The teleoperation and coordination of multiple industrial robots play an important role in today’s industrial internet-based collaborative manufacturing systems. The user-friendly teleoperation ...approach allows operators from different manufacturing domains to reduce redundant learning costs and intuitively control the robot in advance. Nevertheless, only a few preliminary works have been introduced very recently, let alone its effective implementation in the manufacturing scenarios. To address the gap, this research proposes a novel multi-robot collaborative manufacturing system with human-in-the-loop control by leveraging the cutting-edge augmented reality (AR) and digital twin (DT) techniques. In the proposed system, the DTs of industrial robots are firstly mapped to physical robots and visualize them in the AR glasses. Meanwhile, a multi-robot communication mechanism is designed and implemented, to synchronize the state of robots in the twin. Moreover, a reinforcement learning algorithm is integrated into the robot motion planning to replace the conventional kinematics-based robot movement with corresponding target positions. Finally, three interactive AR-assisted DT modes, including real-time motion control, planned motion control, and robot monitoring mode are generated, which can be readily switched by human operators. Two experimental studies are conducted on (1) a single robot with a commonly used peg-in-hole experiment, and (2) the motion planning of multi-robot collaborative tasks, respectively. From the experimental results, it can be found that the proposed system can well handle the multi-robot teleoperation tasks with high efficiency and owns great potentials to be adopted in other complicated manufacturing scenarios in the near future.
•Presented a multi-robot multi-client-based communication mechanism to synchronize teleoperation states.•Introduced three typical augmented reality-assisted digital twin-enabled human-in-the-loop robot control modes.•Proposed a RL algorithm for motion control and planning of the multi-robots with learning intelligence.
Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in ...facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an <inline-formula><tex-math notation="LaTeX">\alpha</tex-math></inline-formula>-cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options.
•The e-commerce internal order processing flow is streamlined and re-designed.•A GA-rule-based system for efficient e-commerce order fulfilment is proposed.•An optimal order processing plan is ...generated by genetic algorithm technique.•A system implementation shows a significant order processing time reduction.
In today's world of digitization, the rise of the e-commerce business around the globe has brought a tremendous change not only in our purchasing habits, but also to the entire retail and logistics industry. Given the irregular e-commerce order arrival patterns, limited time for order processing in e-fulfilment centres, and the guaranteed delivery schedules offered by e-retailers, such as same-day or next-day delivery upon placing an order, logistics service providers (LSPs) must be extremely efficient in handling outsourced e-commerce logistics orders. Without re-engineering the order fulfilment processes, the LSPs are found to have difficulties in executing the order fulfilment process due to the tight handling requirements. This, in turn, delays the subsequent processes in the supply chain, such as last-mile delivery operations, consequently affecting customer satisfaction towards both the retailer and the LSP. In view of the need to improve the efficiency in handling e-commerce orders, this study aims at re-engineering the fulfilment process of e-commerce orders in distribution centres. The concept of warehouse postponement is embedded into a new cloud-based e-order fulfilment pre-processing system (CEPS), by incorporating the genetic algorithm (GA) approach for e-commerce order grouping decision support and a rule-based inference engine for generating operating guidelines and suggesting the use of appropriate handling equipment. Through a case study conducted in a logistics company, the CEPS provides order handling solutions for processing e-commerce logistics orders very efficiently, with a significant reduction in order processing time and traveling distance. In turn, improved operating efficiency in e-commerce order handling allows LSPs to better align strategically with online retailers, who provide customers with aggressive, guaranteed delivery dates.
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.
Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly ...customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability.
This paper considers the re-entrant scheduling problem, wherein the most remarkable character is that the jobs enter the processing lines more than once. The objective is to provide a comprehensive ...review which gives the researchers and practitioners an overview of the applicability of techniques in re-entrant scheduling. Few published reviews dealt with this particular kind of problem and only some research regards the re-entrant character as a hypotaxis of their main problems. This paper is the first paper that gives a full picture of the re-entrant scheduling problem. Considered as a NP-hard problem, a growing number of researchers have employed various methods to solve this complex problem. A survey has been conducted from the recently published literature on the re-entrant problem. This paper has summarised the problem and the relative research methodologies have been studied. Mathematical methods and meta-heuristics, especially Petri net, dispatching rules and genetic algorithm, emerge as the most frequently used methods in recent years, which are presented in detail. Moreover, future research implications have been identified and are suggested. It may help to bring in more awareness of the problem and new techniques to solve it.
Parallel mechanisms have been employed as architectures of high-precision vibration isolation systems. However, their performances in all degrees of freedom (DOFs) are nonidentical. The conventional ...solution to this problem is isotropic mechanism design, which is laborious and can hardly be achieved. This article proposes a novel concept; namely, isotropic control, to solve this problem. Dynamic equations of parallel mechanisms with base excitation are established and analyzed. An isotropic control framework is then synthesized in modal space. We derive an explicit relationship between modal control force and actuation force in joint space, enabling implementation of the isotropic controller. The multi-DOF system is transformed into multiidentical single-DOF systems. Under the framework of isotropic control, parallel mechanisms obtain an identical frequency response for all modes. An identical corner frequency, active damping, and rate of low-frequency transmissibility are achieved for all modes using a combining proportional, integral, and double integral compensator as a subcontroller. A 6-U P S parallel mechanism is presented as an example to demonstrate effectiveness of the new approach.
With the rapid growth of the global digital economy, supply chain finance has entered the stage of platform development in view of the history, policy environment, market status and other factors. ...Supply chain finance relies on multiple supply chain stakeholders to carry out financial business, which needs to solve a variety of financial risk control and pricing issues. The financing model in supply chain finance can be differentiate from the traditional credit model. The service mode and leading mode of supply chain finance need to be adjusted in accordance with the changes of the industrial operation. Further, the business mode of supply chain finance shows a diversification trend, which includes traditional supply chain financial models such as accounts receivable, advance payment, inventory financing, and supply chain credit financing models. In this paper, we investigate the similarities between supply chain finance and options, and further introduce American call options to supply chain financial products under the mode of small and medium-sized enterprises’ (SMEs) accounts receivable financing. The price of supply chain financial products is derived through the trinomial tree option pricing model, which determines the corporate financing interest rates. The rationality of the proposed pricing model is validated in comparison with the medium and long-term load bank interest rates. The contribution of the paper is in providing SMEs with supply chain financial products under the accounts receivable model to resolve financing difficulties and the pricing the products through the trinomial budget pricing model.
PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive ...products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.
Dynamic pricing, which aims to dynamically adjust the charging price in a timely fashion to unlock the flexibility of electric vehicle (EV) customers, has been extensively studied with the rapid ...development of charging technologies. Many existing works on dynamic pricing focus on maximizing the social welfare of charging service providers and EV customers. Cases of high-dimensional charging environments, which are often encountered with the rapid growth of EV market penetration, have been rarely considered to date. This article proposes a new dynamic pricing framework for EV charging stations that can offer multiple charging options to customers over a finite-time horizon. The charging price can be dynamically adjusted to maximize the quality of service (QoS) with a differentiated service requirement level (SRL) whenever the arrival rates and queuing system capacities of the charging systems are given at the end of a time period. The dynamic pricing problem is formulated as a finite-discrete horizon Markov decision process (MDP) with a mixed state space. A customized deep reinforcement learning (DRL) approach is employed to solve the examined EV dynamic pricing problem. The simulation results demonstrate the effectiveness of the proposed method.
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