Search results

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

Due to a change in higher education and adult education ideas and practices globally that have become more learner-centered, higher education is undergoing a transformation at a rate never before seen. Education has also evolved into a lifetime endeavor as the importance of higher education and adult learning has grown. In light of the fact that it offers guidance on how people can find purpose in their lives, transformative learning theory has a prominent position in higher education and adult education. By critically examining their presumptions and expectations and updating them to support higher education students' successful learning, educators can transform their theory and practice of instruction through active and transformative learning. Adapting to the changing capacities brought on by digitization, technological advancements, growing technological connectivity, global market expansion, mobility and migration, and workplace diversity is becoming more and more difficult for higher education institutions. The idea of active and transformative learning and transformative learning strategies are discussed in detail in this chapter to help readers understand their importance and function in effective teaching and learning in the transforming world of higher education. This chapter's major contribution to Active and Transformative Learning: Digital Transformation in Education is the provision of a comprehensive guide and strategy on how to successfully incorporate digital technologies into the teaching and learning process in order to improve student engagement, knowledge acquisition, and the growth of critical thinking skills.

The effects of the spread of COVID-19 across the world are devastating, both from a health and an economic perspective. However, we also see encouraging examples of collaborative and innovative initiatives, in society and in organizations. The purpose of this paper is to focus on initiatives related to digital business model innovation. The authors explore how organizational characteristics provide a variety of opportunities for digital responses to the COVID-19 pandemic and discuss the potential consequences for the speed of digital transformation in organizations and society.

In this paper, the authors analyze how organizations attempt to mitigate the negative effects of fighting COVID-19 using digital business model responses. The authors draw on a qualitative study where they have collected data from the retail and service industries. They have analyzed the data in relation to theory to better understand this ongoing phenomenon.

The authors have identified four categories of organizations (crisispreneurs, accelerators, endurers and thrivers). Each category faces different challenges and shows a different intensity in their digital transformation. The authors propose that the rapid turn toward digital business models will have enduring effects, as organizations have gained transformational capabilities that will remain, and that the digital trajectory has, as a result, changed forever.

The findings in this paper point toward new challenges for leaders and policymakers in terms of how to support initiatives and meet the needs of different categories of organizations while simultaneously being conscious of the potential societal effects of this rapid digital shift. The authors hope that this paper can be of value for managing this shock and learning how to adapt for the future taking certain aspects of current business models as the departure point.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

The purpose of this paper is to propose a framework of green strategies as a combination of energy-efficiency measures and solutions towards environmental impact reduction for improving environmental sustainability at logistics sites. Such measures are examined by discussing the related impacts, motivations and barriers that could influence the measures' adoption. Starting from the framework, directions for future research in this field are outlined.

The proposed framework was developed starting from a systematic literature review (SLR) approach on 60 papers published from 2008 to 2022 in international peer-reviewed journals or conference proceedings.

The framework identifies six main areas of intervention (“green strategies”) towards green warehousing, namely Building, Utilities, Lighting, Material Handling and Automation, Materials and Operational Practices. For each strategy, specific energy-efficiency measures and solutions towards environmental impact reduction are further pinpointed. In most cases, “green-gold” measures emerge as the most appealing, entailing environmental and economic benefits at the same time. Finally, for each measure the relationship with the measures' primary impacts is discussed.

From an academic viewpoint, the framework fills a major gap in the scientific literature since, for the first time, this study elaborates the concept of green warehousing as a result of energy-efficiency measures and solutions towards environmental impact reduction. A classification of the main areas of intervention (“green strategies”) is proposed by adopting a holistic approach. From a managerial perspective, the paper addresses a compelling need of practitioners – e.g. logistics service providers (LSPs), manufacturers and retailers – for practices and solutions towards greener warehousing processes to increase energy efficiency and decrease the environmental impact of the practitioners' logistics facilities. In this sense, the proposed framework can provide valuable support for logistics managers that are about to approach the challenge of turning the managers' warehouses into greener nodes of the managers' supply chains.

This paper aims to evaluate the performance of the Mashhad No. 5 wastewater treatment plant (WWTP) using a combination of data mining (regression) algorithms and artificial neural networks.

In this research, the performance of WWTP located in Mashhad, Iran, has been evaluated using two data mining models, neural network and regression model.

The proposed model has the potential of implementing in other WWTPs in Iran or other countries.

The authors would also like to thank Mashhad No.5 WWTP for data access.