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Conference

International Conference on Logistics, Informatics and Service Sciences 

About: International Conference on Logistics, Informatics and Service Sciences is an academic conference. The conference publishes majorly in the area(s): Supply chain & The Internet. Over the lifetime, 539 publications have been published by the conference receiving 1058 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
01 Aug 2018
TL;DR: In this paper, a smart logistics solution encapsulating smart contracts, logistics planner and condition monitoring of the assets in the Supply Chain Management area is proposed, which demonstrates accountability, traceability and liability for asset handling across the supply chain by various parties involved in a logistics scenario.
Abstract: Advancements in sensors and devices have enabled Internet of Things (IoT) adoption in various sectors, especially in domains looking to automate and increase their real-time decision making capabilities to improve efficiencies. Supply chain management in logistics is a perfect fit for adoption of IoT, since it involves shipment of assets being moved, tracked and housed by a number of machines, vehicles and people each day. Smart Contracts are terms and conditions parties can specify that assure trust in the enforceability of the contract and provide visibility at every step of a supply chain. IoT devices can write to a smart contract as a product moves from the factory floor to the store shelves, providing real-time visibility of an enterprises entire supply chain. This paper proposes a smart logistics solution encapsulating smart contracts, logistics planner and condition monitoring of the assets in the Supply Chain Management area. A prototype of the solution is implemented which demonstrates accountability, traceability and liability for asset handling across the supply chain by various parties involved in a logistics scenario.

43 citations

Proceedings ArticleDOI
01 Aug 2018
TL;DR: The results of correctness and security analysis showed that this scheme can provide new technical ideas and methods for big data sharing and data trace.
Abstract: The existing data sharing models have some issues such as poor transparency of data transactions, data without security assurance and lacking of effective data tracking methods. This paper proposed a brand-new data sharing scheme based on blockchain technology. Firstly, a blockchain double-chain structure about blockchain was introduced, one chain was used to store the original data and another was used to store transaction data generated by transactions. This structure separated the original data storage and data transactions. Secondly, combined with proxy re-encryption technology, safe and reliable data sharing were achieved. Finally, a new design was implemented. The logical structure of data transaction records enables data to be traced. The results of correctness and security analysis showed that this scheme can provide new technical ideas and methods for big data sharing and data trace.

42 citations

Proceedings ArticleDOI
24 Jul 2016
TL;DR: The analysis obtained from the existing literature highlighted two key issues, which were; i) most FDM ignored certain important characteristics of DM, and ii) lack of explanation on how FDM obtained controlled feedback with the lack of iteration process.
Abstract: The Fuzzy Delphi Method (FDM) is the modified and enhanced version of the classical Delphi technique. Improvement was made to rectify the imperfection of traditional Delphi Method (DM) that leads to low convergence in retrieving outcomes, loss of important information, and long progress of investigation. Nevertheless, this approach has been employed in various application domains, including humanities, management, business, physical science, and engineering. Furthermore, the literature has exposed numerous key issues in relation to the existing FDM that need to be resolved with regard to the classical DM. Hence, the analysis obtained from the existing literature highlighted two key issues, which were; i) most FDM ignored certain important characteristics of DM, and ii) lack of explanation on how FDM obtained controlled feedback with the lack of iteration process. Additionally, it was observed that there had been lack of comparison between decision outcomes obtained based on FDM and the decision outcomes obtained from the traditional DM.

29 citations

Proceedings ArticleDOI
27 Jul 2015
TL;DR: The weighted least connections scheduling algorithm is improved, and the Adaptive Scheduling Algorithm Based on Minimum Traffic (ASAMT) is designed, which conducts the real-time minimum load scheduling to the node service requests and configures the available idle resources in advance to ensure the service QoS requirements.
Abstract: Cloud computing has officially entered the commercial application stage, which puts forward higher requirements on network load balancing. Leveraging effective load distribution and traffic scheduling algorithm to reasonably allocate the request data between every processing nods to achieve optimal processing capacity of the system is one of the effective ways to improve the utilization of network resources. The unique self-directed learning and reconfiguration capabilities of cognitive network [1] enable the load balancing to become more effective. Based on research of the existing traffic scheduling algorithm, this paper improves the weighted least connections scheduling algorithm, and designs the Adaptive Scheduling Algorithm Based on Minimum Traffic (ASAMT). ASAMT conducts the real-time minimum load scheduling to the node service requests and configures the available idle resources in advance to ensure the service QoS requirements. Being adopted for simulation of the traffic scheduling algorithm, OPNET is applied to the cloud computing architecture. Experimental results show that, under the premise of no large network cost, the load condition of this algorithm is better than that of the unmodified weighted least connection scheduling algorithm.

23 citations

Proceedings ArticleDOI
27 Jul 2015
TL;DR: This paper extends the FRLM, combining with the queuing theory, and reformulate a new location-sizing model in a given largest waiting time that customers can accept that optimally allocate the charging spots without exceed the given waiting time so as to maximize the total charging service.
Abstract: Imperfect electric vehicle charging infrastructure network has become a major obstacle for prompting the adoption of electric vehicles. Kuby (2005) considered the constraint of vehicle range, and developed a location model for alternative-fueling vehicles based on maximum flow -FRLM (flow - refueling location mode). In the plan of deploying an electric vehicle charging station network, not only should we consider the vehicle range, but also need to realize the queuing issue because of the number limitation of charging spots. Therefore, when we design a charging station network, we need to take the number of charging spots into consideration. In this paper, we extend the FRLM, combining with the queuing theory, and reformulate a new location-sizing model in a given largest waiting time that customers can accept. The location-sizing model optimally allocate the charging spots without exceed the given waiting time so as to maximize the total charging service. And we also conclude the different influence on the results of different factors via a series of numerical experiments, and give some advice for the developing direction of the electric car in the future.

18 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
20211
20201
20191
201857
2016260
2015219