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 purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

With increasing pressure from the government and private sectors to be more environmentally and socially responsible, sustainable supplier selection has gained enormous currency in recent times. Particularly, in the case of the construction industry, owing to a large amount of industrial wastage generated and extreme workplace conditions, it is even more important to devise strategies to mitigate the harmful consequences. The most crucial step in this regard is the selection of sustainable suppliers that acquire a pivotal position in the supply chain ecosystem. This study aims to identify indicators for three criteria such as economic, environmental and social, and prioritize them according to their level of significance for sustainable supplier selection in the Indian construction industry.

In this study, the best-worst method (BWM) is presented for sustainable supplier selection in Indian construction organizations. Total of 27 indicators was identified for the three criteria of the triple bottom line (TBL) approach namely economic, environmental and social. Using BWM, the most important criterion was found and subsequently, all the indicators under each criterion were ranked in order of importance.

The analysis revealed that the environmental criterion was the most important criteria in the sustainable supplier selection followed by the economic criteria. The indicators that were the most influential in the effective selection process were “Usage of recyclable raw materials for production”, “Adoption of clean and green technologies”, “Waste management” and “Periodical environmental audits” under the environmental criteria; “Efficiency”, “Cost” and “Flexibility and Scalability” from the economic criteria; and “Safety programs” and “Information disclosure” in the social criteria.

The study provides a reference framework for the selection of sustainable suppliers in construction organizations. The findings can also be used for the assessment of suppliers' performance in the supply chains.

The novelty of this work lies in its attempt to model the performance of suppliers in the Indian construction supply chains.

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

Growing concern over sustainability adoption has presented an array of challenges to businesses. While vital to an economy's success, banking is not immune to societal, environmental and economic consequences of business practices. The study has examined the sustainable performance of banking institutions on the suggested multidimensional framework comprising economic, environmental, social, governance and financial dimensions and 52 sustainability indicators. The study benchmarks the significant performance indicators of leading banks indispensable to sustainable banking performance. The findings attempt to address research questions concerning the extent of sustainable banking performance, ranking the sustainability dimensions and indicators and standardizing sustainability adoption metrics.

To determine the responsiveness of the banking industry to sustainability dimensions, content analysis was conducted using NVivo software for the year 2021–2022. Furthermore, a hybrid multicriteria decision-making (MCDM) approach is used by integrating entropy, the technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija KOmpromisno Resenje (VIKOR) to provide relative weights to performance indicators and prioritize banks based on their sustainable performance. Sensitivity analysis is used to ensure the robustness of results.

In the context of the Indian banking industry, the pattern of sustainability reporting is inconsistent and concentrated on addressing environmental and social concerns. The results of the entropy methodology prioritized “Environmental” sustainability over other selected dimensions while “Financial” dimension has been assigned the least priority in the ranking order. The significant sustainable performance indicators delineated in this study should be used as standards to ensure the accountability and credibility of the sustainable banking industry. Additionally, the research findings will provide valuable inputs to policymakers and regulators to assure better contribution of the banking sector in meeting sustainability goals.

Considering the paucity of studies on sustainable banking performance, this study makes two significant contributions to the literature. First, the suggested multidimensional disclosure model integrating financial and nonfinancial indicators would facilitate banking institutions in addressing the five aspects of sustainability. As one of the first studies in the context of the Indian banking industry, the findings would pave the way for better diffusion of sustainability practices. Second, the inclusion of MCDM techniques prioritizes the significance of sustainability indicators and benchmarks the performance of leading banks to achieve better profits and more substantial growth.

This study aims to develop a benchmarking model with productivity, management, and sustainability indicators (PMS), measure the performance of furniture firms in Vietnam, explore the causes of performance gaps, and identify the barriers and factors of benchmarking practice.

The article uses both qualitative and quantitative methods. Literature review, exploratory interviews and a grounded-theory process are employed to develop a benchmarking framework and identify performance gaps, barriers and factors of benchmarking practice. The PMS benchmarking model and quantitative analysis are utilized to assess performance indicators.

The study proposes the PMS benchmarking model and measures performance indicators of furniture firms. The sources of performance gaps are explored as design, material supply, the economy of scale, market, management systems and openness. Benchmarking practice encounters barriers of difficult indicators, unsuitable firms, insufficient benchmarking knowledge, reluctance to share data, unavailable and unreliable data, and weak engagement. Benchmarking practice is determined by core factors: leader; internal factors: systems, engagement, strategy, scope, culture; external factors: customers, suppliers, associations, support, competition.

Firms could learn benchmarking indicators and the causes of these gaps to improve their performance. When implementing a benchmarking study, scholars and practitioners need to pay attention to barriers and factors of the benchmarking practice to ensure effective results.

This study develops the PMS benchmarking model and estimates performance indicators in an emerging country with the performance gap justification. It provides readers with benchmarking barriers with solutions and success factors of benchmarking practice.

This study aims to discover the key performance indicators (KPIs) of the agricultural cold supply chain (ACSC) and analyze their consequences on the performance of ACSC within the bounds of Indian topography.

The KPIs have been explored based on the literature review both in global and Indian context and domain expert's opinions. The interdependency characteristics and cause–effect relationship among the KPIs have been analyzed using a fuzzy decision-making trial and evaluation laboratory (f-DEMATEL) approach.

The findings extracted from the empirical assessment of the problem find strong compliance with the notions of theoretical model assessment. The results highlight that the cost of product waste and operating and performance costs are the two most important performance indicators of an Indian ACSC. Furthermore, governmental policies and regulations and the effectiveness of cold chain (CC) equipment also have a high degree of influencing characteristics on ACSC performance.

To connect the study with practicalities, the assessment of the KPIs is allied with real-time practices by clustering the beliefs of Indian professionals. Therefore, the decision-making behavior of the experts might be influenced by geographical constraints. However, the key findings provide advantages to the ACSC players, a bright hope for future food security and a significant profit for farmers.

The presented paper encompasses various aspects of the ACSC, including theoretical and empirical perspectives exercised to contemplate the system dynamics, which inculcates the essence of the associated practicalities. Thus, this study has various practical contributions relevant to managerial and societal perspectives.

The study of sustainability within universities is recognized as essential for debates and research; in the long term, the “sustainable university” concepts can contribute to sustainability from a larger perspective. This study aims to propose a conceptual model for evaluating the students’ sustainability considering their interactions with the university and the environment. The proposed model is titled Sunshine model. It is applied to students of the La Salle University, Peru.

The model combines academic performance, happiness and the ecological footprint to quantify university students’ sustainability. A structured questionnaire survey was elaborated and applied to get the raw data that feeds the three methods. The students’ average grades evaluate academic performance. Happiness is quantified by the happiness index method, and the ecological footprint is measured by the demand for food, paper, electricity, transport and built-up areas. Results are evaluated under both approaches, overall group performance and clusters.

The proposed model avoids misleading interpretations of a single indicator or discussions on sustainability that lack a conceptual model, bringing robustness in assessing students’ sustainability in universities. To have a low ecological footprint, the student needs to need up to 1 planet for their lifestyle, be considered happy with at least 0.8 (of 1) for happiness index, and have good academic performance with at least a grade of 7 (of 10) in their course. Regarding the case study, La Salle students show a high academic grade degree of 7, a high level for happiness index of 0.8 and low performance for ecological footprint by demanding 1.8 Earth planets, resulting in an “environmentally distracted” overall classification for students with 2019 data. From a cluster approach, 81% of evaluated students (n = 603) have low performance for ecological footprint, whereas 31% have low performance for indicators of recreational activities of happiness. Changing lifestyles and making more recreational activities available play crucial roles in achieving higher sustainability for the La Salle students.

The happiness assessment questionnaire can be subject to criticism, as it was created as a specific method for this type of audience based on existing questionnaires in the literature. Although it can be seen as an important approach for diagnoses, the proposed model does not consider the cause–effect aspect. The decision-maker must consider the sociocultural aspects before implementing plan actions.

University managers can better understand why university students have high or low sustainability performance and provide more effective actions toward higher levels of students’ sustainability.

The proposed model, Sunshine model, overcomes the single-criteria existing tools that access the sustainability of universities. Rather than focusing on university infrastructure, the proposed model focuses on the students and their relationship with the university.