The capacitated multi-AGV scheduling problem with conflicting products: Model and a decentralized multi-agent approach

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production systems in the context of Industry 4.0. This paper investigates an optimization model and a solution using a decentralized multi-agent approach for a new capacitated multi-AGV scheduling problem with conflicting products (CMASPCP) to take full advantage of AGVs. The novelty of the problem and our model lies in the introduction of AGV capacity constraints and constraints arising from conflicting products, i.e. products that cannot be transported together. As the new I4.0 paradigm tends towards decentralized control, we also present a decentralized multi-agent approach in which AGVs autonomously coordinate to solve the task. The performance of the proposed decentralized approach is compared to a mixed-integer linear programming model on a set of 110 problem instances with different sizes and degrees of complexity. The obtained results show that the proposed decentralized multi-agent approach is effective and competitive in terms of the solution quality and computational time.     

Multi-objective routing algorithms for low-earth orbit satellite network

The low-earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large-scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link-state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi-constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi-object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.     

MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems

In the context of increasingly competitive intelligent manufacturing, the multi-load Automated guided vehicles (AGVs) based Automated Storage and Retrieval System (AS/RS) has been of particular interest, as reductions in the number of AGVs required can significantly decrease potential congestions and increase the system effectiveness. In comparison with the single-load AGVs system, more difficult and critical issue of scheduling multi-load AGVs to automate storage/retrieval missions and to maximize economic benefits remains unresolved. Therefore, we propose a task scheduling optimization method for multi-load AGVs-based systems, with which, the objectives of least number of occupied AGVs, shortest travel time and minimum conflicts can be met simultaneously. The experiments are conducted in various scenarios, and verify that our work can use fewer AGVs to optimize tasks delivery, which enables the AS/RS stakeholders to reach win-win results for system performance and AGVs investment, thus maximizing economic benefit.     

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.     

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network (WSN), scheduling is one of the important issues that impacts the lifetime of entire WSN. Various scheduling schemes have been proposed earlier to increase the lifetime of the network. Still, the results from such methods are compromised in terms of achieving high lifetime. With this objective to increase the lifetime of network, an Efficient Topology driven Cooperative Self-Scheduling (TDCSS) model is recommended in this study. Instead of scheduling the network nodes in a centralized manner, a combined approach is proposed. Based on the situation, the proposed TDCSS approach performs scheduling in both the ways. By sharing the node statistics in a periodic manner, the overhead during the transmission of control packets gets reduced. This in turn impacts the lifetime of all the nodes. Further, this also reduces the number of idle conditions of each sensor node which is required for every cycle. The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints. Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle. According to this, the source can infer the possible routes in a cycle and approximate the available routes. Further, based on the statistics of previous transmissions, the routes towards the sink are identified. Among the routes found, a single optimal route with energy efficiency is selected to perform data transmission. This cooperative approach improves the lifetime of entire network with high throughput performance.     

区块链服务网络的构建机理与技术实现

联盟链技术是我国区块链发展和应用的主阵地.传统联盟链应用存在底层技术平台异构、应用技术门槛高、成链成本大、运维监管难等瓶颈问题,限制了区块链技术与应用发展.提出一种区块链公用基础设施——区块链服务网络(blockchain-based service network, BSN)的构建机理,阐述BSN的技术架构和实现机制. BSN已于2020年4月在中国正式商用,能够显著降低区块链开发、部署、运维、互通和监管成本,有利于区块链技术向企业、政府、行业等领域推广应用,为我国新型智慧城市建设和数字经济发展提供了可信可控的公用基础设施和服务载体.     

基于属性加密的DDS访问控制方案

数据分发服务（Data distribution service,DDS）是一种可靠的实时数据通信中间件标准,它是面向基于发布/订阅模型的分布式环境,在各个领域得到了广泛应用,但现有研究涉及DDS安全技术的成果较少,而在实际应用中发布订阅系统存在多种安全威胁。为了建立灵活可靠的安全机制来确保发布订阅信息的安全性,提出一种以数据为中心的访问控制方案。在属性加密的基础上,对访问树结构进行优化处理,结合发布订阅环境增加属性信任机制。之后采用制定属性连接式与授权策略的方式对发布订阅信息进行加密匹配,并建立DDS访问控制模型来控制发布订阅系统内信息的交互,实现数据的安全分发。经过实验验证,该方案既能够应对DDS存在的几种安全威胁,保障发布订阅信息的机密性,也能够实现系统对特定信息的访问控制,并且发布者订阅者不需要共享密钥,减少了密钥管理的开销。     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

Web service adaptation: A decade’s overview

With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture, in which all aspects of monitoring and adaptation (e.g., proactive, cross-layer, and autonomic adaptation) can be expressed. In addition, the emergence of new computing environments to host and execute various types of services (Web/cloud services, big data-intensive services, mobile services, microservices, etc.) raises the need for more efficient monitoring and adaptation systems. This survey aims to bring a synthesis and a road-map to the adaptation of service-based systems. We also discuss adaptation solutions in emerging service models, such as cloud services and big services. Based on an adaptation taxonomy which we extracted from the surveyed approaches, and by identifying the main requirements and goals of service adaptation in Web, cloud and big data environments, detailed analysis and discussions, as well as the open issues, are provided.     

服务差异二级选址路径问题及大邻域搜索算法

在电子商务背景下,物流公司为了实现及时交付并缓解交通拥堵,可按照客户不同的服务需求,将其分为自取型和配送型,进而选择开放自助点或配送点对客户进行服务。自助点和配送点作为二级物流设施,配送中心作为一级物流设施,由此形成了二级选址-路径问题。在配送车数量约束、自助点容量约束等限制下,兼顾客户点物品回收,以总物流成本最小为目标,建立考虑服务差异的二级选址-路径问题模型。设计自适应大邻域搜索算法进行求解,通过Nguyen的2E-LRP算例进行测试,更新了1个算例的全球最优解,其余算例达到或接近最优解,平均Gap值在1.22%以内,验证了算法的有效性及良好的收敛性。实际案例模拟分析验证了模型的有效性和适用性,可以为相关物流企业提供决策依据和参考。