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The use of unmanned aerial vehicles (UAVs) has significantly increased in the past decade and nowadays is being used for various purposes such as image processing, cargo transport, archaeology, agriculture, manufacturing, health care, surveillance and inspections. For this reason, using the appropriate image processing method for the intended use of UAVs increases the study’s success. This study aims to determine the most suitable one among the innovative methods that constitute the image processing system for a UAV to be used for surveillance purposes.

Analytical hierarchy process has been used in the solution of the decision problem to be handled in three stages, namely, platform, architecture and method. The most suitable alternative and the effect weights of these criteria results were determined at each stage.

As a result of this study, Jetson TX2 was determined as the most suitable embedded platform, ResNet is the optimum architecture and Faster R-convolutional neural networks was the best method in the image processing layer for a system that will provide surveillance with image processing method using UAV.

In UAV designs, where multiple hardware and software choices and system combinations exist, multi-criteria decision-making (MCDM) approaches can be used as a system decision mechanism.

The novelty of this work comes from the application of MCDM methods that are used as a multi-layered decision mechanism in UAV design.

This study establishes a set of seafood processing circular supply chain capabilities (CSCCs) in Vietnam using qualitative data analytics. This study specifies the interrelationships and hierarchical structure comprising six aspects and 24 criteria for the seafood processing circular supply chain in Vietnam.

Fuzzy Delphi method is used to confirm the validity. Fuzzy set theory is used to deal with the complexity and uncertainties from the qualitative information. The decision-making trial and evaluation laboratory method is used to examine the interrelationships among attributes. The analytical network process segregates (or displays) the capabilities in a hierarchical structure.

The results show that management control and technological capability dominate in circular design, circular sourcing, circular production and resource recovery. In practices, the strategic planning, action planning, information technology and technological facilities are important to seafood processing industry.

The CSCCs are pivotal in establishing a concrete foundation for the execution of circular supply chain management, with the aim of optimizing resource utilization and eliminating waste; however, prior studies have lacked a focus on the capability associated interrelationships and hierarchical structure in qualitative data analytics.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This study integrates economic/ecology (eco)-attributes and performance to build a sustainable service supply chain management (SSCM) model.

This study proposes the use of the fuzzy Delphi method to screen for the less important attributes and applies a network data envelopment analysis to explore the hierarchical and eco-efficient network interrelationships. The causality and hierarchal eco-efficient model is acquired using a fuzzy decision-making trial and evaluation laboratory analysis

The findings are as follows: (1) the information and technology management process is derived by enhancing sustainable customer and supplier relationship management, and (2) the eco-efficient model is improved based on long-term relationships with suppliers – that is, synergistic suppliers improve the service chain quality and provide services in an appropriate and timely manner – and research and development coordination. The theoretical and managerial implications are discussed.

The eco-efficient model reveals that the sustainable customer relationship management process, sustainable supplier relationship management process and information and technology management process are the major causal attributes in the model.

The eco-efficient model must be based on (1) long-term relationships with suppliers, (2) synergistic suppliers to improve service chain quality, (3) the provision of services in a timely manner and (4) research and development coordination.

Prior studies neglect to build an ecological economy model using the efficiency causality model of hierarchical interrelationships. Traditional SSCM fails to involve the triple bottom line performance toward sustainability.

To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations.

In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map.

The analytic hierarchy process, when coupled with social network analysis, can be a useful technique for developing interval measures for knowledge‐mapping purposes.

The analytic hierarchy process may become tedious and arduous for use in large social network maps. More research needs to be conducted in this area for scalability.

As social network analysis is gaining more prominence in the knowledge management community, the analytic hierarchy process may be able to provide more valuable measures to determine the strengths of relationships between actors than simply using ordinal numbers.

Coupling the analytic hierarchy process with social network analysis provides a novel approach for future knowledge‐mapping activities.

Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model.

A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives.

The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network.

This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers.

These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high.

This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.

The building and construction industry has a significant potential to reduce adverse climate change effects. There are plans to improve the natural resource use and greenhouse gas emissions caused by the buildings by choosing energy-efficient technologies, renewable energy sources and sustainable architectural and constructional elements. This study systematically reviews the patent data for climate change mitigation technologies related to buildings, aiming to detect their relative importance and evaluate each technology in the Y02B network.

The applied approach covers the process of (1) selecting high-impact technology, (2) collecting patent data from the USPTO database, (3) creating a citation frequency matrix using cooperative patent classification codes, (4) linking high-impact patents with analytical network process method, (5) limiting centrality of identifying core technologies from indicators and (6) creating a technology network map with social network analysis.

The study results show that energy-saving control techniques, energy-efficient lighting devices, end-user electricity consumption, management technologies and systems that convert solar energy into electrical energy are core solutions that reduce the effects of climate change. In addition, solutions that will support core technologies and whose effects are expected to increase in the coming years are energy-efficient heating, ventilating and air conditioning systems, smart grid integration, hybrid renewable energy systems, fuel cells, free cooling and heat recovery units and glazing technologies.

Most of the studies on patent analysis have failed to demonstrate any convincing evidence down to the lowest component groups of an entire technology network. The applied approach considers and evaluates each component included in a technology network from a holistic perspective.

This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.