The current scientific and practical application scenarios and possibilities are illustrated in detail. In summary, there are numerous possible applications in all areas. In particular, in areas where future forecasts and predictions are needed (like marketing or replenishment), the use of ML today is both scientifically and practically highly developed.

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Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Cited by 10 publications

References 19 publications

Optimal Design of International Trade Logistics Based on Internet of Things Technology

Optimal Design of International Trade Logistics Based on Internet of Things Technology

State-of-the-art and adoption of artificial intelligence in retailing

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

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