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This paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.

Mixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.

The results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.

The sustainability of the route construction applied to the PI is validated through carbon emissions.

This approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand.

This approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.

This paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.

Switching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.

It was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.

The simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.

This study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.

Small highly saturated interior permanent magnet- synchronous machines (IPMSMs) show a very nonlinear behaviour. Such machines are mostly controlled with a closed-loop cascade control, which is based on a d-q two-axis dynamic model with constant concentrated parameters to calculate the control parameters. This paper aims to present the identification of a complete current- and rotor position-dependent d-q dynamic model, which is derived by using a finite element method (FEM) simulation. The machine’s constant parameters are determined for an operation on the maximum torque per ampere (MTPA) curve. The obtained MTPA control performance was evaluated on the complete FEM-based nonlinear d-q model.

A FEM model was used to determine the nonlinear properties of the complete d-q dynamic model of the IPMSM. Furthermore, a fitting procedure based on the nonlinear MTPA curve is proposed to determine adequate constant parameters for MTPA operation of the IPMSM.

The current-dependent d-q dynamic model of the machine models the relevant dynamic behaviour of the complete current- and rotor position-dependent FEM-based d-q dynamic model. The most adequate control response was achieved while using the constant parameters fitted to the nonlinear MTPA curve by using the proposed method.

The effect on the motor’s steady-state and dynamic behaviour of differently complex d-q dynamic models was evaluated. A workflow to obtain constant set of parameters for the decoupled operation in the MTPA region was developed and their effect on the control response was analysed.

In response to food supply constraints resulting from coronavirus disease 2019 (COVID-19) restrictions, in the year 2020, the project developed automated household Aquaponics units to guarantee food self-sufficiency. However, the automated aquaponics solution did not fully comply with data privacy and portability best practices to protect the data of household owners. The purpose of this study is to develop a data privacy and portability layer on top of the previously developed automated Aquaponics units.

Design Science Research (DSR) is the research method implemented in this study.

General Data Protection and Privacy Regulations (GDPR)-inspired principles empowering data subjects including data minimisation, purpose limitation, storage limitation as well as integrity and confidentiality can be implemented in a federated learning (FL) architecture using Pinecone Matrix home servers and edge devices.

The literature reviewed for this study demonstrates that the GDPR right to data portability can have a positive impact on data protection by giving individuals more control over their own data. This is achieved by allowing data subjects to obtain their personal information from a data controller in a format that makes it simple to reuse it in another context and to transmit this information freely to any other data controller of their choice. Data portability is not strictly governed or enforced by data protection laws in the developing world, such as Zimbabwe's Data Protection Act of 2021.

Privacy requirements can be implemented in end-point technology such as smartphones, microcontrollers and single board computer clusters enabling data subjects to be incentivised whilst unlocking the value of their own data in the process fostering competition among data controllers and processors.

The use of end-to-end encryption with Matrix Pinecone on edge endpoints and fog servers, as well as the practical implementation of data portability, are currently not adequately covered in the literature. The study acts as a springboard for a future conversation on the topic.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

Auxiliary power system is an indispensable part of the train; the auxiliary systems of both electric locomotives and EMUs mainly are powered by one of the two ways, which are either from auxiliary windings of traction transformers or from DC-link voltage of traction converters. Powered by DC-link voltage of traction converters, the auxiliary systems were maintained of uninterruptable power supply with energy from electric braking. Meanwhile, powered by traction transformers, the auxiliary systems were always out of power while passing the neutral section of power supply grid and control system is powered by battery at this time.

Uninterrupted power supply of auxiliary power system powered by auxiliary winding of traction transformer was studied. Failure reasons why previous solutions cannot be realized are analyzed. An uninterruptable power supply scheme for the auxiliary systems powered by auxiliary windings of traction transformers is proposed in this paper. The validity of the proposed scheme is verified by simulation and experimental results and on-site operation of an upgraded HXD3C type locomotive. This scheme is attractive for upgrading practical locomotives with the auxiliary systems powered by auxiliary windings of traction transformers.

This scheme regenerates braking power supplied to auxiliary windings of traction transformers while a locomotive runs in the neutral section of the power supply grid. Control objectives of uninterrupted power supply technology are proposed, which are no overvoltage, no overcurrent and uninterrupted power supply.

The control strategies of the scheme ensure both overvoltage free and inrush current free when a locomotive enters or leaves the neutral section. Furthermore, this scheme is cost low by employing updated control strategy of software and add both the two current sensors and two connection wires of hardware.

The study aims to explore the argument of implementing the lean method to part or whole of an operation by examining the moderating impact of varying levels of the extent of implementation of four different lean methods, along with their functionalities, in predicting productivity improvement (PI).

As the focus of understanding the efficacy of lean principles is shifting from process to industry level, this study tried to generalize the approach by gathering data from 132 large Indian auto component manufacturers. This involves an assessing/monitoring approach rather than measurement.

Results highlighted the interdependence or individuality of the extent of implementation of lean methods and their functionalities. Findings revealed a significant moderating effect in improving productivity to a greater extent of 50%.

Adopting an assessment approach to a measurement study provides a noteworthy contribution to bridging theory and practical consequences. The findings can be appropriately extrapolated to medium and small enterprises forming a critical connection in the entire automobile manufacturing ecosystem.

The study showed that even if a lean method is applied to a certain extent of operations the chances of PI are significant. This is important for decision makers as they confront problems of optimum resource allocation.

PI, reduced cost and generalization of results would enable the auto component industry to become more competitive.

The examination of the moderation effect of a lean principle implementation extent, along with that of its functionalities to predict the improvement in productivity from its existing level, is a major outcome of this study.

This study aims to provide a comprehensive overview on positive drivers and negative factors connected to the Covid-19 pandemic which can jointly shape wine tourism intentions.

The present study relies on a large sample of 399 US wine tourists. Partial least square structural equation modelling is adopted for data analysis.

Results reveal that willingness to avoid Covid risk while travelling negatively impacts wine tourism intentions and competitively mediates the effect of Covid phobia. Both situational and personal involvement with wine are key antecedents of future wine tourism intentions.

This research contributes to understand the role of willingness to avoid travel-related risks during health crises. Furthermore, it improves existing knowledge on the effect of wine involvement on wine tourism intentions, highlighting the predictive relevance of situational involvement in explaining this relationship.

Results constitute critical information to practitioners and destination management operators for improving their resilience under similar circumstances. Updated information on wine tourists’ profile is also provided.

To the best of the authors’ knowledge, this is among the first studies exploring how positive and negative drivers act synergically in affecting wine tourism intentions after the Covid-19 outbreak.