Decision Support System for SmartPhone Selection with AHP-VIKOR Method Recommendations

Produce products that have various features and diverse functions, which are able to provide convenience with the reliability of their features and functions. The advantages possessed by SmartPhone become more confident for users to assess the level of product intelligence, the more trustworthy. The purpose of this research is to provide additional knowledge on the selection of SmartPhone to the user in having a product with various benefits. The more criteria that become a barometer, the more difficult it is to choose a product in the form of a SmartPhone. Thus, the right method is needed to perform the selection of the SmartPhone. There are several methods offered to carry out the selection process for SmartPhones, namely the Analytic Hierarchy Process (AHP) method combined with the VIKOR elimination method. Both of these methods are very supportive in the selection process with many types of criteria and their meanings against these criteria. A number of criteria that serve as a barometer for selecting object-based applications are Operating System, Processor, Internal Memory, External Memory, Back Camera, Front Camera, Battery, Cassing Model, Screen Size, Wight and Price. Of the eleven criteria have two different characteristics of understanding. The results of this study can be seen explicitly on the selection of SmartPhones through the acquisition of the smallest Qi index with the three highest ratings, namely the first ranked Samsung Galaxy A3 (0.00) the second is the Xiaomi Mi 4C with an index of 0.19, the third is the Lenovo Vibe K5 Plus with index 0.31. Thus it can be said that the collaboration of the AHP and VIKOR Elimination methods is able to provide optimal decision-making support

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR COMPANIES IN THE ENERGY FROM BIOMASS AREA, APPLYING CIRCULAR ECONOMY PRINCIPLES WITH A LIFE CYCLE THINKING APPROACH

The biomass supply chain (BSC) for energy production has emerged as a promising alternative to traditional fossil fuels, playing a crucial role in mitigating climate change and promoting sustainable development. Biomass utilisation offers numerous environmental, economic, and social benefits, including reduced greenhouse gas (GHG) emissions, enhanced energy security, and job creation in rural areas, which are known as important aspects of sustainable development. Moreover, the use of waste, by-products, and residue in BSC is essential to improving the circular economy (CE) in agriculture, wood, and paper processing industries, as well as waste treatment and management. Therefore, to further harness the potential of biomass energy production in sustainability and transition to the CE context, it is significant for companies in the BSC to apply circular business models (CBM).While the role of biomass in the CE has been confirmed, the gap still exists in evaluating the application of CE to the BSC. Up to the authors’ knowledge, currently, there is no set of circularity and sustainability indicators as standard for the company in the BSC. The variety of CE approaches and indicators makes it difficult to convert linear business models into circular ones. In addition, the variety of biomass materials, differences in biomass processing technology and multiple end-products lead to transformation into a CE model in many alternatives with many stages and different technology processes. Furthermore, some indicators assessing aspects of sustainability and circularity of different alternatives are subject to conflict and trade-offs. A more sustainable solution might not necessarily be better in terms of circularity and similar trade-offs exist within the pillars of sustainability. Given the trade-offs between sustainability and circularity, decision support systems (DSS) based on life cycle thinking with a standard set of indicators are promising tools for evaluating and selecting the best alternative of sustainability and circularity BSC.For what is above, this PhD research project was focused on developing a decision support system for a biomass company in the energy sector based on CE and sustainability models with a life cycle thinking approach. With the CE and sustainability model, a set of circularity and sustainability indicators is developed, and it is considered a criteria set to assess the circularity and sustainability of biomass companies and BSC. The life cycle thinking approach is employed to provide a comprehensive assessment for BSC. It is also basic to collect data from BSC and give value to indicators for assessing and ranking alternatives. The trade-off existing in alternatives is solved by using Multiple-criteria decision-making methods. That is integrated into the methodology framework of the decision support system.The PhD research project is structured around two main objectives. First, from CE and sustainability models, a set of circularity and sustainability is development. Secondly, a DSS tool is created. The set of developed indicators considers various stages during the BSC, such as feedstock plantation, processing, transportation, energy conversion, and end-of-life management, being aligned with the United Nations Sustainable Development Goals (SDGs) and the EC’s guidelines on the transition to CE. Meanwhile, the creation of a DSS includes proposing a methodology framework for DSS, creating software in MATLAB GUI and Script as a new tool for DSS, and applying this tool to the rice straw supply chain as testing for the case study.Regarding the case study, a rice straw supply chain for energy production in the Pavia region of Italy is selected. The data for the case study was collected during the internship period at the ENI company, such as parameters of the plant and process. The current of the rice straw supply chain is assessed by the DSS tool, and a re-edited version of this tool was taken. The alternatives of CE applications in the case study were performed through an external internship at the IMDEA Energy Institute (Spain). The data on alternatives is gathered based on the results of the simulation of the chemical process by Aspen plus@ at the IMDEA Energy Institute for suitable parameters of the current supply chain. The sustainability and circularity indicators methodology framework and case study developed during this PhD research project have been published in international journals and conference proceedings. The results of the application and details of the decision support system are present in this thesis. The results of calculating indicators for all indicators show that global warming potential (GWP) is 1.21E+03 ton CO2eq/yr to 55.7E+03 ton CO2eq/yr. Meanwhile, rice straw's acidification potential (AP) in this study ranges from 9.66 tonnes of SO2 eq/yr to 563 tonnes of SO2 eq/yr. The internal rate of return (IRR) of the rice straw supply chain is from 5.92% to 11.3%. In addition, the net present value (NPV) of the case study ranges from 0.72 to 5.79 million euros. Furthermore, the rate of informal labour is from 71.9% to 82.10%, while the percentage of recycling rate out of all waste is from 96.61% to 99.2%, the circular material use is from 54.8% to 88.2%, and the proportion of material losses in primary material is from 14.61% to 15.5%. The ranking results indicate that the digestate pyrolysis option has the best sustainability and circularity points among the other options.This PhD project research shows that the application of a comprehensive approach encompassing Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA) to identify sustainability indicators brings about significant advantages to the biomass supply chain. Existing research seldom integrates all three methodologies simultaneously. This integrated approach enhances the understanding of sustainability implications across the biomass supply chain, paving the way for a more holistic assessment.Moreover, the utilization of the Life Cycle Thinking (LCT) tool and Material Flow Analysis (MFA) for circularity indicators introduces a novel dimension to the existing literature. The incorporation of these tools instills confidence in simulating both circularity and sustainability, a consideration often overlooked in previous studies. The resulting circularity and sustainability indicators offer a standardised set that serves as a step-by-step guide for achieving Sustainable Development Goals (SDGs) and transitioning to a circular economy, aligning with the European Commission's roadmap.The development of a Decision Support System (DSS) methodology framework marks another crucial contribution, particularly by integrating circularity and sustainability within a unified framework for biomass companies in the supply chain. Unlike existing frameworks, this approach employs the PROMETHEE II and Entropy methods, leveraging life cycle results to enhance reliability and streamline calculations. Overcoming the limitations of PROMETHEE, this framework incorporates a multiple-criteria decision-making approach to address trade-offs in sustainability and circularity alternatives. This not only improves the robustness of the framework but also extends its applicability to general companies beyond the biomass sector.Furthermore, the accompanying software in this study presents a more practical and potent DSS tool for ranking alternatives. Its flexibility, allowing the use of the DSS tool for calculating sustainability and circularity indicators for individual alternatives, provides users with a versatile platform. The ability to choose indicator groups and methods for weighting indicators enhances the adaptability of the framework, making it applicable in various scenarios for policymakers and researchers committed to advancing circular economy and sustainability initiatives. In summary, based on methods for application, methodology framework and useful software, the DSS tool developed in this thesis can be used to support companies in the biomass supply chain, managers, practitioners, policy-makers, and researchers in assessing and selecting alternatives for application of CBMs to transfer into CE

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR COMPANIES IN THE ENERGY FROM BIOMASS AREA, APPLYING CIRCULAR ECONOMY PRINCIPLES WITH A LIFE CYCLE THINKING APPROACH

The biomass supply chain (BSC) for energy production has emerged as a promising alternative to traditional fossil fuels, playing a crucial role in mitigating climate change and promoting sustainable development. Biomass utilisation offers numerous environmental, economic, and social benefits, including reduced greenhouse gas (GHG) emissions, enhanced energy security, and job creation in rural areas, which are known as important aspects of sustainable development. Moreover, the use of waste, by-products, and residue in BSC is essential to improving the circular economy (CE) in agriculture, wood, and paper processing industries, as well as waste treatment and management. Therefore, to further harness the potential of biomass energy production in sustainability and transition to the CE context, it is significant for companies in the BSC to apply circular business models (CBM).While the role of biomass in the CE has been confirmed, the gap still exists in evaluating the application of CE to the BSC. Up to the authors’ knowledge, currently, there is no set of circularity and sustainability indicators as standard for the company in the BSC. The variety of CE approaches and indicators makes it difficult to convert linear business models into circular ones. In addition, the variety of biomass materials, differences in biomass processing technology and multiple end-products lead to transformation into a CE model in many alternatives with many stages and different technology processes. Furthermore, some indicators assessing aspects of sustainability and circularity of different alternatives are subject to conflict and trade-offs. A more sustainable solution might not necessarily be better in terms of circularity and similar trade-offs exist within the pillars of sustainability. Given the trade-offs between sustainability and circularity, decision support systems (DSS) based on life cycle thinking with a standard set of indicators are promising tools for evaluating and selecting the best alternative of sustainability and circularity BSC.For what is above, this PhD research project was focused on developing a decision support system for a biomass company in the energy sector based on CE and sustainability models with a life cycle thinking approach. With the CE and sustainability model, a set of circularity and sustainability indicators is developed, and it is considered a criteria set to assess the circularity and sustainability of biomass companies and BSC. The life cycle thinking approach is employed to provide a comprehensive assessment for BSC. It is also basic to collect data from BSC and give value to indicators for assessing and ranking alternatives. The trade-off existing in alternatives is solved by using Multiple-criteria decision-making methods. That is integrated into the methodology framework of the decision support system.The PhD research project is structured around two main objectives. First, from CE and sustainability models, a set of circularity and sustainability is development. Secondly, a DSS tool is created. The set of developed indicators considers various stages during the BSC, such as feedstock plantation, processing, transportation, energy conversion, and end-of-life management, being aligned with the United Nations Sustainable Development Goals (SDGs) and the EC’s guidelines on the transition to CE. Meanwhile, the creation of a DSS includes proposing a methodology framework for DSS, creating software in MATLAB GUI and Script as a new tool for DSS, and applying this tool to the rice straw supply chain as testing for the case study.Regarding the case study, a rice straw supply chain for energy production in the Pavia region of Italy is selected. The data for the case study was collected during the internship period at the ENI company, such as parameters of the plant and process. The current of the rice straw supply chain is assessed by the DSS tool, and a re-edited version of this tool was taken. The alternatives of CE applications in the case study were performed through an external internship at the IMDEA Energy Institute (Spain). The data on alternatives is gathered based on the results of the simulation of the chemical process by Aspen plus@ at the IMDEA Energy Institute for suitable parameters of the current supply chain. The sustainability and circularity indicators methodology framework and case study developed during this PhD research project have been published in international journals and conference proceedings. The results of the application and details of the decision support system are present in this thesis. The results of calculating indicators for all indicators show that global warming potential (GWP) is 1.21E+03 ton CO2eq/yr to 55.7E+03 ton CO2eq/yr. Meanwhile, rice straw's acidification potential (AP) in this study ranges from 9.66 tonnes of SO2 eq/yr to 563 tonnes of SO2 eq/yr. The internal rate of return (IRR) of the rice straw supply chain is from 5.92% to 11.3%. In addition, the net present value (NPV) of the case study ranges from 0.72 to 5.79 million euros. Furthermore, the rate of informal labour is from 71.9% to 82.10%, while the percentage of recycling rate out of all waste is from 96.61% to 99.2%, the circular material use is from 54.8% to 88.2%, and the proportion of material losses in primary material is from 14.61% to 15.5%. The ranking results indicate that the digestate pyrolysis option has the best sustainability and circularity points among the other options.This PhD project research shows that the application of a comprehensive approach encompassing Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA) to identify sustainability indicators brings about significant advantages to the biomass supply chain. Existing research seldom integrates all three methodologies simultaneously. This integrated approach enhances the understanding of sustainability implications across the biomass supply chain, paving the way for a more holistic assessment.Moreover, the utilization of the Life Cycle Thinking (LCT) tool and Material Flow Analysis (MFA) for circularity indicators introduces a novel dimension to the existing literature. The incorporation of these tools instills confidence in simulating both circularity and sustainability, a consideration often overlooked in previous studies. The resulting circularity and sustainability indicators offer a standardised set that serves as a step-by-step guide for achieving Sustainable Development Goals (SDGs) and transitioning to a circular economy, aligning with the European Commission's roadmap.The development of a Decision Support System (DSS) methodology framework marks another crucial contribution, particularly by integrating circularity and sustainability within a unified framework for biomass companies in the supply chain. Unlike existing frameworks, this approach employs the PROMETHEE II and Entropy methods, leveraging life cycle results to enhance reliability and streamline calculations. Overcoming the limitations of PROMETHEE, this framework incorporates a multiple-criteria decision-making approach to address trade-offs in sustainability and circularity alternatives. This not only improves the robustness of the framework but also extends its applicability to general companies beyond the biomass sector.Furthermore, the accompanying software in this study presents a more practical and potent DSS tool for ranking alternatives. Its flexibility, allowing the use of the DSS tool for calculating sustainability and circularity indicators for individual alternatives, provides users with a versatile platform. The ability to choose indicator groups and methods for weighting indicators enhances the adaptability of the framework, making it applicable in various scenarios for policymakers and researchers committed to advancing circular economy and sustainability initiatives. In summary, based on methods for application, methodology framework and useful software, the DSS tool developed in this thesis can be used to support companies in the biomass supply chain, managers, practitioners, policy-makers, and researchers in assessing and selecting alternatives for application of CBMs to transfer into CE

An enhanced supplier selection model based on optimized analytic network process towards sustainable information technology outsourcing

Information Technology Outsourcing (ITO) has become part of the organization’s strategy as it offers benefits such as high-quality products, cost reduction, and increased productivity. Essentially, ITO is a complex process in which selecting the right supplier involves evaluation of multi criteria. To ensure the sustainable of the ITO project, the evaluation criteria should consider risk factors and other sustainability criteria of the project. However, existing ITO supplier selection models lack of sustainability criteria and risk factors. Moreover, these methods rely on human judgment in weight allocation. Therefore, this study proposes an Enhanced Supplier Selection Model (ESS) for sustainable ITO mainly to eliminate human judgment in Analytical Network Process (ANP) method. The ESS Model was constructed through theoretical, exploratory and experimental studies. The exploratory study was carried in Thailand using survey which involved 45 respondents. Findings from the study was used to construct evaluation criteria and become datasets for ESS. The proposed ESS Model was evaluated using expert reviews and case studies in Thailand. The ESS model contains two main components: evaluation criteria and a decision-making method. The first has nineteen (19) sustainability criteria and seven (7) risk factors. While the latter is an enhanced ANP with Firefly Algorithm (ANP-FA). The evaluation results indicate that the Consistency Ratio (CR) for ANP-FA is smaller than ANP, which is 0.003 compared to 0.031. This outcome shows that the ESS model is feasible in removing human judgment in supplier selection of ITO projects. The study’s contributions can be interpreted from two perspectives. The proposed ESS model is a theoretical contribution in Multi-Criteria Decision-Making and Supplier Selection in ITO project. In terms of practicality, the model has been realized in Thailand organizations to ensure the sustainability of ITO projects

Decision-Making in Reuse of Highway Bridge Foundations

According to the 2019 National Bridge Inventory data from Federal Highway Administration, the average age of highway bridges in the U.S.A. is 45 years, with almost 43% of existing highway bridges being older than 50 years, and eight percent of all U.S. national highway bridges being in poor condition. Foundation and substructure of existing highway bridges (over land and water) may have significant functional values even after being under service for decades. Reusing an existing bridge foundation during the reconstruction of a bridge (e.g., major rehabilitation, retrofitting, replacement of superstructure and substructure, and addition/removal of a span) has the potential for significant savings on total cost and construction time. However, because of uncertainties in the evaluation of integrity, durability, and load-carrying capacity of an existing foundation, an inevitable level of risk is inherently associated with their reuse. This risk may weigh on potential benefits of reuse of a bridge foundation during the decision making. Therefore, developing a comprehensive framework and procedure for studying the feasibility of foundation reuse is an important step in the foundation reuse decision process. In particular, bridge owners need to have an estimation of various risks associated with available options during a reconstruction project (i.e., fully reuse, partial reuse, and no- reuse). In this dissertation, a decision-making framework is developed to evaluate the feasibility of reusing an existing foundation and substructure of highway bridges. This framework incorporates three factors including safety, cost, and environmental impacts of reusing an existing foundation and compares the available alternatives to find the most feasible alternative. Four general options in bridge reconstruction projects are defined. The time-dependent probability of failure, time-dependent consequences of failure, and subsequently time-dependent safety risk associated with available options are estimated using a comprehensive reliability-based approach. In the next step, bridge life-cycle costs of each of the options are estimated using deterministic and probabilistic approaches. Later, the environmental impacts of each option are estimated using Life-cycle Environmental Impact Assessment (LCIEA). The combination of results of risk analysis, bridge life-cycle costs analysis, and environmental impacts assessment of all options are used to compare the reuse attractiveness of four options. The comparison is conducted by implementing a pairwise comparison technique to incorporate three criteria of safety, life-cycle costs, and environmental impacts of each option. The final decision on the reuse of an existing bridge foundation is made using the Analytical Hierarchy Process (AHP). Finally, Oxford Valley Road Bridge over US-1 in Bucks County, PA is used as a case study to demonstrate application of the proposed framework

A performance reporting tool for electricity service delivery for selected local South African municipalities

Thesis (PhD (Industrial Systems))--University of Pretoria, 2021.Basic services such as sanitation, waste removal, water, and electricity supplies are necessary for life, well-being, and human dignity. In South Africa, municipalities are the sphere of government constitutionally designated to provide these services. With months of rolling blackouts and volatile operating performance, Electricity Service Delivery (ESD) deserves some attention and improvement because it is setting the country on a pathway to national emergency, weakening investors’ confidence and stagnating the country’s already problematic economic growth prospects. Since improvement does not materialise spontaneously, deliberate and purposeful actions are required to understand the current state of ESD, extract stakeholders’ intentions for an improved ESD (diagnosis), and then devise means to operationalise the intentions. This study focuses on performance assessment with initial reporting capabilities to provide adequate information and insight for diagnosis of ESD within South African local municipalities. It starts by exploring a systematic literature review of available tools for diagnostic service performance assessment. Then, it extracts and validates, through a focus group session, the criteria which such tools must satisfy to be considered useful in the South African context. The study is based on a Design Science Research (DSR) methodology and follows an inquisitive process of multi-stakeholder engagement to extract evidence about existing functional and constructional ESD areas of concern/requirements. The study inductively develops an artefact, the ESD Performance Reporting Tool (ESD-PRT) to guide improvement in electricity service delivery in South African local municipalities. The ESD-PRT continuously extracts performance metrics from Power System Resources (PSR), citizens, and organisational competencies of the municipalities, with provisions for emerging areas of concern and requirements within design domains and sub-domains. It was evaluated for practicality and usefulness based on the DSR iterative approach and compared to the closest available similar solution. This entry point solution to an optimised local municipality ESD would guide the redesign of ESD and potentially save South Africa billions of Rands currently lost to energy losses, downtime in economic activities and social discontent occasioned by power outages and rolling blackouts. The study was demonstrated in three local municipalities geo-located in two different provinces. The researcher believes that the study outcome would apply to most local municipalities in South Africa. However, its applicability to metropolitan municipalities still needs to be tested.Industrial and Systems EngineeringPhD (Industrial Systems)Unrestricte