In the landscape of Industry 4.0, enhancing data traceability across the supply chain is imperative. With the development of quantum computers, blockchains traditionally used for traceability struggle to guarantee the authenticity of data. Furthermore, only chained blocks have been used in previous traceability systems, which has resulted in a full backup for each business unit in the supply chain, wasting storage resources significantly. Another problem is that there are differences in the data structure and consensus mechanisms of each unit, which makes maintenance difficult. To address these problems, it is encouraging to create a new blockchain with high scalability and resistance to quantum attacks. This article innovatively proposes a quantum hybrid blockchain (QHB)-based data authenticity framework (QHB-DA) with five layers, which not only accomplishes traceability, but also has assurance that data can withstand verification. In QHB-DA, blockchain utilizes a combination of directed-acyclic-graph and chained structures to reduce data redundancy, in which quantum hashes are performed to connect blocks. Algorithms ED-QKD and D2B-QSC are designed to keep the information from being leaked during transmission and format conversion. Through cost-benefit analysis, security analysis and empirical experiments, feasibility of quantum hashing and the attack resistance of the QHB-DA are demonstrated, which can reduce storage wastage and completely implement the data authenticity.

Digital transformation (DT) and automation technologies have significantly impacted logistics and supply chain management (LSCM), enhancing business performance. Efforts to automate processes alongside machine and robotic automation aim to improve reliability, consistency, efficiency, and cost-effectiveness. While existing research has explored specific aspects of DT and robotic process automation (RPA), the deployment and implementation of RPA as a service (RPAaaS) in LSCM are under-explored. This study focuses on RPA in LSCM and aims to improve organisational processes associated with DT. A systematic review identifies five knowledge clusters regarding RPA deployment for DT in LSCM. Real-life RPA implementation cases in a third-party logistics company highlight challenges and opportunities related to RPA deployment. Insights on the timing and choice of the RPAaaS platform to facilitate DT in enterprises are discussed. This paper provides extensive theoretical insights into RPA for DT in LSCM and offers practical guidance for streamlined implementation.

In natural disasters, the emergency rescue system is of utmost importance for the smart city development, but the search and rescue (SAR) leveraging unmanned aerial vehicles (UAVs) is under-explored. This study has established an Internet of UAVs architecture motivated by search and rescue activities, focusing on the path planning problem of UAV in three-dimensional urban disaster scenes. A mathematical model for UAV-based SAR missions has been built, aiming to use UAV platforms equipped with life-detection radars to search for survivors inside buildings. Our aim is to search for the most survivors in the shortest amount of time while ensuring coverage of the entire search area. Based on the mathematical model for SAR activities, the improved Multi-Verse Optimizer (IMVO) is used to solve the path of UAV. Finally, simulations were conducted in a photorealistic urban scenario, demonstrating the paths generated by the proposed method. The results indicate the potential of the generated path in terms of search time and the number of survivors discovered.

Efficiently harnessing group wisdom in complex decision-making domains is a crucial endeavour as socioeconomic society evolves and real-life problems become increasingly intricate. The synergy of fuzzy sets, information aggregation, consensus reaching, and multi-criteria decisionmaking (MCDM) techniques offers a versatile framework for systematically incorporating human intelligence in quantitative judgements. The Best Worst Method (BWM), a robust pairwise comparison-based MCDM technique, has gained prominence due to its distinct advantages: streamlined comparison steps, more consistent preference information, and mitigation of anchoring bias and equalizing bias. This paper focuses on a comprehensive survey of advanced fuzzy set theories (including intuitionistic, neutrosophic, hesitant, Pythagorean, spherical and Fermatean fuzzy sets), commonly used aggregation operations under fuzzy numbers (including classical mean, generalised Heronian mean, power averaging, min and max, and ordered weighted averaging operators), and group consensus reaching process for the BWMbased decision models. A generalised algorithm for the fuzzy best-worst group decision-making (F-BWGDM) process based on democratic-autocratic decision style is demonstrated with five performance measures, including consistency ratio, conformity, minimised violation, total deviation, and sensitivity analysis. In this survey, the technical pitfalls and application studies related to F-BWGDM are analysed to stimulate upcoming exploration in this domain, with the ultimate goal of maintaining human group decision-making processes for qualitative assessments in a fair and systematic manner

The autonomous remote inspection method based on unmanned aerial vehicle (UAV) has potential benefits in solving the safety inspection problem of large-scale industrial facilities. However, the low coverage rate caused by unsatisfactory trajectory quality is the main challenge of autonomous inspection operations. Therefore, in order to effectively optimize the trajectory quality of UAV, in this article, a mathematical model for trajectory planning considering UAV energy consumption, mapping efficiency, and target structure coverage is established while respecting various constraints related to hardware limitations of visual sensors and UAVs. To solve the above model to achieve autonomous inspection, a two-stage heuristic algorithm is designed, aiming to optimize a set of viewpoints for maximizing coverage of the target structure and reducing energy and time consumption. Finally, computational experiments were conducted based on three real industrial scenarios, proving that the model with the proposed algorithm in this study outperforms other advanced methods.

The rapid development of the Industrial Internet-of-Things (IIoT) has led to an exponential growth in the deployment of industrial applications on user-owned smart devices, which poses significant challenges in identity management (IDM) concerning both privacy and quantity. The advent of blockchain technology can fulfill some of these requirements. However, as the number of identities in the IIoT environment grows exponentially, the storage occupied by nodes in the blockchain system gets larger gradually and can never be curtailed. In addition, to maintain the security of identity information, the characteristics of blockchain on openness and transparency are not appropriate for IDM. To this end, we propose a lightweight, secure, and trustworthy stateless blockchain-enabled IDM architecture for IIoT. Specifically, by incorporating the cryptographic accumulator with blockchain, the set of transactions can be turned into a length-constant proof that does not change when identities are modified, in which the identity information is concealed completely. Furthermore, the stateless blockchain structure is formulated and new consensus, identity modification and verification algorithms are presented. Then, we give a comprehensive threat model and security analysis of the proposed system. Finally, the experimental results demonstrate that total time cost and blockchain size are 130.25 ms and 100.13 MB, which significantly improves portability and efficiency in IIoT scenarios.

This study aims to determine the optimal deployment plan for EV fast charging stations in a transportation network with a limited budget. The objective of the deployment problem is to maximize the quality of service (QoS) with respect to both waiting time and range anxiety from the perspective of EV customers. With the rapid growth of the electric vehicle (EV) market penetration, state-of-the-art algorithms based on mathematical programming are limited in handling high-dimensional optimization problems adequately. Unlike previous studies, we make the first attempt to formulate the fast charging station deployment problem (FCSDP) as a finite discrete Markov decision process (MDP) in a novel reinforcement learning (RL) framework to alleviate the curse of dimensionality problem. Since creating a supervised training dataset is impractical due to the high computational complexity of the FCSDP, we propose a recurrent neural network (RNN) with an attention mechanism to learn the model parameters and determine the optimal policy in a completely unsupervised manner. Finally, numerical experiments are conducted on multiple problem sizes to evaluate the performance of the RNN-based RL framework. Simulation results show that the proposed approach outperforms the comparing algorithms in terms of solution quality and computation time.

International audience

Recently, global e-commerce businesses have been blooming due to the convenience they offer, their product range, and the individualized products and services they offer. To maintain an entire ecosystem, effective platform-vendor relationships should be considered, through which e-commerce platforms can provide collaborative packages to vendors. E-vendor relationship management (eVRM) should then be developed to identify, attract, retain, and develop existing and new vendors so that groups of loyal vendors can be managed. However, eVRM in e-commerce is an area that has received less attention. This paper proposes an adaptive e-vendor relationship-management system (AVRMS) to provide decision-making support for the formulation of vendor management strategies. The contribution of this study is that it addresses the missing link of platform-vendor relationship management in global e-commerce environments, while integrating data-driven approaches and artificial intelligence techniques to generate a new synergy for the facilitation of eVRM.

Abstract With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.