Multi-criteria Decision-making Process Research Articles

Importance of Government Roles for Market Expansion of Eco-Village Development Plan Establishment Research: Case Study in the City of Suwon, South Korea

Korean governmental ministries are promoting strategic projects to support cost-saving and low-carbon technologies in residential complexes and commercial buildings in the City of Suwon, South Korea. Suwon City will build throughout the city focusing on economic feasibility by selecting performance targets and using applied technologies for locations to be continuously expanded. This case study of Suwon shows that the local Korean government has prepared a project to spread eco-friendly residential complexes and is trying to introduce and realize eco-friendly construction standards proposed by the central Korean government. The central government is working to actively establish a system to promote eco-friendly construction technologies and encourages people to use eco-friendly construction methods and products. To build the demo-complex in the city, the role of the government was re-examined considering the universalization of energy and environmental technology through the analysis of case studies where these technologies were applied to residential complexes. The objectives of this research study are: (1) to establish a land use plan for the eco-village site in the City of Suwon, (2) to establish an external space plan, which includes the environmental aspects, and (3) to have alternative designs through a multi-criteria decision-making process. This study also used a cost-benefit analysis (BCA) to evaluate and ensure that there was no waste of the Korean government budget contribution, and to observe the business feasibility based on economic performance.

Appropriation of sustainability priorities to gain strategic advantage in a supply chain

PurposeThe authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.Design/methodology/approachSSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.FindingsThe case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.Practical implicationsThe study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.Originality/valueThis study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

Assisted requirements selection by clustering

Requirements selection is a decision-making process that enables project managers to focus on the deliverables that add most value to the project outcome. This task is performed to define which features or requirements will be developed in the next release. It is a complex multi-criteria decision process that has been focused by many research works, because a balance between business profits and investment is needed. The spectrum of prioritization techniques spans from simple and qualitative to elaborated analytic prioritization approaches that fall into the category of optimization algorithms. This work studies the combination of the qualitative MoSCoW method and cluster analysis for requirements selection. The feasibility of our methodology has been tested on three case studies (with 20, 50 and 100 requirements). In each of them, the requirements have been clustered, and then the clustering configurations found have been evaluated using internal validation measures for the compactness, connectivity and separability of the clusters. The experimental results show the validity of clustering strategies for the identification of the core set of requirements for the software product, being the number of categories proposed by MoSCoW a good starting point in requirements prioritization and negotiation.

Novel MCGDM with q-rung orthopair fuzzy soft sets and TOPSIS approach under q-Rung orthopair fuzzy soft topology

In this article, we study some concepts related to q-rung orthopair fuzzy soft sets (q-ROFS sets), together with their algebraic structure. We present operations on q-ROFSSs and their specific properties and elaborate them with real-life examples and tabular representations to develop influx of linguistic variables based on q-rung orthopair fuzzy soft (q-ROFS) information. We present an application of q-ROFS sets to multi-criteria group decision-making (MCGDM) process related to the university choice, accompanied by algorithm and flowchart. We develop q-ROFS TOPSIS method and q-ROFS VIKOR method as extensions of TOPSIS (a technique for ordering preference through the ideal solution) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje), respectively. Finally, we tackle a problem of construction business utilizing q-ROFS TOPSIS and q-ROFS VIKOR methods.

VASMA Weighting: Survey-Based Criteria Weighting Methodology that Combines ENTROPY and WASPAS-SVNS to Reflect the Psychometric Features of the VAS Scales

Data symmetry and asymmetry might cause difficulties in various areas including criteria weighting approaches. Preference elicitation is an integral part of the multicriteria decision-making process. Weighting approaches differ in terms of accuracy, ease of use, complexity, and theoretical foundations. When the opinions of the wider audience are needed, electronic surveys with the matrix questions consisting of the visual analogue scales (VAS) might be employed as the easily understandable data collection tool. The novel criteria weighting technique VASMA weighting (VAS Matrix for the criteria weighting) is presented in this paper. It respects the psychometric features of the VAS scales and analyzes the uncertainties caused by the survey-based preference elicitation. VASMA weighting integrates WASPAS-SVNS for the determination of the subjective weights and Shannon entropy for the calculation of the objective weights. Numerical example analyzing the importance of the criteria that affect parents’ decisions regarding the choice of the kindergarten institution was performed as the practical application. Comparison of the VASMA weighting and the direct rating (DR) methodologies was done. It revealed that VASMA weighting is able to overcome the main disadvantages of the DR technique—the high biases of the collected data and the low variation of the criteria weights.

Latency Minimization in a Fuzzy-Based Mobile Edge Orchestrator for IoT Applications

Currently, matching the incoming Internet of Things applications to the current state of computing and networking resources of a mobile edge orchestrator (MEO) is critical for providing the high quality of service while temporally and spatially changing the incoming workload. However, MEO needs to scale its capacity concerning a large number of devices to avoid task failure and to reduce service time. To cope with this issue, we propose MEO with fuzzy-based logic that splits tasks from mobile devices and maps them onto the cloud and edge servers to reduce the latency of handling these tasks and task failures. A fuzzy-based MEO handles the multi-criteria decision-making process to decide where the offloaded task should run by considering multiple parameters in the same framework. Our approach selects the appropriate host for task execution and finds the optimal task-splitting strategy. Compared to the existing approaches, the service time using our proposal can achieve up to 7.6%, 22.6%, 38.9%, and 51.8% performance gains for augmented reality, healthcare, compute-intensive, and infotainment applications, respectively.

A Cost Minimization Resource Allocation Model for Disaster Relief Operations With an Information Crowdsourcing-Based MCDM Approach

The occurrence of a sudden, calamitous, and unfortunate event requires a quick response that can reduce damage and save lives, fulfilling the basic humanitarian needs of the affected population. National or international organizations and agencies may be initiated to provide assistance but local agencies need to act as soon as possible, because they are familiar with the geographical location as well as the local conditions of the affected areas. Effective coordination of disaster assistance is often crucial, particularly when resources are limited in the local agencies and the demands are more. In such conditions, it becomes a challenging job for the agencies to distribute resources so that the resources are optimally used. This article is initiated to address the challenging fact by introducing the concept of priority measure for demands. In this regard, ranking of the affected areas based on resource requirement for a disaster scenario is evaluated through a multicriteria decision-making process. A formula is developed to find the numerical value of priority factor collecting the responses of requirement of relief materials using information crowdsourcing. To this contingent, a three-echelon emergency solid transportation problem is formulated, in which the demand with the most priority is fulfilled by the local agencies in echelon-I, and the remaining demand is fulfilled by the other national or international agencies in echelon-II along with a restoration of local agencies in echelon-III. A numerical example is presented for the sake of model performance and to test the convergence using LINGO and CPLEX optimization solvers. The model is also designed using a genetic algorithm to obtain the optimal solution. Moreover, a brief analysis of the model is presented by considering 11 different numerical instances to enhance the efficacy of the model in real-life applications.

A hybrid intelligence approach for sustainable service innovation of smart and connected product: A case study

Nowadays, smart and connected product (SCP) is gradually replacing the traditional functional products, which has attracted widespread attention from the industry and academia. Service innovation, as a crucial part of SCP iterative improvement, is a multi-criteria decision-making (DM) process facilitated by intelligent automation (IA) and cognitive technologies. However, product user’s intelligence (e.g. physiological feeling) that can intuitively reflect and evaluate the product service satisfaction is rarely considered in the process of service innovation. Hence, it is difficult to measure the product users’ preferences with precise numerical terms to make a strategic decision. In order to fill this gap, a hybrid intelligence approach is proposed to perform the service innovation for SCP. The product-user data (e.g. subjective data and physiological data) and product-sensor data are collected and used for the process of service innovation. A smart group spinning bicycle system is presented as an elaborated case study to illustrate the proposed architecture and approach. The service innovation of real-time and dynamic monitoring, user participation improvement and smart feedback manners are achieved. In addition, an ergonomic experiment is conducted to validate the effectiveness of the proposed approach in implementing the service innovation for SCP.

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

Life cycle sustainability assessment of window renovations in schools against noise pollution in tropical climates

Because of the significant increase in the number of noise complaints, reducing and limiting noise pollution have become prevalent subjects related to the retrofitting of school buildings. The present case study considers a multi-apartment school building in the city of Johor, Malaysia. The life cycle criteria evaluated in this study are carbon dioxide (CO2) emissions and the cost and social impacts of each window type. The preliminary assessment showed that the school's current noise level is 74.31 dB (A), which exceeds the acceptable threshold of 55 dB (A). In the next step, three more windows were applied and reevaluated, with the triple glazing window performing the best (48.66 dB (A)), followed by the double glazing window (51.3 dB (A)). In terms of carbon emissions and cost, the preference window had the best performance. Meanwhile, considering the social aspect, the double glazing window performed the best. Because three different windows were deemed the best choice depending on which of the four criteria was considered, multi-criteria decision making (MCDM) was applied by TOPSIS to weigh and estimate each alternative. The final decision was made by giving priority to the four criteria as follows: noise (0.322), cost (0.257), CO2 emissions (0.227), and SLCA (0.194). The MCDM process revealed that the double glazing window is the most sustainable choice for school buildings. Furthermore, two sensitivity analyses were performed to eliminate human subjectivity involved in AHP.

Multi-Criteria Decision Making Process in Metropolitan Transport Means Selection Based on the Sharing Mobility Idea

The article presents the idea of modeling the decision-making process in the field of the metropolitan areas transport system. Due to the increasing process of metropolization and urbanization, which is predicted to be 68.4% worldwide and 83.7% in Europe in 2050, the issue will be even more sophisticated. The problem of depletion of transport network capacity as well as the implementation of modern technology solutions forces metropolitan committees to apply tools for metropolitan passenger transport system optimization. Significantly, the policy and regulations on sustainable urban mobility management are based on the mobility demand predictions and understanding of the travel decision-making process of citizens. The scientific purpose of this article is to build a mathematical model, as a tool supporting the multi-criteria decision-making process regarding the choice of means of transport in a developing metropolis. The issue raised in this article considers the most important research areas of the metropolitan transport means selection, which includes transport safety, qualitative, financial, and ecological aspects. The model was implemented in Silesian Metropolis in Poland with a particular emphasis on sharing mobility transport means users. As a result, a ranking of sharing transport means was developed, which is a piece of significant information for planners and future investors in the development of the metropolitan transport system.

A novel entropy-based weighted attribute selection in enhanced multicriteria decision-making using fuzzy TOPSIS model for hesitant fuzzy rough environment

The existing approaches of multicriteria decision-making (MCDM) process might yield unreliable and questionable results. The notable challenges of MCDM approaches are rank reversal paradox and uncertainty. The prime inspiration for researchers is the MCDM for hesitant fuzzy sets (HFSs). In some scenarios, the decision-makers could not choose one from numerous values while expressing their preferences. HFS which is the extension of fuzzy sets (FS) is found to be helpful in solving such decision-making (DM) problems. The DM process is revolutionized with the commencement of powerful and efficient tools of data representation for expressing vagueness and uncertainty in data sets as FSs (both generalized and hesitant ones). This paper copes with one such novel approach that involves entropy-based attribute weighting, followed by an evaluation of approximate sets in the fuzzy rough framework. Correlation of the input alternatives in respect of evaluation criteria and the output class is evaluated. With the fuzzy technique for ordered preference by similarity to ideal solutions (FTOPSIS), the generated correlation matrix is utilized for calculating the degree of closeness ( delta ) of the output classes to the input alternatives. This paper made a novel contribution of performance indicator centered on FTOPSIS for the hesitant fuzzy rough domain. The proposed method’s efficiency is established through comprehensive and systematic experimentation on datasets utilized by researchers globally. The proposed algorithms prove its ability to handle datasets that involve human-like hesitant thinking in the MCDM system by contrasting with the existing ones.

Developing and evaluating a proposed health security framework in IoT using fuzzy analytic network process method

Internet of Things (IoT) played a significant role in Healthcare. One of the major challenges in IoT health is security. For this reason, regarding the past studies, there is a lack of a comprehensive security framework that encompasses all layers of IoT. This article offers a security framework with four layers of sensor, network, service, and application. These layers help to develop security mechanisms within the proposed framework by providing security solutions. Also, security improvement has been influenced by the Multi Criteria Decision Making (MCDM) technique. To evaluate this framework, a conceptual model was defined with six critical criteria including security, network, services, interoperability, privacy, and reliability. The validity and reliability of this conceptual model were analyzed using SPSS 24 and AMOS 18. After proving the important criteria in the proposed model, we used the MCDM process of fuzzy analytic network process to prioritize the important criteria. The evaluation results of the conceptual model in the proposed framework, after analyzing the routes, show the validity of the proposed criteria. Therefore, in this model, 8 out of 12 communication routes between the criteria have a positive and significant relationship and 4 routes do not. The results of prioritizing important criteria in the research model, using analytic network process method, show that the network criterion, and authentication and validation sub-criteria have the highest priority among the proposed criteria. Thus, it can be decided that security solutions in the network layer and authentication and validation sub-criteria help to develop the proposed security framework.

A Socio-economic Indicator for EoL Strategies for Bio-based Products

In recent years, the bioeconomy has received increased attention from policy makers and practitioners, leading to a first switch from fossil to bio-based products. To open up new market opportunities, transparent information on the sustainability of bio-based products must be provided, taking into account all life cycle stages. The determination of suitable end of life (EoL) strategies furthers this aim. Notably, the socio-economic aspects of EoL alternatives associated with bio-based products have been neglected. This paper contributes to filling this gap by proposing a new socio-economic indicator for EoL (SEI-EoL). The SEI-EoL is developed from an integrated analytic hierarchy process–multicriteria decision analysis (AHP-MCDA) model based on experts' involvement, and is capable of measuring and comparing the socio-economic performance of EoL alternatives for bio-based products. The SEI-EoL is applied to the specific case of polylactic acid (PLA)–based film for food packaging. The results indicate that value chain actors are the most influential category of stakeholders in EoL management, and the assessment of criteria shows that waste disposal cost, resource efficiency and EoL responsibility play a key role in this management. The highest value EoL strategy for PLA-based film for food packaging is mechanical recycling, followed by chemical recycling. Circularity assessment, policy maker responsibility and incentives for recycled materials/green processes represent the most relevant items to consider for policy recommendations and actions.

Sistema de indicadores para gestão de projetos multidiscilinares de ensino

O objetivo do presente artigo é o de propor um sistema de indicadores para gestão de Projetos Multidisciplinares Escolares (ProjMEs), para servir de apoio a tomada de decisão em escolas. Para tal, foi desenvolvida uma survey, a partir de uma revisão sistemática da literatura combinada com uma entrevista em profundidade com stakeholders de projetos. Os resultados desta survey serviram para mapear o cenário brasileiro da gestão de ProjMEs. E, a partir dos resultados deste mapeamento, combinados com a entrevista em profundidade foi gerado o Sistema de Indicadores para Projetos Multidisciplinares Escolares (SID-ProjMEd) baseado em oito Objetivos de projetos: (i) Ampliação do Conhecimento do aluno; (ii) autonomia do aluno; (iii) convívio social; (iv) experimentação prática do conteúdo; (v) interdisciplinaridade; (vi) protagonismo do aluno; (vii) variação do cotidiano escolar; e (viii) Iron Triangle. O SID-ProjMEs é composto por estes oito objetivos e 24 indicadores, sendo três para cada Objetivo.

COMPETENCY MODEL AND MULTI-CRITERIA DECISION MAKING IN TOURISM AND HOTEL INDUSTRY IN SERBIA

In today’s global and highly competitive business world what essentially differentiates one organization from another are the people who work there. Regardless of the technological advantages of a hotel or a travel agency, the people, their development, motivation and satisfaction are key factors for success on the market. Therefore, many hotels started applying competency models to determine skills, knowledge and personal characteristics needed for good business performance. The advantages of this model are also reflected in the recruitment, selection, training, development, promotion, performance evaluation, and rewarding employees. The emphasis is on the selection of career advancement criteria by the multi-criteria decision-making process, the essence of which is to identify all available criteria for comparing possible alternatives, to compare all the alternatives according to the selected criteria, and thus to choose the most favourable alternative. The aim of the research is to develop a software package that will, based on executive manager’s selection in a hotel and a travel agency, present specific steps of business decision-making application in the field of human resources. The AHP method is used to conduct a weight analysis of professional competencies prioritized for future reference in terms of career advancement. Sampling methods for data collection from eight top hotel managers and five travel agency managers are used for the purpose of this paper, as well as qualitative and quantitative surveys including interviews and questionnaire. A detailed case study illustrates the application of the proposed evaluation system for management staff advancement in tourism and hotel industry. The estimated results show that the most important criterion is professional knowledge, followed by business planning and subsequently team work and foreign language skills.This solution is general and can also be applied in candidates ranking for employment, promotion or training.

Application specific thresholding scheme for handover reduction in 5G Ultra Dense Networks

Traditional multi-criteria decision making (MCDM) algorithms are used in the handover of user equipment (UE) in an Ultra Dense Network (UDN). UDN refers to the increased density of the Radio Access Technologies (RATs) in a region which leads to the overlapping of the areas covered by individual RATs. MCDM algorithms such as TOPSIS, PROMETHEE and SAW are used to initiate handovers between these RATs based on the parameters obtained by the UE from each of the overlapping networks. However, initiating a handover abruptly and frequently, in case of availability of a new RAT without any thresholding technique proves to be unfriendly to the system resources. This can degrade the performance of the system. In this paper, a thresholding approach to the handover procedure is integrated to the MCDM process for the selection of RATs. First, an application-specific approach has been used in the selection of weights using the analytical hierarchy process which, is depending upon the application being used by the user. Then the ranking of the available RATs is done using the various MCDM algorithms and depending on the threshold specified for a handover, a decision is made whether to perform the handover process or not. In the case of streaming class of network traffic, the proposed method improves the performance of the system and reduces the handover by 13.14%, 19.35% and 8.62% of RAT modifications for TOPSIS, PROMETHEE and SAW respectively.

Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach

The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.

Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection

Multi-Criteria Decision Making (MCDM) is a discipline aimed at supporting decision-makers to settle on an ideal choice within the sight of different and clashing criteria. MCDM strategies have developed to oblige different kinds of utilizations. One comprehensively utilized MCDM strategy is the Analytic Hierarchy Process (AHP). AHP, in spite of its simplicity in idea, it can't consider imprecise input. Throughout the years, fuzzy logic has demonstrated its effectiveness as a MCDM technique. It includes a few preferences inside uncertain, imprecise and obscure settings than AHP and other MCDM strategies. Consolidating Fuzzy strategies with AHP is one methodology for taking care of the entangled issues of AHP. The significance of this study is to provide baseline information to the construction clients and consultants on the importance of contractor's prequalification decision criteria to be adopted, which will eventually translate to a better decision making and increase project performance. Taking into account our past research, this paper proposed a Feedback Integrated Fuzzy Analytic Hierarchy Process (FAHP) model for ranking decision criteria for contractual worker determination by combining the selection process and consistency control module. This consistency control module contains a feedback module that generates advice to decision makers so as to check the irregularity in their decisions' during pairwise comparisons. In this paper, the performance investigation of the proposed FAHP model is validated on datasets from three diverse FAHP models. The proposed model accuracy obtained when tested with three distinctive datasets were 98.2%, 99.99%, and 98.24% respectively. The results established the adequacy and uselfulness of the proposed FAHP model in ranking contractor decision criteria. The research additionally guide decision-makers on picking distinctive FAHP algorithms in assessing and ranking decision criteria utilized in contractors selection.

Sewage treatment process refinement and intensification using multi-criteria decision making approach: A case study

In assessing sewage treatment systems, decision makers now have to consider sustainability, affordability, and reliability beyond functionality. To facilitate this complex assessment, multi-criteria decision making (MCDM) tools have evolved recently. This case study illustrates an application of the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) method in the MCDM process in evaluating five sewage treatment systems: activated sludge treatment followed by anaerobic digestion (P1); photoautotrophic algal treatment followed by hydrothermal liquefaction (P2) or by anaerobic digestion (P3); and, mixotrophic algal treatment followed by hydrothermal liquefaction (P4) or by anaerobic digestion (P5). This evaluation is based on 15 criteria aligned with the United Nation’s sustainable development goals. Based on energy recovery, the five processes ranked as follows: P2; P3; P4; P5; and P1. But, when all the 15 criteria are considered, P4 ranked as the most preferred option followed by P5 and P2. Utility of the MCDM approach in highlighting drawbacks of the options and identifying areas for improvement is illustrated. This study indicated that recovering phosphates and energy from the digested sludge in P1 offer greater potential to improve its ranking than by minimizing its energy demand for aeration. Increasing biomass density in the mixotrophic system can further improve its ranking, minimizing footprint and maximizing recoveries. Since the energy-intensity of hydrothermal liquefaction in P2 and P4 (4.57 and 23.23 kWh/kg BOD, respectively) is higher than that of anaerobic digestion in P1 (0.01 kWh/kg BOD), nutrient recovery from its byproducts has to be maximized to justify its selection.