The Role of People and Data in Complex Spatial-Related Long-Term Decisions: A Case Study of Capital Project Management Groups

Significant long-term investment projects can involve complex decisions. These are often described as capital projects and the factors that contribute to their complexity include budgets, motivating reasons for investment, stakeholder involvement, interdependent projects, and the delivery phases required. The complexity of these projects often requires management groups to be established involving stakeholder representatives, these teams are inherently multidisciplinary. This study uses two university campus capital projects as case studies for this type of management group. Due to the interaction of projects with wider campus infrastructure and users, decisions are made at varying spatial granularity throughout the project lifespan. This spatial-related context brings complexity to the group decisions. Sensemaking is the process used to achieve group situational awareness of a complex situation, enabling the team to arrive at a consensus and make a decision. The purpose of this study is to understand the role of people and data in complex spatial related long-term decision and sensemaking processes. The paper aims to identify and present issues experienced in practical settings of these types of decision. A series of exploratory semi-structured interviews with members of the two projects elicit an understanding of their operation. From two stages of thematic analysis, inductive and deductive, emergent themes are identified around the group structure, the data usage, and the decision making within these groups. When data were made available to the group, there were commonly issues with perception of veracity and validity of the data presented; this impacted the ability of the group to reach consensus and therefore for decision to be made. Similarly, there were different responses to forecasted or modelled data, shaped by the experience and occupation of the individuals within the multidisciplinary management group. This paper provides an understanding of further support required for team sensemaking and decision making in complex capital projects. The paper also discusses the barriers found to effective decision making in this setting and suggests opportunities to develop decision support systems in this team strategic decision-making process. Recommendations are made for further research into the sensemaking and decision-making process of this complex spatial-related setting.

Physics of Decision for Polling Place Management: A Case Study from the 2020 USA Presidential Election

In the context of the global pandemic, the practical management of the 2020 presidential election in the USA was a strong concern. To anticipate and prepare for this election accurately, one of the main challenges was to confront: (i) forecasts of voter turnout, (ii) capacities of the facilities and, (iii) potential configuration options of resources. The approach chosen to conduct this anticipative study consists of collecting data about forecasts and using simulation models to work simultaneously on resource allocation and facility configuration of polling places in Fulton County, Georgia’s largest county. This article presents the results of the simulations of such places facing pre-identified potential risks. These results are oriented towards the efficiency of these places according to different criteria (health, trust, comfort). Then a dynamic framework is introduced to describe risks as physical forces perturbing the efficiency of the observed system. Finally, the main benefits and contributions resulting from this simulation campaign are presented.

Technical, Environmental, and Financial Assessment for the Optimal Sizing of a Run-of-River Small Hydropower Project: A Case Study in Colombia

Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes’ cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an internal rate of return (IRR) 1.5 times higher than the discount rate. 

Facility Location Selection using Preference Programming

This paper presents preference programming technique based multiple criteria decision making analysis for selecting a facility location for a new organization or expansion of an existing facility which is of vital importance for a decision support system and strategic planning process. The implementation of decision support systems is considered crucial to sustain competitive advantage and profitability persistence in turbulent environment. As an effective strategic management and decision making is necessary, multiple criteria decision making analysis supports the decision makers to formulate and implement the right strategy. The investment cost associated with acquiring the property and facility construction makes the facility location selection problem a long-term strategic investment decision, which rationalize the best location selection which results in higher economic benefits through increased productivity and optimal distribution network. Selecting the proper facility location from a given set of alternatives is a difficult task, as many potential qualitative and quantitative multiple conflicting criteria are to be considered. This paper solves a facility location selection problem using preference programming, which is an effective multiple criteria decision making analysis tool applied to deal with complex decision problems in the operational research environment. The ranking results of preference programming are compared with WSM, TOPSIS and VIKOR methods.

Military Fighter Aircraft Selection Using Multiplicative Multiple Criteria Decision Making Analysis Method

Multiplicative multiple criteria decision making analysis (MCDMA) method is a systematic decision support system to aid decision makers reach appropriate decisions. The application of multiplicative MCDMA in the military aircraft selection problem is significant for proper decision making process, which is the decisive factor in minimizing expenditures and increasing defense capability and capacity. Nine military fighter aircraft alternatives were evaluated by ten decision criteria to solve the decision making problem. In this study, multiplicative MCDMA model aims to evaluate and select an appropriate military fighter aircraft for the Air Force fleet planning. The ranking results of multiplicative MCDMA model were compared with the ranking results of additive MCDMA, logarithmic MCDMA, and regrettive MCDMA models under the L2 norm data normalization technique to substantiate the robustness of the proposed method. The final ranking results indicate the military fighter aircraft Su-57 as the best available solution.

Methodology for the Multi-Objective Analysis of Data Sets in Freight Delivery

Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Vulnerability Analysis for Risk Zones Boundary Definition to Support a Decision Making Process at CBRNE Operations

An effective emergency response to accidents with chemical, biological, radiological, nuclear, or explosive materials (CBRNE) that represent highly dynamic situations needs immediate actions within limited time, information and resources. The aim of the study is to provide the foundation for division of unsafe area into risk zones according to the impact of hazardous parameters (heat radiation, thermal dose, overpressure, chemical concentrations). A decision on the boundary values for three risk zones is based on the vulnerability analysis that covered a variety of accident scenarios containing the release of a toxic or flammable substance which either evaporates, ignites and/or explodes. Critical values are selected for the boundary definition of the Red, Orange and Yellow risk zones upon the examination of harmful effects that are likely to cause injuries of varying severity to people and different levels of damage to structures. The obtained results provide the basis for creating a comprehensive real-time risk map for a decision support at CBRNE operations.

Development of an Intelligent Decision Support System for Smart Viticulture

The Internet of Things (IoT) represents the best option for smart vineyard applications, even if it is necessary to integrate the technologies required for the development. This article is based on the research and the results obtained in the DISAVIT project. For Smart Agriculture, the project aims to provide a trustworthy, intelligent, integrated vineyard management solution that is based on the IoT. To have interoperability through the use of a multiprotocol technology (being the future connected wireless IoT) it is necessary to adopt an agnostic approach, providing a reliable environment to address cyber security, IoT-based threats and traceability through blockchain-based design, but also creating a concept for long-term implementations (modular, scalable). The ones described above represent the main innovative technical aspects of this project. The DISAVIT project studies and promotes the incorporation of better management tools based on objective data-based decisions, which are necessary for agriculture adapted and more resistant to climate change. It also exploits the opportunities generated by the digital services market for smart agriculture management stakeholders. The project's final result aims to improve decision-making, performance, and viticulturally infrastructure and increase real-time data accuracy and interoperability. Innovative aspects such as end-to-end solutions, adaptability, scalability, security and traceability, place our product in a favorable situation over competitors. None of the solutions in the market meet every one of these requirements by a unique product being innovative.

Protection of Cultural Heritage against the Effects of Climate Change Using Autonomous Aerial Systems Combined with Automated Decision Support

The article presents an ongoing work in research projects such as SCAN4RECO or ARCH, both funded by the European Commission under Horizon 2020 program. The former one concerns multimodal and multispectral scanning of Cultural Heritage assets for their digitization and conservation via spatiotemporal reconstruction and 3D printing, while the latter one aims to better preserve areas of cultural heritage from hazards and risks. It co-creates tools that would help pilot cities to save cultural heritage from the effects of climate change. It develops a disaster risk management framework for assessing and improving the resilience of historic areas to climate change and natural hazards. Tools and methodologies are designed for local authorities and practitioners, urban population, as well as national and international expert communities, aiding authorities in knowledge-aware decision making. In this article we focus on 3D modelling of object geometry using primarily photogrammetric methods to achieve very high model accuracy using consumer types of devices, attractive both to professions and hobbyists alike.

Meteorological Risk Assessment for Ships with Fuzzy Logic Designer

Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.

Conceptualizing Thoughtful Intelligence for Sustainable Decision Making

Thoughtful intelligence offers a sustainable position to enhance the influence of decision-makers. Thoughtful Intelligence implies the understanding to realize the impact of one’s thoughts, words and actions on the survival, dignity and development of the individuals, groups and nations. Thoughtful intelligence has received minimal consideration in the area of Decision Support Systems, with an end goal to evaluate the quantity of knowledge and its viability. This pattern degraded the imbibed contribution of thoughtful intelligence required for sustainable decision making. Given the concern, this paper concentrates on the question: How to present a model of Thoughtful Decision Support System (TDSS)? The aim of this paper is to appreciate the concepts of thoughtful intelligence and insinuate a Decision Support System based on thoughtful intelligence. Thoughtful intelligence includes three dynamic competencies: i) Realization about long term impacts of decisions that are made in a specific time and space, ii) A great sense of taking actions, iii) Intense interconnectivity with people and nature and; seven associate competencies, of Righteousness, Purposefulness, Understanding, Contemplation, Sincerity, Mindfulness, and Nurturing. The study utilizes two methods: Focused group discussion to count prevailing Decision Support Systems; 70% results of focus group discussions found six decision support systems and the positive inexistence of thoughtful intelligence among decision support systems regarding sustainable decision making. Delphi focused on defining thoughtful intelligence to model (TDSS). 65% results helped to conceptualize (definition and description) of thoughtful intelligence. TDSS is offered here as an addition in the decision making literature. The clients are top leaders.

Leveraging Hyperledger Iroha for the Issuance and Verification of Higher-Education Certificates

Higher Education is resisting the pull of technology, especially as this concerns the issuance and verification of degrees and certificates. It is widely known that education certificates are largely produced in paper form making them vulnerable to damage while holders of such certificates are dependent on the universities and other issuing organisations. QualiChain is an EU Horizon 2020 (H2020) research project aiming to transform and revolutionise the domain of public education and its ties with the job market by leveraging blockchain, analytics and decision support to develop a platform for the verification and sharing of education certificates. Blockchain plays an integral part in the QualiChain solution in providing a trustworthy environment to store, share and manage such accreditations. Under the context of this paper, three prominent blockchain platforms (Ethereum, Hyperledger Fabric, Hyperledger Iroha) were considered as a means of experimentation for creating a system with the basic functionalities that will be needed for trustworthy degree verification. The methodology and respective system developed and presented in this paper used Hyperledger Iroha and proved that this specific platform can be used to easily develop decentralize applications. Future papers will attempt to further experiment with other blockchain platforms and assess which has the best potential.

A Bayesian Classification System for Facilitating an Institutional Risk Profile Definition

This paper presents an approach for easy creation and classification of institutional risk profiles supporting endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support set up of the most important risk factors. Subsequently, risk profiles employ risk factors classifier and associated configurations to support digital preservation experts with a semi-automatic estimation of endangerment group for file format risk profiles. Our goal is to make use of an expert knowledge base, accuired through a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation of risk factors for a requried dimension for analysis. Using the naive Bayes method, the decision support system recommends to an expert the matching risk profile group for the previously selected institutional risk profile. The proposed methods improve the visibility of risk factor values and the quality of a digital preservation process. The presented approach is designed to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and values of file format risk profiles. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert and to define its profile group. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Development of Requirements Analysis Tool for Medical Autonomy in Long-Duration Space Exploration Missions

Improving resources for medical autonomy of astronauts in prolonged space missions, such as a Mars mission, requires not only technology development, but also decision-making support systems. The Advanced Crew Medical System - Medical Condition Requirements study, funded by the Canadian Space Agency, aimed to create knowledge content and a scenario-based query capability to support medical autonomy of astronauts. The key objective of this study was to create a prototype tool for identifying medical infrastructure requirements in terms of medical knowledge, skills and materials. A multicriteria decision-making method was used to prioritize the highest risk medical events anticipated in a long-term space mission. Starting with those medical conditions, event sequence diagrams (ESDs) were created in the form of decision trees where the entry point is the diagnosis and the end points are the predicted outcomes (full recovery, partial recovery, or death/severe incapacitation). The ESD formalism was adapted to characterize and compare possible outcomes of medical conditions as a function of available medical knowledge, skills, and supplies in a given mission scenario. An extensive literature review was performed and summarized in a medical condition database. A PostgreSQL relational database was created to allow query-based evaluation of health outcome metrics with different medical infrastructure scenarios. Critical decision points, skill and medical supply requirements, and probable health outcomes were compared across chosen scenarios. The three medical conditions with the highest risk rank were acute coronary syndrome, sepsis, and stroke. Our efforts demonstrate the utility of this approach and provide insight into the effort required to develop appropriate content for the range of medical conditions that may arise.

Developing a Web-Based Tender Evaluation System Based on Fuzzy Multi-Attributes Group Decision Making for Nigerian Public Sector Tendering

Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent and more prone to manipulations and errors. The advent of the Internet and the World Wide Web has led to the development of numerous e-Tendering systems that addressed some of the problems associated with the manual paper-based tendering system. However, most of these systems rarely support the evaluation of tenders and where they do it is mostly based on the single decision maker which is not suitable in public sector tendering, where for the sake of objectivity, transparency, and fairness, it is required that the evaluation is conducted through a tender evaluation committee. Currently, in Nigeria, the public tendering process in general and the evaluation of tenders, in particular, are largely conducted using manual paper-based processes. Automating these manual-based processes to digital-based processes can help in enhancing the proficiency of public sector tendering in Nigeria. This paper is part of a larger study to develop an electronic tendering system that supports the whole tendering lifecycle based on Nigerian procurement law. Specifically, this paper presents the design and implementation of part of the system that supports group evaluation of tenders based on a technique called fuzzy multi-attributes group decision making. The system was developed using Object-Oriented methodologies and Unified Modelling Language and hypothetically applied in the evaluation of technical and financial proposals submitted by bidders. The system was validated by professionals with extensive experiences in public sector procurement. The results of the validation showed that the system called NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing manual tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Thus, based on the results of the validation, the automation of the evaluation process to support tender evaluation committee is achievable and can lead to a more proficient public sector tendering system.

Effective Planning of Public Transportation Systems: A Decision Support Application

Decision making on the true planning of the public transportation systems to serve potential users is a must for metropolitan areas. To take attraction of travelers to projected modes of transport, adequately fair overall travel times should be provided. In this fashion, other benefits such as lower traffic congestion, road safety and lower noise and atmospheric pollution may be earned. The congestion which comes with increasing demand of public transportation is becoming a part of our lives and making residents’ life difficult. Hence, regulations should be done to reduce this congestion. To provide a constructive and balanced regulation in public transportation systems, right stations should be located in right places. In this study, it is aimed to design and implement a Decision Support System (DSS) Application to determine the optimal bus stop places for public transport in Istanbul which is one of the biggest and oldest cities in the world. Required information is gathered from IETT (Istanbul Electricity, Tram and Tunnel) Enterprises which manages all public transportation services in Istanbul Metropolitan Area. By using the most real-like values, cost assignments are made. The cost is calculated with the help of equations produced by bi-level optimization model. For this study, 300 buses, 300 drivers, 10 lines and 110 stops are used. The user cost of each station and the operator cost taken place in lines are calculated. Some components like cost, security and noise pollution are considered as significant factors affecting the solution of set covering problem which is mentioned for identifying and locating the minimum number of possible bus stops. Preliminary research and model development for this study refers to previously published article of the corresponding author. Model results are represented with the intent of decision support to the specialists on locating stops effectively.

A Comparative Analysis Approach Based on Fuzzy AHP, TOPSIS and PROMETHEE for the Selection Problem of GSCM Solutions

Sustainable economic growth is nowadays driving firms to extend toward the adoption of many green supply chain management (GSCM) solutions. However, the evaluation and selection of these solutions is a matter of concern that needs very serious decisions, involving complexity owing to the presence of various associated factors. To resolve this problem, a comparative analysis approach based on multi-criteria decision-making methods is proposed for adequate evaluation of sustainable supply chain management solutions. In the present paper, we propose an integrated decision-making model based on FAHP (Fuzzy Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) to contribute to a better understanding and development of new sustainable strategies for industrial organizations. Due to the varied importance of the selected criteria, FAHP is used to identify the evaluation criteria and assign the importance weights for each criterion, while TOPSIS and PROMETHEE methods employ these weighted criteria as inputs to evaluate and rank the alternatives. The main objective is to provide a comparative analysis based on TOPSIS and PROMETHEE processes to help make sound and reasoned decisions related to the selection problem of GSCM solution.

Design of a Pneumonia Ontology for Diagnosis Decision Support System

Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Tools and Techniques in Risk Assessment in Public Risk Management Organisations

Risk assessment and the knowledge provided through this process is a crucial part of any decision-making process in the management of risks and uncertainties. Failure in assessment of risks can cause inadequacy in the entire process of risk management, which in turn can lead to failure in achieving organisational objectives as well as having significant damaging consequences on populations affected by the potential risks being assessed. The choice of tools and techniques in risk assessment can influence the degree and scope of decision-making and subsequently the risk response strategy. There are various available qualitative and quantitative tools and techniques that are deployed within the broad process of risk assessment. The sheer diversity of tools and techniques available to practitioners makes it difficult for organisations to consistently employ the most appropriate methods. This tools and techniques adaptation is rendered more difficult in public risk regulation organisations due to the sensitive and complex nature of their activities. This is particularly the case in areas relating to the environment, food, and human health and safety, when organisational goals are tied up with societal, political and individuals’ goals at national and international levels. Hence, recognising, analysing and evaluating different decision support tools and techniques employed in assessing risks in public risk management organisations was considered. This research is part of a mixed method study which aimed to examine the perception of risk assessment and the extent to which organisations practise risk assessment’ tools and techniques. The study adopted a semi-structured questionnaire with qualitative and quantitative data analysis to include a range of public risk regulation organisations from the UK, Germany, France, Belgium and the Netherlands. The results indicated the public risk management organisations mainly use diverse tools and techniques in the risk assessment process. The primary hazard analysis; brainstorming; hazard analysis and critical control points were described as the most practiced risk identification techniques. Within qualitative and quantitative risk analysis, the participants named the expert judgement, risk probability and impact assessment, sensitivity analysis and data gathering and representation as the most practised techniques.

Q-Map: Clinical Concept Mining from Clinical Documents

Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Most of the data in the field are well-structured and available in numerical or categorical formats which can be used for experiments directly. But on the opposite end of the spectrum, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature which can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written narrative format and neither have any relational model nor any standard grammatical structure. An important step in the utilization of these texts for such studies is to transform and process the data to retrieve structured information from the haystack of irrelevant data using information retrieval and data mining techniques. To address this problem, the authors present Q-Map in this paper, which is a simple yet robust system that can sift through massive datasets with unregulated formats to retrieve structured information aggressively and efficiently. It is backed by an effective mining technique which is based on a string matching algorithm that is indexed on curated knowledge sources, that is both fast and configurable. The authors also briefly examine its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval and present the advantages the former displays over the latter.