Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection

This study combines Fuzzy Analytic Hierarchy Process (FAHP), Geographic Information System (GIS) and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP). The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future

Sensitivity Analysis Method to Address User Disparities in the Analytic Hierarchy Process

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived weights and the ranking of alternatives. The method can also be applied to decision processes that require the aggregation of results obtained by several users, as it highlights which individuals most critically impact the aggregated group results while also enabling to focus on inputs that drive the final ordering of alternatives. An aerospace design and engineering example that requires group decision making is presented to demonstrate and validate the proposed methodology

Machine learning-driven approach for large scale decision making with the analytic hierarchy process

The Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.This research was funded by National Funds through the FCT—Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020

Enhancing urban sustainability through novel visualisation

Sustainable decision making in Urban Design is a complex and non-linear process that requires the interaction of a wide variety of stakeholders. The engagement of a range of stakeholders throughout the decision making process presents challenges including the need to communicate the complex and interdependent facets of sustainability and the need to demonstrate the short and long term implications of alternative courses of action.This paper presents the results of an initial application of a prototype simulation and visualisation tool (S-City VT) that was developed to enable all stakeholders, regardless of background or experience, to understand, interact with and influence decisions made on the sustainability of urban design. S-City VT takes the unique approach of combining computer game technology with computer modelling to present stakeholders with an interactive virtual development. The paper uses the Dundee Central Waterfront Development Project as a case study to evaluate the potential for the application of the tool and explains how parallel research work on the implementation of a sustainability enhancement framework for the Central Waterfront Development has informed the choice of sustainability indictors and identified the key stakeholders in the decision making processes.The paper shows how stakeholders can be presented with the outputs from the model using a 3D visualisation of the development and thus enables judgements to be made on the relative sustainability of aspects of the development. The visualisation tool employs a number of different methods of displaying the sustainability results to the stakeholders. These methods can show data in varying levels of complexity, depending on the expertise of the stakeholder, empowering all stakeholders by illustrating possible interactions between indicator values and sustainability and by showing how different stakeholder perceptions of the importance of the indicators can influence the sustainability assessment.Initial tests on the effectiveness of the different visualisation methods in displaying the model output to communicate the sustainability of the Development are described. The results of the tests and presented and discussed and conclusions are drawn on the further development and application of the tool to model and visualise through time the possible results of decisions made at different stages of the project

Using Future Value Analysis to Select an Optimal Portfolio of Force Protection Initiatives

With the recent increase in terrorist activity, force protection has become a key issue for the Department of Defense, Leading the research for new ideas and concepts in force protection for the US Air Force is the Air Force Force Protection Battlelab (FPB). The FPB is charged with searching out force protection ideas and selecting those most worthy for future consideration. In 2002, a Value-Focused Thinking (VFT) hierarchy was created to help the FPB select those ideas that provided the most value to the Air Force and its force protection goals. This research effort uses the Future Value Analysis (FVA) approach, a decision-making methodology, to provide a more accurate project selection tool to the FPB. FVA incorporates the ideals of multi-attribute utility theory, specifically using the VFT process, as well as linear programming optimization techniques, to provide an optimal portfolio of initiatives for the FPB to pursue. FVA provides a solution that optimizes the value of initiatives selected, while remaining within the organizational constraints of the FPB. This research provides a proof of implementation for the FVA process in the force protection environment

Sustainable R&D portfolio assessment.

Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality etc. Even if these data are available, then consistent decision support tools are not ready available. Based on the findings from an industry review, we developed a DEA model that permits to support strategic R&D portfolio management. We underscore the usability of this approach with real life examples from two different industries: consumables and materials manufacturing (polymers).R&D portfolio management; Data envelopment analysis; Sustainable R&D;

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

Influence networks based methodology for consensus reaching in group-decision-making problems

[EN] The purpose of this work is to show a way to improve agreement in group decision problems. This work focuses his effort on the issue refers to assign different importance to every decision-maker. We propose as a novelty a methodology to assign different levels of importance to every decision-maker according to their perceived importance in the group. First, judgments are collected by an html form and use a proposed method based on SNA and DEMATEL to assign weights to decision-makers according to their reputation in the decision-group. Next, we solve the problem using AHP in order to rank the alternatives.Romero-Gelvez, JI.; Cortes-Aldana, FA.; García-Melón, M.; Herrera Cuartas, JA.; Garcia Bedoya, O. (2019). Influence networks based methodology for consensus reaching in group-decision-making problems. CEUR Workshop Proceedings. 2486:280-294. http://hdl.handle.net/10251/155012S280294248