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An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Shaanxi, 710072, PR China / Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, PR China.
Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Department of Production, University of Vaasa, 65200, Vaasa, Finland.ORCID iD: 0000-0001-8006-3236
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
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2019 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 215, p. 806-820Article in journal (Refereed) Published
Abstract [en]

Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 215, p. 806-820
Keywords [en]
Internet of things, Green logistics, Dynamic optimization, Real-time information
National Category
Computer Engineering Transport Systems and Logistics Communication Systems Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-154906DOI: 10.1016/j.jclepro.2018.12.254ISI: 000459358300068OAI: oai:DiVA.org:liu-154906DiVA, id: diva2:1293332
Note

Funding agencies: National Natural Science Foundation of China [51675441, 51475096]; 111 Project Grant [B13044]; Fundamental Research Funds for the Central Universities [3102017jc04001]; Circularis (Circular Economy through Innovating Design) - Vinnova Swedens innovation a

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-03-08Bibliographically approved

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