Multi-objective Decision Analysis Research Articles

Transferable preference learning in multi-objective decision analysis and its application to hydrocracking

AbstractHydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

A multicriteria model for assessing item importance and risk using operational data from military supply chains

PurposeThis paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.Design/methodology/approachThe approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.FindingsRisk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.Originality/valueTo better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Knowledge-driven networking and ambidextrous innovation equilibrium in power systems transition

PurposeThe ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.Design/methodology/approachThe authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.FindingsResults show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.Practical implicationsThe study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.Originality/valueThe primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis

The present work aims at (a) carbonizing agriculture biomass residue; (b) characterizing the obtained biochar; and (c) exploring its potential use for energy/resource recovery purposes. Six types of biomass were carbonized. The biochar was investigated through scanning electron microscopy with energy dispersive X-ray spectroscopy detector, thermogravimetric (TGA), proximate, ultimate, and Brunauer–Emmett–Teller analyses, along with bulk density, pH, electrical conductivity, and salt content measurements. The results served as input data for multi-criteria, multi-objective decision analysis of biochar, aiming to evaluate its best application prospective. The TGA identified two general stages: devolatilization (stage 2: 180–560 °C), and combustion (stage 3: 560–720 °C). The activation energy of stage 2 decreased with an increasing heating rate, but the opposite trend was observed for stage 3. The biochar CO2 adsorption suggested possible applications beyond energy conversion technologies. The decision support analysis revealed that peach stones, cherry stones, and grape pomace biochar achieved the most promising results for all evaluated applications (biofuel; catalyst; CO2 sequestration and soil amendment; supercapacitor) in contrast to colza, softwood, or sunflower husks char.

Sequential optimization of process and supply chains considering re-refineries for oil and gas circularity

The United Nations Climate Change Conference COP26 held in 2021 concluded a global effort to hasten the energy transition toward a net-zero emission industry. As such, green initiatives, which transition the conventional oil and gas (O&G) sector towards a circular economy (CE) are necessary. In this work, the integration of waste oil re-refining technology is proposed as a potential strategy to enhance the circularity of the O&G industry. A two-step sequential model, which incorporates multiple systematic analytical tools (e.g., multi-objective decision analysis, information entropy, geospatial information, clustering, and routing analysis) is developed to determine: (i) optimal waste oil re-refinery technologies, and (ii) optimal supply chain design, which addresses the location for setting up the process facilities and the delivery routes, with the consideration of both economic and environmental performances. The effectiveness of the proposed strategy is demonstrated through a case study in Malaysia (that covers both East and West Malaysia). The analysis showed that the proposed strategy is capable of improving economic and environmental performances by about 9.59% and 46.55%, respectively. This work is essentially a useful reference for decision-makers and policymakers in making nationwide transition planning in the O&G sector.

Mitigation of climate change in Mediterranean existing social dwellings through numerical optimization of building stock models

Retrofitting the existing building stock is widely accepted as a crucial factor to reaching 2030 and 2050 climate and energy targets, given that the building sector is among the top three most dominant energy consumers. This paper presents a bottom-up study which uses calibrated and parameterized energy stock models (building archetypes), while also incorporating building stock information from a large database. The thermal performance of the existing social housing stock of southern Spain is assessed through dynamic simulation under present and future climate change scenarios. Subsequently, several passive and low-cost operation-related strategies are numerically optimized through genetic algorithms to determine the best retrofit solutions, taking into consideration global warming scenarios. A multi-objective decision analysis is carried out by optimizing annual overheating hours (%), annual undercooling hours (%), and investment costs (€/m2). Among the conclusions reported, it is important to note the feasibility of implementing low-cost retrofit strategies considering investment costs of up to around 200 €/m2, which would lead to average annual overheating and undercooling hours below 55 % and 45 %, respectively. However, retrofit solutions exclusively based on passive and low-cost operation measures were proven to be significantly limited to improve thermal comfort results in the social stock.

Sustainability Development: Assessment of Selected Indicators of Sustainable Energy Development in Poland and in Selected EU Member States Prior to COVID-19 and Following the Third Wave of COVID-19

An important question in the literature on climate change and sustainable development is the relationship between countries’ economic growth, household electricity consumption and greenhouse gas emissions. Despite the ongoing COVID-19 pandemic and related economic restrictions, sustainable economic growth remains at the forefront of the global development agenda. However, given the strong relationship between the ever increasing electricity consumption and greenhouse gas CO2 emissions, an increasing number of scientists have been questioning the feasibility of the planned emission reduction. In my research, I strove to determine whether there exists a relationship between the change in the structure of electricity consumption of households in selected EU Member States (15 countries), the impact of innovation, changes in electricity prices and economic growth, and CO2 emissions in 2007–2019, prior to the outbreak of the COVID-19 pandemic, and following its third wave (2021). The aim of the article is to propose a synthetic index to assess the degree of sustainable energy development (SISED) in selected EU countries. Multiobjective decision analysis (MODA) was applied in order to assess the sustainable energy development of the selected European countries. Research findings may contribute to both literature and practice if they are applied by individual EU countries in the process of formulating directions aimed at achieving sustainable energy development.

A variable multi‐time‐scale based dual estimation framework for state‐of‐energy and maximum available energy of lithium‐ion battery

To lower the computation burden and enhance co-estimation reliability under unpredicted operating conditions, this paper presents a novel variable multi-time-scale based dual estimation framework for state-of-energy (SOE) and maximum available energy. Through forgetting factor recursive least squares (FFRLS) based model parameters identification method, the first-order RC model is online built firstly to simulate battery dynamics. Subsequently, identified model parameters are inputted into an adaptive extended Kalman filter to predict SOE. Meanwhile, with battery data and two estimated SOE, inaccurate maximum available energy can be further updated by FFRLS when energy accumulation reaches pre-defined threshold. Especially, to determine the optimal macro time-scale considering co-estimation performance comprehensively, a multi-objective decision analysis method by fusion of analytic hierarchy process and the entropy weight is innovatively proposed. The dual estimation accuracy and robustness ability of the proposed framework are verified with experimental data of Federal Urban Driving Schedule tests conducted under various temperatures, whose results show that the presented method has satisfactory co-estimation accuracy and robustness ability. Furthermore, the comparison with other algorithms not only indicates the necessity of maximum available energy updating on SOE prediction but also the superiority of the presented framework on dual estimation accuracy and computational cost.

Integrating emerging and existing renewable energy technologies into a community-scale microgrid in an energy-water nexus for resilience improvement

Rapid urbanization around the world, especially in developing countries, has resulted in a dramatic increase in the need for energy and water resources. This global trend calls for a sustainable shift in energy supplies for urban areas that can deliver robust, economic, environmental, and social benefits. This paper presents a novel planning strategy, along with an integrated system design of microgrids (MGs), to help realize sustainable energy supply patterns in a decentralized manner for improving community resilience and environmental sustainability. The exploration started with the screening of emerging and existing renewable energy technologies that can be integrated and deployed in a community-scale MG. Seamless integration of energy and water technology hubs was examined to develop a sustainable system design of MG in an energy-water nexus within the community under study. Then, a multi-objective programming model was formulated for decision analysis by systematically considering the cost, greenhouse gas (GHG) emissions, and water consumption of the MG. In comparison with existing utility grids, the multi-objective decision analysis helps find a suite of tradeoffs among three economic and environmental indicators, i.e., cost, GHG emissions, and water consumption. Case studies show that the proposed system design of the MG for the urban community can cut more than 75.5% GHG and 75.6% water consumption, compared to the benchmark in a centralized utility system. Case studies also show that this planning strategy can harmonize environmental, economic, and social benefits, making the traditional wide-area synchronous grid greener, cheaper, and more reliable. It is indicative that the system design of MG in an energy-water nexus will improve the reliability of energy supplies and strengthen community resilience, thereby increasing overall economic benefits in the long run.

Rapidly assessing cobenefits to advance threat-management alliances.

Conservation strategies aimed at reducing threats to biodiversity can have significant implications for multiple sectors in a socioeconomic system, but these cobenefits are often poorly understood. For example, many of the threats to native species also impede agricultural production, yet agriculture is typically perceived as in competition with conservation objectives. Although a comprehensive, multiobjective decision analysis is usually beyond the scope and capacity of conservation decision makers, failing to incorporate key socioeconomic costs and benefits into conservation decision-making processes can result in missed opportunities for diversifying outcomes and creating cost-sharing multisectoral partnerships. We devised a straightforward and readily interpretable approach to incorporate cobenefits into a threat-management prioritization approach. We used it to analyze the agricultural cobenefits of implementing 9 invasive animal management strategies designed to ensure the persistence of 148 threatened species across Australia's Lake Eyre Basin over 50 years. A structured elicitation process with 24 participants (scientists, land managers, agriculturalists, and other stakeholders) was used to collect information on each strategy, including costs, technical and social feasibility, benefits to native threatened species, and cobenefits to agricultural production systems. The costs of targeted invasive animal management to save threatened species across the basin (AU$33 million/year) outweighed the overall benefits to the agricultural industry (estimated AU$226 million/year). The return on investment for these management strategies varied substantially when agricultural cobenefits were considered alongside threatened species benefits and showed synergies and challenges. Our approach demonstrates the value of incorporating cobenefits of conservation actions into cost-effectiveness analyses to guide potential investment and partnerships and to diversify implementation pathways.

Multi-objective decision analysis for data-driven based estimation of battery states: A case study of remaining useful life estimation

Data-driven methods, which can explore the relationship among battery external parameters and battery states automatically without establishing complicated battery model, have been intensively applied to estimate state of charge (SOC), state of health (SOH) and remaining useful life (RUL) etc. Nevertheless, relatively few researches have been done on the selection of data-driven model parameters and the determination of model with the optimal comprehensive performance. To address these questions, this paper presents a multi-objective decision method for data-driven based estimation of battery states. This method adopts the combination of the analytic hierarchy process and the entropy weight method together with integrating subjective and objective weights. The mean absolute error and root squared mean error of training-set, validation-set and test-set are used as accuracy indexes, and modeling time is seen as computation burden index. These seven indexes are applied as objective criteria for the multi-objective evaluation method, successfully evaluating the comprehensive performance of estimation model. Moreover, with three cases for RUL estimation, the specific application process of selecting the model with the optimal comprehensive performance by the proposed method is presented in detail.

Multi-objective optimization and decision analysis of a system based on biomass fueled SOFC using couple method of entropy/VIKOR

A power generation system including solid oxide fuel cell (SOFC), gasifier, and high-temperature sodium heat pipes was modeled using the Lagrange method of undetermined multipliers. The model validation was performed in comparison with the data available in the literature. The results showed that the presented model had a high validity. The parametric analysis showed that there was an optimum value for current density to achieve the highest net power but the exergy efficiency was reduced gradually with the current density. Response surface methodology (RSM) was utilized for multi-objective optimization purpose. The analysis of variance (ANOVA) was performed and different regression models with high validity were presented for power (R2 = 93.40%) and exergy efficiency (R2 = 96.22%). The one-objective optimization was performed using the interaction effect of parameters derived by ANOVA. The multi-objective optimization results indicated that the optimum power was achieved at relatively low exergy efficiencies and the optimum exergy efficiency was accompanied by low powers. A stepwise multi-objective optimization procedure was conducted and an optimum region was recognized. The performance of the system was assessed from energy, exergy, and environmental viewpoints for different biomasses including pine sawdust, groundnut shell, rice straw, rice husk, eucalyptus, sunflower shell, and sugarcane bagasse. Multi-criteria decision making (MCDM) analysis was implemented in order to select the biomass with the best performance. The cold gas efficiency, energy efficiency, exergy destruction rate, CO2 emission, and fuel flow rate were selected as different criteria. The MCDM results revealed that eucalyptus had the best performance.

The Development Trend of Marine Economy based on Multi-objective Decision Analysis

ABSTRACT Yao, J.-H., 2019. The development trend of marine economy based on multi-objective decision analysis. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engine...

Sustainable Optimization for China’s Hydropower Project Investment Portfolio Using Multiobjective Decision Analysis

The development of renewable energy becomes increasingly important because of exhaustion of fossil energy. Hydropower is one of the most important ways to generate electricity from renewable energy because of its relatively stable output among them. However, hydropower project development with some inherent characteristics is highly susceptible to social and natural environments, which complicates the investment process. For this purpose, this paper proposes a feasible comprehensive optimization model of portfolio investment from the perspective of sustainable development, describing the tradeoff relationship between economic, social, and ecological factors. As a hybrid uncertain NP-hard optimization problem, there are three critical challenges: (1) achieving comprehensive balance between economy, society, and ecology; (2) identifying available multiple conflicting objectives and reasonable constraints; (3) analysing the hybrid uncertain environment. Therefore, a practical problem-oriented multiobjective decision analysis model is established. Then, a multiobjective adaptive particle swarm optimization algorithm is designed to solve the model. Finally, a case study is carried out to verify the practicality of the model and the effectiveness of the improved algorithm. The result demonstrates that the model can be applied as a useful decision-making tool for decision-makers in sustainable hydropower project development.

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi‐objective model

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides Microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimization (MOO) and decision analysis is put forward to address this model. First, a powerful heuristic optimization algorithm, called the {\theta}-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. And thereby, the best compromise solutions (BCSs) are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

Teaching Performance Evaluation in Smart Campus

With the rapid development of modern teaching technology, the construction of smart campus has become the focus of modern college education reform. The application of technologies, such as the Internet of Things and big data, plays an important role in improving the teaching environment of colleges and universities, improving the utilization of teaching resources, and the flexibility of education. As an important part of campus activities, teaching performance evaluation scientifically and effectively utilizes teaching information and teacher and student interaction information to evaluate teachers’ teaching performance, which helps to motivate teachers’ work enthusiasm, improve teaching quality, and enhance school core competitiveness. This paper analyzes the salient features of smart campus from the perspectives of technology, business, and construction mode, and proposes a smart campus architecture model. According to the research content of teaching performance evaluation, the framework model of smart campus education data collection and storage platform is established, which provides a reference model for the construction of smart campus in colleges and universities. The evaluation of teaching performance in smart campus first analyzes the shortcomings of traditional evaluation methods and proposes the necessity of combining teaching performance evaluation with modern technology. Second, six principal components were determined using the PCA algorithm. Then, use the AHP to calculate the weights of each layer of the indicator set, avoiding the decision errors caused by subjective factors. Finally, the gray correlation degree is used to improve the TOPSIS algorithm for multi-objective decision analysis. The evaluation results of the AHP-TOPSIS teaching performance model are consistent with the actual situation. The application of the smart campus education data platform combined with the AHP and the gray correlation improvement TOPSIS algorithm is more targeted to the teacher’s teaching performance evaluation and provides a new evaluation method for scientific performance evaluation, and avoid the problem of strong subjectivity of traditional teaching performance evaluation.

The Unreasonable Destructiveness of Political Correctness in Philosophy

I submit that epistemic progress in key areas of contemporary academic philosophy has been compromised by politically correct (“PC”) ideology. First, guided by an evolutionary account of ideology, results from social and cognitive psychology and formal philosophical methods, I expose evidence for political bias in contemporary Western academia and sketch a formalization for the contents of beliefs from the PC worldview taken to be of core importance, the theory of social oppression and the thesis of anthropological mental egalitarianism. Then, aided by discussions from contemporary epistemology on epistemic values, I model the problem of epistemic appraisal using the frameworks of multi-objective optimization theory and multi-criteria decision analysis and apply it to politically correct philosophy. I conclude that philosophy guided by politically correct values is bound to produce constructs that are less truth-conducive and that spurious values which are ideologically motivated should be abandoned. Objections to my framework stemming from contextual empiricism, the feminine voice in ethics and political philosophy are considered. I conclude by prescribing the epistemic value of epistemic adequacy, the contextual value of political diversity and the moral virtue of moral courage to reverse unwarranted trends in academic philosophy due to PC ideology.

An Empirical Application of Goal Programming and AHP in Real Estate Assessment

The Goal Programming (GP) is one of the many models which have been developed to deal with the multiple objectives decision-making problems. This model allows taking into account, simultaneously, many objectives while the decision-maker is seeking the best solution from among a set of feasible solutions. At first, the aim of this study is to compare the objectives of the decision maker based on the Analytical Hierarchy Process (AHP) and to rank the objectives according to the AHP process and once the objectives are set then apply the GP to the facility allocation in a real estate rental property. The objective of decision maker is not only to maximize profit rather the decision maker has multiple objective to achieve. The methodology presented in this study can help the decision maker to formulate viable location strategies in the volatile and complex facility allocation environment. This is an empirical study applied to a local business developer utilizing multi objective decision analysis tools.

The most suitable scheme selection of mechanical product configuration based on multi-objective decision analysis

In the process of mechanical product configuration, if there is more than one feasible scheme, it is required to choose the one which is most consistent with the actual conditions of core enterprise. These actual conditions include cost, quality, delivery time and other factors that are related to the interests of the core enterprise. We propose to find the most suitable scheme from the feasible scheme set by using data envelopment analysis. By comparing the projected variable and the original data, we also know how much improvement can be achieved for a specific scheme. The above algorithm is verified and illustrated using a case study.

The most suitable scheme selection of mechanical product configuration based on multi-objective decision analysis

The most suitable scheme selection of mechanical product configuration based on multi-objective decision analysis