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This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

This paper proposes a new typology of Ownership (O) advantages as a function of their differential managerial implications in established multinational enterprises (MNEs). We argue that the mainstream typology of O advantages proposed in Dunning’s eclectic paradigm does not recognize the uniqueness of individual firms. We therefore propose a new typology of O advantages, which distinguishes among four types, based on the geographic source of such advantages and their transferability across borders. Moreover, we acknowledge the importance of resource recombination advantages. Two case examples illustrate the implications of the new typology for established MNEs.

To realise the shared development of the digital economy, people need to transcend the capital logic and advocate the logic of cooperative development, i.e. “co-construction, benefit-sharing and co-governance”. This study aims to discuss the aforementioned statement.

Platform economy is a new economic form produced by the transformation of the social production patterns in the era of digital capitalism. In the neo-imperialist stage, a new stage of capitalist development, capital logic promotes the global expansion of the platform economy and influences its development process, organisational form, contradictions and dilemmas and internal transcendence. Having the spatiotemporal chain of capital circulation repaired, the globalisation of the platform economy is reshaping how the means of production are combined with labour, affecting the local changes in the general relations of production and “international relations of production”.

In the accumulation of digital capitalism, the social contradictions and fundamental contradictions in the capitalist world have been further intensified, making exploitation, income distribution gap, monopoly and other problems increasingly severe. The imbalance and inequality in the global development of the digital economy are increasingly prominent.

Regarding the global governance of the digital economy, China, as a major responsible country, will strive to encourage all countries to co-build a community with a shared future in cyberspace. In the new international development pattern of digital economy globalisation, China must take effective measures to actively safeguard its national security and development interests to meet specific challenges.

This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

This paper analyzes the existing body of work on the relationship between depression and social media use in the information system field, including the impact of social media use on depression, the effect of depression on social media use and the association and interaction between depression and social media use.

Using the systematic review method, this study selected the Web of Science, Emerald, Science Direct, JSTOR, Wiley Online Library and Taylor and Francis Online as search databases and ended up with 29 papers that met all the authors' requirements.

This study identified five possible reasons for the inconsistencies between the findings of the selected studies. First, uses and gratifications theory has different influence mechanisms in evaluating the relationship between social media use and depression. Second, gender can moderate the impact of social media use on depression. Third, age moderates the association between social media use and depression. Fourth, for adolescents, the time spent on social media has a critical effect on their depression. Fifth, negative personality traits (e.g. rumination, envy, etc.) can play a significant role in mediating the relationship between passive social media use and depression.

This study conducted an evaluation of the relationship between depression and social media use. First, the authors summarized the research framework and main body of work covering the relationship between depression and social media use. Second, the authors proposed possible explanations for the inconsistencies between the findings. Third, the authors discussed and explained the possible influence mechanisms of the existing results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0211.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Inconel 718 is used in gas turbine engines for aerospace applications due to high creep resistance but generating a hole with good surface integrity is challenging because the γ′′ interface is very strong so that slip is difficult in the grain boundary. So, the purpose of this work is to enhance the performance of drilling using a micro texture drill tool filled with solid lubricant.

Three different micro textures such as star shaped with 6-sharp apex, rectangular slots parallel and perpendicular to drill axis are created using laser on the drill tool. Deep cryogenic treatment is done on the textured tool to improve the strength and wear resistance before it is filled with solid lubricant. A detailed experimental investigation is performed to analyse the hole geometry and surface integrity of the drilled hole.

The accuracy of the drilled holes is enhanced in the star shaped texture drill tool over other textured and non-textured tools. A significant improvement in surface finish and hardness are observed and moreover cylindricity error, burr height of the hole is less for the above condition. It is also inferred that, at lower feed rate and higher speed produce hole with an accuracy of 96%.

Aerospace industry is focussing on improving the hole geometry and surface in Inconel 718. This work demonstrates the novel technique to improve drilling of Inconel 718 using laser textured tool filled by the solid lubricant.

Ti6Al4V is a commonly used titanium alloy with several applications in aerospace industry due to its excellent strength to weight ratio. But due to low thermal conductivity, it is categorized as “difficult to machine.” Though machinability can be improved with cutting fluids, it is not preferred due to associated problems. This study aims at eliminating the use of cutting fluid and finding an alternate solution to dry machining of Ti6Al4V. AlTiN coated tools provide good heat and oxidation resistance but have low lubricity. In the present work, graphene, which is known for lubricating properties, is added to the tools using five different methods (tool condition) to form graphene self-lubricated cutting tools.

Graphene-based self-lubricating tools are prepared by using five methods: dip coating (10 dips and 30 dips); drop casting; and filling of micro/macroholes. Performance of these tools is evaluated in terms of cutting forces, surface roughness and tool wear by machining Ti6Al4V and comparing with conventional coated cutting tool.

Self-lubricating tool with micro holes filled with graphene outperformed other tools and showed maximum decrease of 33.42% in resultant cutting forces, 35% in surface roughness (Ra) and 30% in flank wear compared to conventional cutting tool.

Analysis of variance for all forces show that tool condition and machining time have significant influence on all components of cutting forces and resultant cutting forces.

This study aims to focus on the impact of multi-level knowledge sharing between and within organizations on the risk control of rural inclusive finance. The paper presents a synergistic risk control system integrating external and internal factors for rural inclusive finance by constructing different knowledge-sharing platforms in an environment, which is full of many uncertainties.

This study is based on survey methods. To achieve the research objectives, the authors adopt a single case study approach. For data collection, the authors apply a wide variety of methods such as semi-structured interviews, field visits, second-hand databases and official websites.

The results emphasize that using multi-level knowledge sharing such as the inter- and intra-organizational level, can facilitate the risk control of rural inclusive finance during the post-COVID-19 era. Furthermore, it is also noted that achieving knowledge sharing at different levels by building diverse knowledge-sharing platforms can promote the risk control of rural inclusive finance from the individual-organization level to the chain level of multi-organization collaboration, which contributes to the formation of symbiotic risk control ecology.

The authors have formed the “Chinese wisdom” to deal with inclusive financial risks and to promote in-depth development in relation to the “last mile” practice of inclusive finance, which means the final and the most important phase of a project. The conclusions contribute to enriching the outcomes regarding the risk control of rural inclusive finance, provide experiences to its sustainable development and offer a reference to other countries with their risk control of rural inclusive finance.

Drawing on the knowledge-sharing approach, this study creatively resolves the persistent problems in the risk control of rural inclusive finance, which forms a powerful supplement to the extant literature. Meanwhile, the paper combines the two contextual factors of the post-COVID-19 era and emerging economies, which can be deemed as a novel attempt.