With the increasing penetration of electric vehicles (EVs) into the transportation sector, the efficient scheduling of charging demands has emerged as a critical challenge. Traditional day-ahead methods depend heavily on accurate predictions of charging demand, which are often impractical due to the dynamic and uncertain nature of EV charging behavior. To tackle this issue, this study develops a smart hour-ahead EV charging scheduling strategy (HCSS) that leverages real-time demand information to dynamically manage and control the charging behavior. An online reservation system (ORS) considering the heterogeneous demands is introduced to enable EV users to submit charging requests in advance, while allowing the system to optimize charging schedules in real time. The objective of the ORS-based HCSS is to minimize peak load within a charging zone to ensure grid stability and secure operation. A tailored simulated annealing (SA) approach is proposed to solve the hour-ahead scheduling problem, where a depth-first search (DFS)-based initial solution construction method and a set of customized neighborhood solution search operators are specifically designed to enhance the exploration and exploitation capabilities of the SA approach. Numerical experiments on multiple problem sizes are conducted to assess the effectiveness of our proposed HCSS and SA-based solution approach. Extensive sensitivity analysis is also performed to explore the impact of several key factors on the hour-ahead scheduling performance.

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.

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.

Assembly lines of large-size products usually allow multiple workers to process tasks simultaneously on the same product. Meanwhile, due to the increasing demand of customized products, a diverse product mix with more product models and optional features is necessary. Although line balancing and model sequencing are interwoven, only the balancing problem of the mixed-model multi-manned assembly lines (MMALs) has been explored. In this study, a new mixed-integer linear programming model is proposed for balancing and sequencing of MMALs to minimize the number of workers, the number of stations, and the amount of utility work. An innovative approach based on Benders’ decomposition algorithm (BDA) is developed. Valid inequalities based on maximal cliques are generated to deal with the incompatible tasks which cannot be assigned to the same station to tighten and reduce the size of the formulation. An initial solution based on the greedy algorithm feeds the BDA to accelerate the convergence. Only one branch-and-search tree is built for the master problem to speed up the algorithm, and optimality cuts based on the subproblems are used as lazy cuts. The effectiveness of the proposed BDA is demonstrated by numerical results.

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.

Human-robot collaboration (HRC) has been considered as a promising paradigm towards futuristic human-centric smart manufacturing, to meet the thriving needs of mass personalization. In this context, existing robotic systems normally adopt a single-granularity semantic segmentation scheme for environment perception, which lacks the flexibility to be implemented to various HRC situations. To fill the gap, this study proposes a multi-granularity scene segmentation network. Inspired by some recent network designs, we construct an encoder network with two ConvNext-T backbones for RGB and depth respectively, and an decoder network consisting of multi-scale supervision and multi-granularity segmentation branches. The proposed model is demonstrated in a human-robot collaborative battery disassembly scenario and further evaluated in comparison with state-of-the-art RGB-D semantic segmentation methods on the NYU-Depth V2 dataset.

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.

Queueing models can be used to evaluate the performance of manufacturing systems. Due to the emergence of cluster tools in contemporary production systems, proper queueing models have to be derived to evaluate the performance of machines with complex configurations. Job cascading is a common structure among cluster tools. Because of the blocking and starvation effects among servers, queue time analysis for a cluster tool with job cascading is difficult in general. Based on the insight from the reduction method, we proposed the approximate model for the mean queue time of a cascading machine subject to breakdowns. The model is validated by simulation and performs well in the examined cases.

The capacitated vehicle routing problem (CVRP) is one of the combinatorial optimization problems with the most widespread applications in practice. Because of the intrinsic computational complexity, the approximate algorithms are commonly employed to solve the CVRP rather than the exact algorithms. In this research, the artificial bee colony algorithm (ABC), derived from the swarm intelligence, is adapted to handle the CVRP. The application of the ABC algorithm in solving the CVRP exploited the inherent features of the swarm intelligence. More importantly, a routing directed ABC algorithm (RABC) is further proposed consisting of numerous improvements in order to enhance the capability of the diversified search and intensified search of the conventional ABC algorithm, which incorporates the useful information from the routing as well. The RABC algorithm is examined with different benchmark test instances. The experimental results show that the RABC algorithm excels the conventional ABC algorithm significantly. Moreover, the application of the RABC algorithm in solving the CVRP can provide practical insights for the implementation of swarm intelligence in solving other combinatorial optimization problems.

Abstract Owing to the shockwaves brought by the recent financial tsunami, most enterprises are facing tremendous challenges in maintaining the good liquidity of their own companies. In order to sustain a desirable level of cash flow for expanding business, inventory needs to be well organized because unnecessary inventory that ties up the capital in the business would prevent the enterprises from making investments. Because the existing approaches to replenishment are inflexible and unsophisticated, a new customer‐based responsive replenishment system embracing online analytical processing, fuzzy logic and genetic algorithm is proposed in this paper. This system could determine accurate and realistic order quantities based on all possible and relevant variables that affect the order quantity for each item that needs to be replenished. Once the quantity has been accurately identified, the company can increase the level of customer satisfaction while minimizing stocks. Furthermore, rather than static rule repositioning, the proposed dynamic rule refining ability makes the replenishment system self‐ameliorating by using genetic algorithm to investigate the possible fuzzy rule candidates for a more accurate inventory management model. A study has been conducted in a case company for the validation of the feasibility of the proposed system. After performing a spatial analysis, the results obtained indicate that the proposed responsive replenishment system is capable of ensuring improved inventory control performance in the case company.

Petri net (PN) is a classical tool for modeling, simulation, and analysis. With the emergence of distributed manufacturing system (DMS), PN has been evolved into different forms such as colored PN (CPN) and timed PN (TPN). To fulfill the practical requirements, CPN has been extended to a larger and more complex model. Meanwhile, DMS becomes an important issue for industry, and how to model a complex manufacturing network for better throughput needs further investigation. A simple single type of PN only cannot fulfill all contemporary requirements at the same time. This paper proposes a new concept for a new PN-hierarchical timed CPN (HTCPN), which can be a powerful tool for modeling, simulation, and analysis for current large complex distributed manufacturing system. This paper presents a general structure of DMS and uses HTCPN to model the manufacturing system as discrete event with dynamic behavior. The proposed approach shows superiority on modeling large complex manufacturing system as it represents the material flow and transitions clearly and provides overview and detailed description for DMS.

Reactive ion etching (RIE) is a process in the fabrication of semiconductor devices. The ability to predict the influence of the process parameters of RIE is crucial in terms of machine performance as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this correspondence paper presents a novel performance tradeoff function for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts, taking into full consideration their impact on the performance of the machine involved. An experiment using the RIE machine is provided to validate the practicability of the proposed approach.

In today's competitive logistics business environment, airfreight forwarders need to optimize every aspect of their logistics operations. However, forwarders still heavily rely on human brain and working experiences for calculating complex cargo packing and scheduling problems. Although recent research studies related to cargo packing and scheduling problems have resulted in the development of a number of advanced techniques of cargo planning, it can be seen that most of the research work is focused on the optimization of space in order to achieve the maximum possible amount of cargo to be packed in the minimum of space. After numerous site evaluation and end-user feedbacks, it is found that space optimization does not necessarily cause profit optimization, which is the ultimate aim of logistics providers. A study of contemporary research publications indicates that there are inadequate research studies related to profit-based optimization in cargo packing areas. This paper presents a profit-based air cargo loading information system (ACLIS) that embeds an innovative technology known as heuristics iterative reasoning technology (HIRT) that supports loading plan generation, focusing on maximization of the profit margin. In general, the proposed system is meant to maximize the profit in the airfreight forwarding business. It adopts an objective function governed by a list of constraints together with rule-based reasoning to provide expert advice to support the generation of appropriate loading plans

Abstract: To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper.