Operationalizing the circular city model for naples' city-port: A hybrid development strategy

The city-port context involves a decisive reality for the economic development of territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model (CCM) operational, preserving and enhancing seas and marine resources in a sustainable way. This can be achieved through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. This paper presents an adaptive decision-making process for Naples (Italy) commercial port's development strategies, aimed at re-establishing a sustainable city-port relationship and making Circular Economy (CE) principles operative. The approach has aimed at implementing a CCM by operationalizing European recommendations provided within both the Sustainable Development Goals (SDGs) framework-specifically focusing on goals 9, 11 and 12-and the Maritime Spatial Planning European Directive 2014/89, to face conflicts about the overlapping areas of the city-port through multidimensional evaluations' principles and tools. In this perspective, a four-step methodological framework has been structured applying a place-based approach with mixed evaluation methods, eliciting soft and hard knowledge domains, which have been expressed and assessed by a core set of Sustainability Indicators (SI), linked to SDGs. The contribution outcomes have been centred on the assessment of three design alternatives for the East Naples port and the development of a hybrid regeneration scenario consistent with CE and sustainability principles. The structured decision-making process has allowed us to test how an adaptive approach can expand the knowledge base underpinning policy design and decisions to achieve better outcomes and cultivate a broad civic and technical engagement, that can enhance the legitimacy and transparency of policies

A Multi-Factorial Risk Prioritization Framework for Food-Borne Pathogens

To lower the incidence of human food-borne disease, experts and stakeholders have urged the development of a science- and risk-based management system in which food-borne hazards are analyzed and prioritized. A literature review shows that most approaches to risk prioritization developed to date are based on measures of health outcomes and do not systematically account for other factors that may be important to decision making. The Multi-Factorial Risk Prioritization Framework developed here considers four factors that may be important to risk managers: public health, consumer risk perceptions and acceptance, market-level impacts, and social sensitivity. The framework is based on the systematic organization and analysis of data on these multiple factors. The basic building block of the information structure is a three-dimensional cube based on pathogen-food-factor relationships. Each cell of the cube has an information card associated with it and data from the cube can be aggregated along different dimensions. The framework is operationalized in three stages, with each stage adding another dimension to decision-making capacity. The first stage is the information cards themselves that provide systematic information that is not pre-processed or aggregated across factors. The second stage maps the information on the various information cards into cobweb diagrams that create a graphical profile of, for example, a food-pathogen combination with respect to each of the four risk prioritization factors. The third stage is formal multi-criteria decision analysis in which decision makers place explicit values on different criteria in order to develop risk priorities. The process outlined above produces a ‘List A’ of priority food-pathogen combinations according to some aggregate of the four risk prioritization factors. This list is further vetted to produce ‘List B’, which brings in feasibility analysis by ranking those combinations where practical actions that have a significant impact are feasible. Food-pathogen combinations where not enough is known to identify any or few feasible interventions are included in ‘List C’. ‘List C’ highlights areas with significant uncertainty where further research may be needed to enhance the precision of the risk prioritization process. The separation of feasibility and uncertainty issues through the use of ‘Lists A, B, and C’ allows risk managers to focus separately on distinct dimensions of the overall prioritization. The Multi-Factorial Risk Prioritization Framework provides a flexible instrument that compares and contrasts risks along four dimensions. Use of the framework is an iterative process. It can be used to establish priorities across pathogens for a particular food, across foods for a particular pathogen and/or across specific food-pathogen combinations. This report provides a comprehensive conceptual paper that forms the basis for a wider process of consultation and for case studies applying the framework.risk analysis, risk prioritization, food-borne pathogens, benefits and costs

An Application of The PROMETHEE Method To Select The Best Response for Carbon Fibre Reinforced Plastic Drilling in Machining Operations

The manufacture of carbon fibre reinforced plastic (CFRP) composites from carbon fibres fused with epoxy matrix, deploying the hand layup method has raised considerable attention. Within this research domain, drilling with various tools such as coated and uncoated drills is of great significance. Unfortunately, the use of intuition and experience to select the best parameter in the drilling operation has been known to be less efficient, causing the inadequate distribution of drilling resources to actualize the effectiveness of drilling parameters. Energy wastages are also associated with the present practice of intuition in drilling process. In this study, a novel approach of PROMETHEE I and II are presented to avoid ineffectiveness in drilling resource distribution and select the best drilling operations parameters. The proposed method utilizes experimental data from the literature to verify the method's performance. This study helps in reducing waste due to the inadequate distribution of drilling operations sources. PROMETHEE analyses the drilling parameters of the CFRP composites using preference functions that map the differences among alternatives during machining judgments. Out of the six responses examined, the best response is exit delamination with a weight of 0.059, surface roughness with a weight of 0.031emerged as the second position, torque weighing 0.003 took the 3rd position while the last position is entry delamination, weighing -0.102

Multi-criteria decision analysis software in healthcare priority setting: A systematic review

Objective The objectives of this systematic review were to identify studies using Multi-Criteria Decision Analysis (MCDA) software tools to support health prioritisation processes and describe the technical capabilities of the MCDA software tools identified. Methods First, a systematic literature review was conducted in the MEDLINE, EMBASE, Web of Science, EconLit and Cochrane databases in July 2019 to identify studies that have used MCDA software for priority setting in health-related problems. Second, the MCDA software tools found in the review were downloaded (full versions, where freely available, and trial versions otherwise) and tested to extract their key technical characteristics. Results Nine studies were included, from which seven different software tools, 1000minds®, M-MACBETH, Socio Technical Allocation of Resources (STAR), Strategic Multi-Attribute Ranking Tool (SMART), Visual PROMETHEE, EVIDEM and the Prioritisation Framework, were identified. These software tools differed in terms of the operating systems (including web interface), MCDA technique(s) available for use, visualisation features, and the capability to perform Value for Money (VfM) and sensitivity analyses. Conclusions The use of MCDA software in prioritisation processes has a number of advantages such as inclusion of several types of stakeholders and the ability to analyse a greater number of alternatives and criteria and perform real-time sensitivity analyses. Proprietary software (i.e. software with licensing fees) seemed to have more features than freely available software. However, this field is still developing, with only a few studies where MCDA software was used to support health priority setting and opportunity costs not explicitly captured in many software tools

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Combined-order Algorithm using Promethee Method Approach and Analytic Hierarchy Decision for Chunk Allocation in LTE Uplink Systems

The problem of chunk-based resource allocation for the uplink of Long Term Evolution is investigated. In this paper, a combined order using the promethee method and analytic hierarchy decision for chunk allocation algorithm is proposed. The utility of each order is sorted based on promethee method approach so that the utility of each order could be approximated as the average of all criteria on each order. To decide the best allocation, analytic hierarchy process score is assigned to its order based on their decision criteria weighting factor to find the best allocation. Using a particular weighting factor, the proposed algorithms outperform the previous mean greedy algorithms which use user-order allocation in term of spectral efficiency and data rate fairness without increase the time complexity. It also outperform iterative swapping chunk algorithm in term of  data rate fairness

Applying MCDM Technique in analyzing the effect of promotion items based on online shopping factors : a case study

Developing technology causes companies in a market to compete with each other in challenging ways. In addition to a holistic marketing concept which focuses on the needs of target markets and delivering superior value, marketing should be adapted with new technology to fulfill consumer needs. Although many strategies have been introduced for marketing, digital marketing or marketing 4.0 is a new generation of marketing that engages with the audience through digital tools. These days, online shopping is only too popular among consumers. Many factors which affect customer decision during online shopping have been explored. In this paper, the effect of promotion items in online shopping will be explained. The main aim of this paper is to apply a multi-criteria decision-making technique in prioritizing eleven promotion items by considering five online customer shopping criteria. The novelty of this paper is to apply PROMETHEE II (Preference Ranking Organization METHod for Enrichment of Evaluations) in analyzing the effect of promotion items based on online shopping criteria. PROMETHEE II completely prioritizes discrete alternatives. A case study is conducted in a home appliance company in Iran