A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Evaluation for Core Competence of Private Enterprises in Xuchang City Based on an Improved Dynamic Multiple-Attribute Decision-Making Model

Because Deng’s grey relational degree is inconspicuous, Deng’s relational degree with an exponential function is first presented. Then, we demonstrate that improved Deng’s relational degree is more conspicuous than the original model. Then, we construct a multiple-attribute decision-making model, based on improved Deng’s relational degree with multiple stages, and a method for determining the weight of the index is also developed. Finally, the core competence of private enterprises in Henan province is analyzed, illustrating the validity and feasibility of the improved model

AHP-Fuzzy Comprehensive Evaluation Model of Venture Investment and Financing System: Based on the Case of Incubation Base in Anhui

Establish and improve the venture investment and financing system is an important issue of economic growth in China. By reference to the stakeholder theory, we select first grade assessment indicators including government policy factor, financial institutions factor, intermediary service factor, enterprises factor and other factors (risk investment funds, insurance funds, private capital) and second grade assessment indicators. The evaluation index system was constructed to the venture investment and financing system of China. We analyze new ventures in Anhui incubation base by AHP method and fuzzy comprehensive evaluation method. The results show that: The intermediary service system performed quite well in the evaluation, while government factor, financial institutions factor, enterprises factor and other factors perform poorly. So the overall performance of the system is in mediocre level. Finally some relevant recommendations are put forward to optimize the venture investment and financing system

Optimising the preparedness capacity of enterprise resilience using mathematical programming

This article belongs to the Special Issue Mathematical Methods and Analysis for the Industrial Management and Business.In today's volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation.This work was supported by the Spanish State Research Agency (Agencia Estatal de Investigación) under the Reference No. RTI2018-101344-B-I00-AR

Conficuration of enterprise networks

International audienceIn the study, we consider the methods for optimizing the configuration of the network structure of enterprises based on the theory of fuzzy sets. These methods allow customizing the value chain in such a way as to maximize the likelihood of the success of a joint project to create innovative products. A strategic decision to change the configuration of the network structure is made based on an analysis of deviations of the generalized capabilities from the generalized requirements for the enterprise and its closest neighbors along the value chain. This optimization principle allows changing the configuration, taking into account the interests of participants in the network structure as a whole. We have formulated the task of developing tools for enterprise engineering based on intelligent decision support technologies and multi-agent systems. The approach to justifying decisions in the conditions of lack and incompleteness of the initial data on the basis of soft models is an alternative to existing traditional methods. The proposed network structure optimization model will allow effective strategic planning, supporting flexible management mechanisms at the strategic and operational levels. The research results show that it is possible to improve the efficiency of interaction between enterprises united by common goals by using services that allow enterprises to find information about their potential partners