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Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

The aim of this research is to provide an approach for modeling system risk management and to develop an analytic hierarchy process (AHP)-based model for simulating decisions on introducing innovations in air transport systems.

The paper proposes AHP and analytic network process (ANP) methodologies for overcoming fragmentation in risk assessments perceived by risk, budget, quality or schedule managements, and for resolving potential conflicts between safety, efficiency and well-being.

Issues in system risk evaluation and management were identified and transferred to a list of requirements. A generic ANP-based model for system risk management was developed as well as a template for capture of knowledge on risks, including expert knowledge, and for implementation of a new decision-making process as applied to introducing innovation(s).

Since this research addresses evaluation and management of non-event based risks due to innovations in air transport systems, further analysis and re-evaluation of risks is required during and after the implementation in order to provide continuously dynamic representation of system risk.

The results of this study contribute to the development and implementation of a usable version of multi-criteria decision analysis at senior management level. Further, it stimulates mechanism for learning and trade-offs between various stakeholders.

This work is original in that it is cross-disciplinary (e.g. risk management, management of innovation, systems design). It addresses the issue of integrating a safety management system with an overall business management system. Also, it introduces qualitative non-event risk assessments into system risk management. In addition to the use of the AHP-model for system risk management, an implementation model (“risk stakeholder model”) is also developed.

Decision system analysis is a conceptually simple technique that maps the process of group decisions over time. The data is gathered in a variety of ways, but most often some form of protocol analysis is the foremost tool. The data is then condensed and depicted as a flowchart for a specific decision. If several such flowcharts can be assembled within an industry, they can be melded together to form a generic guide that is very useful to practitioners and very interesting to theorists. Here, a brief history of the development of the technique leads to a description of the process. This is followed by a comparison to cognitive mapping (a similar technique applied to mapping thought processes rather than physical processes), and an illustrative longitudinal example of DSA.

Decision support systems (DSS) is the mathematical‐model building tool of business environments. Artificial intelligence (AI) is the tool that will hopefully help computers make better business decisions by allowing the computer to learn from its experiences. The combination of DSS and AI takes us out of the theoretical by integrating the analytical tools into the management decision process, thereby making the decisions practical and useful. Discusses the integration of DSS and AI into the business decision‐making process.

Companies are becoming more and more dependent on data processing systems. In this context, cybernetics again finds its meaning dealing with man‐machine communication problems. The two professions (data‐processing and management) have to find an answer to the same question: how to present a model of a company as a complex system? Decision system analysis follows this trend and suggests a set of concepts and approaches that allows us to analyze and conceive complex systems, whether they be human organizations or systems of artefacts. Despite the fact that it belongs to the cybernetic trend of thinking, the analysis integrates such notions as culture, creativity, well‐being, goals, real time, suggesting a more complete vision of these aspects starting from a logical deductive model, to a renewed cybernetic vision.

This paper aims to focus on the decision-making process of integrated system design. Buildings can benefit from different system integration working toward the unified goal of providing the needed conditions and improving the comfort level of occupants. It is important to engage all system needs and priorities in the design by keeping goal into consideration. Even though there is vast potential in the coordination of system design decisions, there is a need to increase the transparency of the decision-making process by developing methods to incorporate multi-dimensional design attributes.

This is achieved by considering all system design priorities with respect to decision attributes, as well as the inter-system inputs based on information and knowledge. Data were collected through interviews, collaboration meetings and design document reviews, which helped to facilitate triangulation.

This paper presents the findings of a case study of deep retrofit design process that seeks to reduce energy consumption through integrated system decisions with several system combinations. In addition, such design decisions highlighted the fact that the values need to be flexible at the system level.

This paper presents an in-depth analysis of a single case study. Multiple case studies are being investigated for the future of this research.

This paper presents the methods used for integrated design process priorities that will enable design teams to make decisions that lead to improved energy performance in retrofit projects.

The case study building in this paper is a showcase building with cutting edge technologies and techniques, as well as a scalable and collaborative design process. It is an example of a best-in-class retrofit process designed through whole building design principles within the target budget. The paper demonstrates system design selection criteria that are embraced by value prioritization.

Reconfigurable vibrating screen (RVS) is an innovative beneficiation machine designed at Tshwane University of Technology, Republic of South Africa (RSA); with adjustable screen structure to ensure sorting, sizing and screening of varying mineral particles (sizes and quantities) demanded by the customers in a cost-effective manner through the screen structure geometric transformation. In order to ensure that this machine is optimally maintained and managed when utilized in surface and underground mining industries, there is a need to establish or ascertain the best maintenance practices that would be used in optimally managing the RVS machine using decision making techniques. In view of this, the purpose of this paper is to ascertain the best maintenance practices that would be used to optimally maintain and manage the RVS machine when used in surface and underground mines.

Decision making techniques such as weighted decision matrix (WDM) and analytical hierarchy process (AHP) were used in this research work to establish the best maintenance practice for optimally maintaining and managing the RVS machine using relevant literature survey on maintenance management systems as well as the different maintenance criteria decision indices obtained from different conventional vibrating screen machine manufacturers and maintenance experts.

Based on the results obtained from the WDM analysis, it was anticipated that e-maintenance (e-M) system embedded with diagnosing and prognosing algorithms; with a cumulative weight score of 2.37 is the best maintenance practice for managing the RVS machine when used in surface mines, while AHP with deeper decision making analysis anticipated that the robotic-driven maintenance (RM) system with an important decision criteria; safety, and a cumulative hierarchy score of 28.6 percent, supported by e-M management system with a cumulative hierarchy score of 17.6 percent are the best maintenance mix that could be used in optimally maintaining and managing the RVS machine, when used in a craggy and hazardous underground mining environment.

To this effect, it could be anticipated that e-M management system (endowed with the ability to detect fault on the machine, diagnose and prognose the different subsystems of the RVS machine and ascertain the reconfiguration time and process of the RVS machine in recovering production loss during the maintenance of the machine as well as meeting customers demand, etc.) is the best maintenance practice for optimally maintaining the RVS machine when utilized in surface mines while both e-M management system and RM management system (endowed with the ability to carry out automated maintenance tasks achievement under little or no maintenance manager intervention) are also anticipated as the best customized maintenance practices mix that could be used in optimally maintaining the RVS machine, when used in dangerous and hazardous underground mining environment.

This maintenance management system evaluation and selection for optimal RVS machine functionality will serve as a useful information to different mining machines (and other related machines) maintenance managers, in selecting the best maintenance management system for ensuring optimal functionality, reliability and maintainability of machines used in their industries.

Decision analysis, an approach developed to lend support in applying the concepts of decision theory, is particularly relevant for structuring and analysing complex decisions in which uncertainty plays a major part.

Noting the scarcity of complexity techniques applied to modelling social systems, this paper attempts to formulate a conceptual model of decision‐making behaviour within the information systems evaluation (ISE) task, against the backdrop of complexity theory.

Complexity theory places an emphasis on addressing how dynamic non‐linear systems can be represented and modelled utilising computational tools and techniques to draw out inherent system dynamics. In doing so, the use of fuzzy cognitive mapping (FCM) and morphological analysis (MA) (hence a fuzzy‐morphological approach), is applied to empirical case study data, to elucidate the inherent behavioural and systems issues involved in ISE decision making within a British manufacturing organisation.

The paper presents results of applying a combined FCM and MA approach to modelling complexity within management decision making in the ISE task: both in terms of a cognitive map of the key decision criteria; a matrix of constraint criteria; and a synthesised model that provides an indication of the linkages between technology management factors and organisational imperatives and goals. These findings show the usefulness of viewing the topic in complexity science terms (emergent behaviour, non‐linearity and chaotic response).

This research is limited in applying the given technique to a single case study organisation in the UK manufacturing sector, where the sample size is limited. Since this is the first time that such a combined MA‐FCM technique has been used in this field known to the authors, future research needs to validate and explore the implications of this approach in a wider context (multiple organisations and viewpoints).

The paper highlights the need for those involved in analysing managerial decision making to include aspects of complexity theory in their evaluations – namely uncovering inherent inter‐relationships that may exist between stakeholders, processes and systems. In doing so, expanding the manager's understanding of how to achieve congruence between driving forces and factors, which may exhibit non‐linear, chaotic or feedback behaviour.

The given research brings together both artificial intelligence and operational research techniques, applied in the socio‐technical milieu of information systems evaluation, within the context of complexity theory, in order to describe the rich detail within the ISE decision‐making task.

Lean manufacturing (LM) has attracted the attention of industries all over the world. Many operation managers have implemented or will be implementing LM because of the benefits reported by other companies or because their customers have demanded it. This paper aims to present a case study of a medium‐sized valve manufacturer in which the decision of implementing LM is made by analyzing the capabilities, practices, tools and techniques of alternative manufacturing systems apart from understanding its effect on the decision areas of the operations department.

A multi attribute decision making (MADM) model, namely, the analytic network process (ANP) has been used for this purpose, which structures the problem related to selection of alternative manufacturing systems in a hierarchical network form. In this problem, it links the performance measures or competitive priorities, decision areas, and the elements with alternatives available to the decision maker and provides a holistic framework for the selection of alternative manufacturing systems to achieve the competitive priorities of the organisation under study.

From an extensive analysis of the results, under the given circumstances, it is evident that implementation of a lean manufacturing system (LMS) is a better alternative, as it would result in overall improvement in the performance of an organisation in comparison with the alternatives.

This paper demonstrated a step‐by‐step approach of the ANP model using a case study of a small‐ and medium‐sized enterprise, which makes it more suitable for managers to learn and adopt such MADM models to support their decisions.

To the author's knowledge, there is no paper available in the existing literature that discusses the application of ANP in the field of LM.