Rao Zheng, Kui‐Sheng Wang and Yun Wang
Emergence is the kernel concept of complexity science. Lack of precision when people refer to “emergent properties” hinders the research of complex systems. The purpose of this…
Abstract
Purpose
Emergence is the kernel concept of complexity science. Lack of precision when people refer to “emergent properties” hinders the research of complex systems. The purpose of this paper is to develop a formal definition of emergence to make it intrinsic to a system and to integrate different views on emergence.
Design/methodology/approach
Based on the modeling framework of entity grammar systems (EGS), a formal definition of emergence is proposed and a theorem is obtained for exploring the producing conditions of emergence. With the definition and theorem, three views on emergence are unified using the same formalism of EGS.
Findings
The concept of emergence can be formally defined in the framework of EGS to integrate the “downward causation” and “upward causation” views of emergence and makes emergent properties intrinsic to a system. It is possible to control the production of emergence when the system is analyzed using the formalism of EGS.
Originality/value
A formal definition of emergence is proposed in this paper. This work combines the modeling power of EGS with the formal analysis of emergence, which will prompt the further application of EGS in modeling, simulation, and analysis of complex systems in many fields and will provide practical tools for complexity research.
Details
Keywords
Nayanthara De Silva, Malik Ranasinghe and C.R. De Silva
Artificial neural network (ANN) has been used for risk analysis in various applications such as engineering, financial and facilities management. However, use of a single network…
Abstract
Purpose
Artificial neural network (ANN) has been used for risk analysis in various applications such as engineering, financial and facilities management. However, use of a single network has become less accurate when the problem is complex with a large number of variables to be considered. Ensemble neural network (ENN) architecture has proposed to overcome these difficulties of solving a complex problem. ENN consists of many small “expert networks” that learn small parts of the complex problem, which are established by decomposing it into its sub levels. This paper seeks to address these issues.
Design/methodology/approach
ENN model was developed to analyze risks in maintainability of buildings which is known as a complex problem with a large number of risk variables. The model comprised four expert networks to represent building components of roof, façade, internal areas and basement. The accuracy of the model was tested using two error terms such as network error and generalization error.
Findings
The results showed that ENN performed well in solving complex problems by decomposing the problem into its sub levels.
Originality/value
The application of ensemble network would create a new concept of analyzing complex risk analysis problems. The study also provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyze maintainability risks of buildings at early stages.
Details
Keywords
Bai Yun, Zhao Yue and Zhou Yaolin
This study aims to identify the prominent topics, the distribution and association characteristics of topics and the topic evolutionary trends of Documentary Heritage Preservation…
Abstract
Purpose
This study aims to identify the prominent topics, the distribution and association characteristics of topics and the topic evolutionary trends of Documentary Heritage Preservation and Conservation (DHPAC) research in China.
Design/methodology/approach
Keywords of relevant papers in China National Knowledge Infrastructure (CNKI) were extracted as the data source in this study. First, frequency and co-occurrence of keywords of the selected papers were obtained by using SATI. Second, co-word network indicators were calculated with the Pajek software. Then, VOSviewer was applied to optimize the visualization of the sub-communities. Finally, a topics evolution map of this research field was implemented by CorTexT.
Findings
The research topics of DHPAC research in China were unbalanced but distinct. Topics of DHPAC research in China possessed inconspicuous orientation and consistency. The core topics had less influence on the overall network. A research system had formed with archival conservation and ancient books conservation as the core research directions. Research in this field had formed four continuous evolutionary paths about ancient books conservation, salvage conservation, archival conservation and archives conservation technology science with topics fusion and differentiation coexisting. Attentions on “ancient books conservation”, “paper relics conservation”, “electronic record”, “digitization”, “minority”, “documents in the republic of China” had increased during the past two decades and new hot topics of DHPAC research kept appearing in China.
Originality/value
This study synthesized and analyzed the research results of DHPAC research in China from a more comprehensive perspective and revealed the topic structure and longitudinal evolution process intuitively with co-word analysis and social network analysis, which can assist researchers to improve research systematization, discover new research directions and seek cooperative research path.
Details
Keywords
The purpose of this paper is to develop some picture uncertain linguistic aggregation operators based on Bonferroni mean operators, which is combined with multiple attribute…
Abstract
Purpose
The purpose of this paper is to develop some picture uncertain linguistic aggregation operators based on Bonferroni mean operators, which is combined with multiple attribute decision-making (MADM) and has applied the proposed MADM model for selecting the service outsourcing provider of communications industry under picture uncertain linguistic environment.
Design/methodology/approach
The service outsourcing provider selection problem of communications industry can be regarded as a typical MADM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator.
Findings
The results show that the proposed model can solve the MADM problems within the context of picture uncertain linguistic information, in which the attributes are existing interaction phenomenon. Some picture uncertain aggregation operators based on Bonferroni mean have been developed. A case study of service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the picture uncertain linguistic decision information in which the attributes are not independent so as to select the most suitable supplier.
Research limitations/implications
The proposed methods can solve the picture uncertain linguistic MADM problem, in which the interactions exist among the attributes. Therefore, it can be used to solve service outsourcing provider selection problems and other similar management decision problems.
Practical implications
This paper develops some picture uncertain aggregation operators based on Bonferroni mean and further presents two methods based on the proposed operators for solving MADM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.
Social implications
It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.
Originality/value
The paper investigates the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator and develops the picture uncertain linguistic Bonferroni mean operator and picture uncertain linguistic geometric Bonferroni mean operator, picture uncertain linguistic weighted Bonferroni mean operator and picture uncertain linguistic weighted geometric Bonferroni mean operator for aggregating the picture uncertain linguistic information, respectively. Finally, a numerical example concerning the service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods.
Details
Keywords
Chiranjit Das and Sanjay Jharkharia
The purpose of this paper is to review the relevant literature on low carbon supply chain management (LCSCM) and classify it on contextual base. It also aims at identifying key…
Abstract
Purpose
The purpose of this paper is to review the relevant literature on low carbon supply chain management (LCSCM) and classify it on contextual base. It also aims at identifying key decision-making issues in LCSCM. This paper also highlights some of the future challenges and scope of research in this domain.
Design/methodology/approach
A content analysis is carried out by systematically collecting the literature from major academic sources over a period of 18 years (2000-2017), identifying structural dimensions and classifying it on contextual base.
Findings
There is an increasing trend of research on LCSCM, but this research is still in a nascent stage. All supply chain functions such as supplier selection, inventory planning, network design and logistic decisions have been redefined by integrating emissions-related issues.
Research limitations/implications
Limitation of this study is inherent in its unit of analysis. Only peer-reviewed journal articles published in English language have been considered in this study.
Practical implications
Findings of prior studies on low carbon inventory control, transportation planning, facility allocation, location selection and supply chain coordination have been highlighted in this study. This will help supply chain practitioners in decision making.
Originality/value
Though there are an increasing number of studies about carbon emission-related issues in supply chain management, the present literature lacks to provide a review of the overarching publications. This paper addresses this gap by providing a comprehensive review of literature on emissions-related issues in supply chain management.
Details
Keywords
Recently, the spread of malicious IT has been causing serious privacy threats to mobile device users, which hampers the efficient use of mobile devices for individual and…
Abstract
Purpose
Recently, the spread of malicious IT has been causing serious privacy threats to mobile device users, which hampers the efficient use of mobile devices for individual and business. To understand the privacy security assurance behavior of mobile device users, this study aims to develop a theoretical model based on technology threat avoidance theory (TTAT), to capture motivation factors in predicting mobile device user’s voluntary adoption of security defensive software.
Design/methodology/approach
A survey is conducted to validate the proposed research model. A total of 284 valid survey data are collected and partial least square (PLS)-based structural equation modeling is used to test the model.
Findings
Results highlight that both privacy concern and coping appraisal have a significant impact on the intention to adopt the security defensive software. Meanwhile, privacy security awareness is a crucial determinant to stimulate mobile device user’s threat and coping appraisal processes in the voluntary context. The results indicate that emotional-based coping appraisal of anticipated regret is also imperative to arouse personal intention to adopt the security tool.
Practical implications
This result should be of interest to practitioners. Information security awareness training and education programs should be developed in a variety of forms to intensify personal security knowledge and skills. Besides, emotion-based warnings can be designed to arouse users’ protection behavior.
Originality/value
This paper embeds TTAT theory within the mobile security context. The authors extent TTAT by taking anticipated regret into consideration to capture emotional-based coping appraisal, and information security awareness is employed as the antecedent factor. The extent offers a useful starting point for the further empirical study of emotion elements in the information security context.
Details
Keywords
Hasnae Zerouaoui, Ali Idri and Omar El Alaoui
Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality…
Abstract
Purpose
Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.
Design/methodology/approach
The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.
Findings
Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.
Originality/value
The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.
Details
Keywords
The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the…
Abstract
Purpose
The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently.
Design/methodology/approach
In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities.
Findings
The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies.
Originality/value
This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.
Details
Keywords
Sudipta Ghosh, Madhab Chandra Mandal and Amitava Ray
The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using…
Abstract
Purpose
The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.
Design/methodology/approach
The literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.
Findings
The result shows that “Total CO2 emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.
Research limitations/implications
This research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.
Practical implications
The methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.
Originality/value
The design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.
Details
Keywords
AbdulRahman Asaad and Sameh Monir El-Sayegh
This paper aims to identify and assess the key criteria for selecting green suppliers in the United Arab Emirates (UAE) construction industry.
Abstract
Purpose
This paper aims to identify and assess the key criteria for selecting green suppliers in the United Arab Emirates (UAE) construction industry.
Design/methodology/approach
A total of 20 criteria were identified and shortlisted through an extensive literature review. These criteria were grouped into four categories: technical and commercial bid, company characteristics, environmental and socioeconomic. A questionnaire was then developed and distributed to construction professionals in the UAE. A total of 39 professionals responded to the survey including contractors, consultants, owners and suppliers. The respondents performed pairwise comparisons among the selection criteria. Data was then analyzed using the Expert Choice Software.
Findings
The research findings highlighted that the technical and commercial bid category was ranked as the most important with a weight of 0.338, followed by socioeconomic, company characteristics and environmental categories weighing 0.239, 0.225 and 0.199, respectively. The UAE construction professionals also ranked health and safety, material’s quality and tender price as the top three most important criteria when selecting a sustainable supplier.
Practical implications
This research addresses the lack of literature toward green supplier selection in the UAE. In addition, it assists contractors in selecting the appropriate supplier and promotes sustainable practices in the construction industry.
Originality/value
Material suppliers play an important role in the successful delivery of construction projects. Selecting the appropriate supplier is of paramount importance to project success. Several methods can be used to evaluate and select the best-fit suppliers. However, the selection criteria in such methods are primarily based on traditional construction projects rather than sustainable construction projects. Recently, there is an increase in the number of sustainable construction projects in the UAE. Therefore, identifying and assessing the key criteria for selecting green suppliers is needed. This paper fills the gap in literature as to selecting green suppliers in construction projects.