Operations management of smart logistics: A literature review and future research

doi:10.1007/s42524-021-0156-2

Front. Eng

2021, Vol. 8

Issue (3) : 344-355 https://doi.org/10.1007/s42524-021-0156-2

REVIEW ARTICLE

Operations management of smart logistics: A literature review and future research

Bo FENG¹, Qiwen YE²(

)

¹. School of Business and Research Center for Smarter Supply Chain, Soochow University, Suzhou 215021, China
². School of Economics & Management, South China Normal University, Guangzhou 510006, China

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Abstract

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.

Keywords smart logistics operations management optimization Internet of Things

Corresponding Author(s): Qiwen YE

Just Accepted Date: 16 March 2021 Online First Date: 13 April 2021 Issue Date: 13 July 2021

Cite this article:

Bo FENG,Qiwen YE. Operations management of smart logistics: A literature review and future research[J]. Front. Eng, 2021, 8(3): 344-355.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0156-2
https://academic.hep.com.cn/fem/EN/Y2021/V8/I3/344

Country/Area	Year	Policies/Programs	Policy focuses
US	1993	Commodity Flow Survey (CFS)	Collect the data of commodity flow for transportation planning and assessing the demand of transportation facilities
US	2012	The moving ahead for progress in the 21st century	105 billion USD is invested for developing the surface transportation network and infrastructure
UK	2018	Industrial strategy: Artificial intelligence sector deal	Facilitate the development of artificial intelligence and data-driven economics
France	2015	Logistics Strategic Plan 2025	Focus on the development of logistics innovation and the cooperation between smart logistics and smart manufacturing
France	2018	Digital industrial transformation: Measures for mid-caps and SMEs	Smart Internet of Things and smart transportation are proposed as the “future industries” in France
European Union	2019	The Trans-European Transport Network (TEN-T) policy	Facilitate the development of a Europe-wide transportation network with innovative applications, emerging technologies, and digital solutions
Russia	2018	The State of the Union Address 2018	Develop a digital platform for the logistics industry that is compatible with the global information space
Japan	2018	Strategic Innovation Promotion Program (SIP) phase two	Expand the related services with a self-driving system
China	2013	Guidance on advancing logistics informatization	Facilitate the standardization, digitization, automation, and intelligence of logistics information collection
	2016	The implementation of “Internet+” efficient logistics	Address the applications of RFID, integrated sensor, robot, and big data analysis in the logistics industry to realize the intelligence of logistics
	2019	Opinions on promoting high-quality development of logistics industry and facilitating the formation of a strong domestic market	Develop a high-quality network of logistics facilities and a shared logistics information platform to support the logistics industry in serving the real economy

Tab.1 Smart logistics policies

Fig.1 Smart logistics.

Fig.2 Research streams of smart logistics.

Tab.2 Research on the application of intelligent technologies in smart logistics

Logistics functions	Application scenario	System framework	Literatures
Optimization	Cold chain logistics	Decentralized and centralized carbon trading systems provide a reasonable revenue-and-cost-sharing contract with the trade-off among the market demand, total carbon emission, and supplier’s profit	Ma et al. (2020)
	Warehouse management	An IoT-based warehouse management system maximizes warehouse productivity and picking accuracy with computational intelligence techniques	Lee et al. (2018)
	Routing optimization	User-centric logistics service model using ontology: Users’ smart devices are applied to collect the location and situation information from the delivery vehicle and user for routing optimization	Sivamani et al. (2014); Shen et al. (2019)
	Dispatching optimization	IoT-based logistics dispatching systems: Dijkstra’s algorithm and ant colony algorithm are applied to improve the coordination among demand, order-picking, and cloud technologies	Wang et al. (2020)
	Cross-docking management	An IoT-enabled information infrastructure system provides a closed decision-execution cross-docking loop with frontline real-time data and user feedback	Luo et al. (2016a)
	Emergency medicine logistics	A multi-stage emergency medicine logistics system with ambulance drones shows better performance of survival probability	Wang et al. (2017a)
Automation	Production management	A two-stage inspection process with an automation policy increases the efficiency of discounted sale in the disposal subsection by discarding defective products automatically	Sarkar et al. (2019)
	Storage management	Process-based context-aware storage system: Static and dynamic context data are applied to conduct the staged configuration for resolving process variability at runtime automatically	Murguzur et al. (2014)
	E-commerce logistics distribution	Intelligent E-commerce logistics system: Semantic web and data mining are applied to obtain the probability distribution of random variables for automated decision-making	Wang et al. (2017b)
	Material handling and transport	A smart connected logistics system integrates different IoT technologies with mobile automated platforms, mobile robotic systems, and multi-agent cloud-based control	Gregor et al. (2017)
System security	Data storage and access security	Data storage and access mechanism based on blockchain: Consensus authentication is built by constructing the correlations between the fundamental data and corresponding blockchain	Fu and Zhu (2019)
	System gateways security	Device driver security architecture provides trustworthy gateways between manufacturer and logistics system	Fraile et al. (2018)
	IoT devices security	Blockchain-based security system framework: Multiple-agreement algorithm is applied to enable thin-plate spline performance, which is efficient in solving the vulnerability of IoT multi-platform security	Kim et al. (2018)
	Authentication of object tracking	Fine-grained IoT-enabled object tracking system: Cloud storage, symmetric key cryptosystem, and one-way hash function are applied to perform end-to-end authentication protocol	Anandhi et al. (2019)

Tab.3 Research on the framework design of CPS

Tab.4 Research on the algorithms of VRPs in smart logistics

Fig.3 Future research in smart logistics.

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