Prioritization Of Alternatives Research Articles

Abstract 4926: Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data

Abstract Somatic variant calling involves the identification of genomic alterations that occur in somatic cells, requiring deep coverage to enable high sensitivity for low-frequency variants. Characterizing somatic variants across the entire genome therefore benefits from novel cost-efficient sequencing platforms, such as UG100. Here, we present optimization of variant calling tools for short and structural variants on WGS and WES data from UG100. For calling short variants, we optimized DeepVariant (DV) for somatic calling using data from matched tumor-normal sample pairs, improving both variant calling accuracy and pipeline running time (up to 10-fold). We defined the task of somatic variant calling as deciding if the pileup image containing reads from the tumor and normal samples represents a true somatic variant (vs a germline variant or artifact). The challenge of robust variant calling using deep learning models is exacerbated in somatic calling, where sequencing depth and coverage variability are typically high. Our optimized DV overcomes these challenges by several data sampling strategies. First, allele-frequency preserving down-sampling reduces randomness of read sub-sampling in high coverage regions. Second, alternative allele prioritization samples alt-allele supporting reads first allowing to call variants at very high coverage loci without sacrificing sensitivity and computational efficiency. Finally, a Panel-of-Normals based on targeted WES data provides an additional improvement of precision for this assay type. We used these strategies to train two models, one for tumor characterization using WGS (T/N coverage: 40x-150x/40x-100x), and one for deep WES (T/N coverage: >500x/>120x). We called variants on simulated tumors using the WGS model. For VAF>10% the model showed SNV recall >98% and indel recall >95% with false-positive rate of 0.2/Mb. For VAF range of 5-10%, indel recall was 67% and SNV recall was 86%. To demonstrate the utility of our somatic variant calling, we applied the models to call somatic variants from well characterized cancer cell lines: COLO829, HCC1395 and HCC1143. Results showed F1>90% for variants with VAF>10%. The WES model was used to reliably call variants at VAF>5% on simulated tumors with average SNV recall of 99% with precision >99% and indel recall >86% with precision >94%. To analyze structural and copy-number variations, we optimized the assembly engine of GRIDSS to enable fast calling of structural variations and demonstrate that Control-FREEC can be used to call copy number variants. SV calling on COLO829/COLO829BL achieved sensitivity >95%. In conclusion, our research highlights the utility of UG100 within the field of oncology, demonstrating its capacity for comprehensive and precise somatic variant detection, both on WGS and WES data. Citation Format: Doron Shem-Tov, Maya Levy, Gil Hornung, Ilya Soifer, Hila Benjamin, Ariel Jaimovich, Adam Blattler, William Brandler, Robert Sugar, Isaac Kinde, Omer Barad, Doron Lipson. Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4926.

Alternative prioritization for mitigating urban transportation challenges using a Fermatean fuzzy-based intelligent decision support model

Practitioners and decision-makers often face difficulties in selecting and prioritizing effective strategies to address challenges to sustainable urban transportation development. Although there has been considerable research conducted on the subject, the Tanzanian context, which is greatly affected by social and environmental problems, has received inadequate attention. Therefore, this study intends to bridge this gap by pinpointing the obstacles to sustainable urban transportation and proposing the most appropriate strategies to tackle them. The study proposes seven strategies and determines five criteria to prioritize them. To accomplish this, the study proposes a novel Fermatean fuzzy-based intelligent decision support model to assess the criteria weights and prioritizes strategies based on the weighted criteria. The study validates the proposed methodology by conducting a sensitivity analysis, which indicates that restricting car use (A5), improving sector coordination (A1), and conducting extensive research on transportation issues (A7) are the top three strategies for promoting sustainable urban transportation. The study’s findings hold significant value in providing urban transportation planners with helpful guidance to develop optimization techniques that can improve transportation systems.

Addressing a decision problem through a bipolar Pythagorean fuzzy approach: A novel methodology applied to digital marketing

Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing. The proposal considers both the positive and negative dimensions of data, leading to better-informed decisions. Our contribution integrates bipolarity into Pythagorean fuzzy matrices, a framework that broadens the utility of bipolar fuzzy theory. Through computational experimentation, we identify the most effective strategy for digital marketing platforms. When compared to existing techniques, our approach shows advantages, underlining its potential to improve decision-making in uncertain scenarios and offering insights for businesses that refine their digital marketing strategies.

A consumer-oriented kansei evaluation model through online product reviews

Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers’ perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers’ kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online reviews. Subsequently, the kansei data is adjusted based on consumers’ kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers’ kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method.

Impact of the COVID-19 pandemic on delays in diagnosis and treatment of tick-borne diseases endemic to southeastern USA

BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic was marked by an increase in diagnosis and treatment delays for a range of medical conditions. Yet the impact of the pandemic on the management of tick-borne diseases, which frequently manifest as an acute febrile illness similar to COVID-19, has not been well described.MethodsIn this retrospective cohort study of patients with suspected tick-borne disease attending the University of North Carolina Health facilities, we compared the timeliness of diagnosis and treatment in a “pre-COVID” period (March 2019 to February 2020) and a “post-COVID” period (March 2020 to February 2021). Participants included patients with an ICD-10 diagnosis code of spotted fever group rickettsiosis or ehrlichiosis and a positive Rickettsiarickettsii or Ehrlichia indirect immunofluorescence assay immunoglobulin G antibody test result. Of the 897 patients who had an eligible diagnosis, 240 (26.8%) met the inclusion criteria. The main outcome was time from initial presentation to definitive diagnosis and treatment.ResultsDuring the 2-year study period, 126 (52.5%) patients were grouped in the pre-COVID period and 114 (47.5%) were grouped in the post-COVID period; 120 (50.0%) were female; and 139 (57.9%) were aged > 50 years. Comparing the post-COVID to the pre-COVID period, the adjusted odds ratio (aOR) for delay in treatment > 0 days was 1.81 (95% confidence interval [CI] 1.07–3.07, P = 0.03), and for a treatment delay > 7 days, 1.65 (95% CI 0.94–2.90, P = 0.08). The odds of a delay in diagnosis were similar for patients in the post- and pre-COVID periods, with an aOR of 1.61 (95% CI 0.96–2.72, P = 0.07) for delays > 0 days, and aOR of 1.72 (95% CI 0.99–3.00, P = 0.05) for delays > 7 days.ConclusionsThe odds of a delay in treatment > 0 days were significantly higher in the post-COVID period than in the pre-COVID period. However, the odds of a delay in treatment > 7 days, or a delay in diagnosis, were similar between these two periods. Shifts in care-seeking, alternative care delivery models and prioritization of COVID-19 may contribute to diminished timeliness of treatment for patients with tick-borne diseases.Graphical

Evaluation of rural communities for the planning of renewable energy projects: a SWOT-AHP methodological framework

Historically, we know that renewable energies have been a key element for the development of rural communities and have addressed social problems. If we talk about the implementation of renewable energies with the objective of solving social problems, these technologies will be understood as the means of intervention to improve a social, economic or environmental good of a region. The use of methodologies in project management has the benefit of organizing and providing tools to reduce the risks of projects to be implemented in a region. The purpose of this article is to test the suitability of an innovative methodological framework that integrates multicriteria for decision-making and analysis tools (SWOT-AHP). SWOT allows planners to gather relevant knowledge from local stakeholders (Uhunamure & Shale, 2021), while the AHP-Hierarchical Analytical Process allows the prioritization of alternatives. This in order to prioritize the needs to be met in the community, promotes social acceptance of the technology and contributes to the development of rural areas.

A Hybridization of Modified Rough Bipolar Soft Sets and TOPSIS for MCGDM

Uncertain data is a challenge to decision-making (DM) problems. Multi-criteria group decision-making (MCGDM) problems are among these problems that have received much attention. MCGDM is difficult because the existing alternatives frequently conflict with each other. In this article, we suggest a novel hybrid model for an MCGDM approach based on modified rough bipolar soft sets (MRBSs) using a well-known method of technique for order of preference by similarity to ideal solution (TOPSIS), which combines MRBSs theory and TOPSIS for the prioritization of alternatives in an uncertain environment. In this technique, we first introduce an aggregated parameter matrix with the help of modified bipolar soft lower and upper matrices to identify the positive and negative ideal solutions. After that, we define the separation measurements of these two solutions and compute relative closeness to choose the best alternative. Next, an application of the proposed technique in the MCGDM problem is introduced. Afterward, an algorithm for this application is developed, which is illustrated by a case study. The application demonstrates the usefulness and efficiency of the proposal. Compared to some existing studies, we additionally present several merits of our proposed technique. Eventually, the paper handles whether additional studies on these topics are needed.

Alternative prioritization of freeway incident management using autonomous vehicles in mixed traffic using a type-2 neutrosophic number based decision support system

Traffic incident management is combining the assets of authorities to identify, deal with, and manage traffic problems as rapidly as possible while providing the safety of on-scene responders and the traveling public. The advancement of autonomous vehicles is an opportunity for enhancing incident management implementations. This study aims to provide policymakers with four main alternatives to control freeway incidents using autonomous vehicles in mixed traffic. The presented alternatives are namely autonomous vehicles behaving as human-driven vehicles, ones connected, ones using an algorithm for incident management, and ones used in the traditional incident management methodology. The study also aims to introduce an integrated decision-making tool that is comprehendible for policymakers and mobility experts. It is based on the integration of an Entropy-based approach and the complex proportional assessment (COPRAS) method under the type-2 neutrosophic number (T2NN) environment. T2NN can represent uncertainties such as uncertainty, inconsistency, and inconsistency in real-world problems. T2NN-Entropy is presented to reveal the objective importance of evaluation criteria for freeway incident management. T2NN-COPRAS is proposed to order alternatives when deciding on the behavior of autonomous vehicles. The comparative investigation shows the superiority of the T2NN-Entropy-COPRAS model. Its major advantages are high robustness in making real-world multi-criteria decisions due to the triple-normalization backbone, and high flexibility in solving complex decision-making problems. The research findings show that using an algorithm for incident management is the best alternative to solve problems during and after an incident in mixed traffic, while autonomous vehicles that act like human-driven vehicles are the least advantageous.

Assisted site-dependent selection of the most suitable Airborne Wind Energy system via Fuzzy Analytic Network Process

Over the last decade, the Airborne Wind Energy (AWE) innovation has been evolving with the development of several prototypes by various companies/research institutions. AWE crosswind systems may differ in the electricity generation mode, the wing type, the take-off/landing approaches and the control mechanisms, to name a few. Each system has characteristics which enhance their performance regarding key factors of AWE exploration at the expense of penalizing others. Hence, deciding for the most appropriate system in a possible AWE implementation, targeting a certain location, is a difficult and uncertain scenario. This paper presents a Multi-Criteria Decision Analysis (MCDA) approach, using the Fuzzy Analytic Network Process (FANP), aiming to determine, from nine alternatives, the most suitable AWE crosswind system for two distinct exploration sites: on-shore rural and off-shore. The importance of key selection criteria for AWE is presented and discussed, alternatives in each criterion are evaluated and compared and, finally, a prioritization of both alternatives and criteria is obtained. The FANP methodology indicates rigid wing pumping-cycle systems with horizontal take-off as the most suitable solution for both investigated sites, revealing Aerodynamic Performance, Mass-to-Area Ratio and Controlability as the most relevant factors in the decision.

Unleashing the Potential: Exploring Attitudes and Overcoming Challenges in Implementing Differentiated Instruction in the Philippines’ English Language Classrooms

This study investigates Filipino English language teachers’ views and issues with diversified education. Differentiated instruction, a student-centered approach, fosters inclusive education by giving students alternative ways to engage with the subject, process knowledge, and demonstrate mastery. However, there is little data on its use in Filipino English language classes. This study examines English language teachers’ views on tailored education and identifies the challenges they encounter when adopting differentiated education. Mixed-methods data collection was used to collect data on teachers’ varied instructional attitudes and obstacles. Most poll respondents were positive about differentiated instruction and confident in constructing effective differentiated units. However, inadequate resources, time, various student needs, reluctance to change, assessment and evaluation, and classroom management were highlighted. Alternative resources, collaboration, prioritization, and time-saving measures helped overcome these challenges. This study found that flexible scheduling, differentiating content, process, and product, cooperative learning, digital technologies, and stakeholder involvement can help teachers meet students’ diverse needs and promote equal learning outcomes.

A comprehensive performance assessment system for diagnosis and decision-support to improve water and energy efficiency and its demonstration in Portuguese collective irrigation systems

In regions with a strong seasonal or interannual asymmetry in the distribution of precipitation and higher frequency of droughts, collective irrigation systems are vital infrastructures for agricultural activity. Although operating for decades, several systems have ageing infrastructures, with relevant water losses and pumping energy inefficiencies. Consequently, the systems are not adequately designed or operated to meet current and future water demand. Therefore, rehabilitation to improve water and energy efficiency while ensuring infrastructure, economic sustainability and service quality is crucial. In this sense, comprehensive approaches using performance assessment to support the planning process or benchmarking between water users associations play an essential role in improving efficiency in collective irrigation systems. However, few methodologies assess interdependencies between water losses, energy efficiency, infrastructure condition, service quality, economic and operational dimensions. Additionally, these approaches rarely were applied to the different stages of the planning process (diagnosis, planning, monitoring, and reviewing the impact of measures) and to different types of collective irrigation systems (gravity, pressurised, combined) for comparative analysis. This paper presents a comprehensive performance assessment system (PAS) for diagnosis and decision support about measures to improve water and energy efficiency in collective irrigation systems. Afterwards, the PAS is applied for diagnosis and prioritisation of alternatives to enhance the efficiency of a gravity system. The results indicate significant water losses due to the canal and intermediate reservoirs discharges and leakage in canals and low-pressure pipes, related to network ageing and insufficient flow monitoring and control for the gravity system. Ranking allowed identifying the gravity network area with high priority of intervention due to poor performance in non-revenue water, water losses due to discharges, energy efficiency of pumping stations and system energy in excess. Several alternatives were studied for this network area, and infrastructural solutions involving canal rehabilitation and water discharge control significantly impact global performance improvement besides the substantial investment associated. Subsequently, the PAS is used for comparing gravity and pressurised systems. In opposition to gravity, the pressurised system, with efficient use of water resources, presents a poor performance in pumping energy efficiency. Furthermore, the significant energy costs indicate the importance of energy improvement measures for the pressurised system. Besides assessing water and energy efficiency, the novel PAS may help managers and policymakers identify every system's best practices and weak points.

Assessment of Battery Energy Storage Systems Using the Intuitionistic Fuzzy Removal Effects of Criteria and the Measurement of Alternatives and Ranking Based on Compromise Solution Method

The energy storage is an important character for sustainable energy structures and the prospective future economy. This paper aims to propose a multi-attribute decision analysis (MADA) approach to prioritize and choose the energy storage system (ESS) alternatives in terms of the different technical, economic, environmental and social aspects of them. In this line, an integrated approach is developed with the combination of intuitionistic fuzzy sets (IFSs), a method using the removal effects of criteria (MEREC), rank sum (RS) and the measurement of the alternatives and ranking based on compromise solution (MARCOS) methods for prioritizing the ESSs. The IF-MEREC-RS was used to find the integrated weight by combining the objective and subjective weights of the different indicators for prioritizing the ESSs. The MARCOS method was implemented to rank the various ESSs over several crucial indicators of sustainability. The practical outcome illustrates that the Li-ion battery (LIB) is the best ESS among all of the options, and this is followed by NaSB and NiMHB. A sensitivity investigation with the diverse weights of the indicators shows the impact of the risk preferences on an alternative prioritization. A comparison is discussed with the outcomes of the different presented, extant approaches to certify the superiority of the presented approach.

An application of dimensional reduction approach for evaluating energy generation alternatives for energy sustainability

The success of sustainability is mainly linked to the energy sector, which is governed by the mix of different fuel resources. This work aims to calculate the sustainability performance scores (SPSs) of the energy alternatives that constitute Pakistan’s current electric energy mix and future energy plans. For this purpose, a decision framework consisting of eighteen indicators is considered for evaluating the performance of energy alternatives. The SPSs of energy alternatives are calculated using the dimensional reduction feature of the principal component analysis technique. The resultant prioritization of energy alternatives favors solar resource the most for planning a roadmap for sustainable net-zero energy infrastructure development. Based on the outcomes, recommendations are put forward to improve the current energy situation so that the country could catch the pathway towards achieving energy sustainability.

A Hybrid Fuzzy AHP/VIKOR Approach to Funding Strategy Selection for Advanced Prosthetic and Orthotic Medical Devices in Low-Income Countries: A Case of Pakistan

In the context of advanced prosthetic and orthotic medical devices, funding is a major challenge, especially in low-income countries. This research aims to investigate the best funding solution for such medical devices to offer disabled people a better quality of life and improve their health and economic situation. This is the first time such methods are being used in a study based on related research involving a fuzzy analytic hierarchy process (FAHP)/VIekriterijumsko KOmpromisno Rangiranje (VIKOR) model to find the best funding source. A survey questionnaire created by the author and completed by Pakistan subject experts was used to analyze data via a weightage value ranking system as an assessment tool. Prioritization of funding alternatives was based on main criteria and subcriteria. Limitations include sample number of study participants, amount of data collected, number of models used, and time constraints. Potential funding sources were ranked based on a VIKOR weightage value model using a hybrid decision-making process that showed results as “private insurance” having a weightage value of (0.1956) as being the best funding source since they are having the minimum weightage and then next from best to least they were “need-based” with a weightage value of (0.2510), “self-pay” with a weightage value of (0.7970), and finally “government-based” having a weightage value of (0.9636). Implications of this study conclude that the proposed model efficiently finds the best funding sources for advanced prosthetic and orthotic (P&O) medical devices in low-income countries. Executing this model in low-income countries such as Pakistan can help solve P&O medical device coverage challenges and offer disabled people better opportunities to work and contribute to their quality of life and economy.

Systematic Down-Selection of Repurposed Drug Candidates for COVID-19.

SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.

An alternative vaccine prioritization approach in response to COVID-19 pandemic

PurposeThe purpose of this study is to evaluate the impact of food access and other vulnerability measures on the COVID-19 progression to inform the public health decision-makers while setting priority rules for vaccine schedules.Design/methodology/approachIn this paper, the authors used the Supplemental Nutrition Assistance Program (SNAP) data combined with the Centers for Disease Control and Prevention (CDC)’s social vulnerability score variables and diabetes and obesity prevalence in a set of models to assess the associations with the COVID-19 prevalence and case-fatality rates in the United States (US) counties. Using the case prevalence estimates provided by these models, the authors developed a COVID-19 vulnerability score. The COVID-19 vulnerability score prioritization is then compared with the pro-rata approach commonly used for vaccine distribution.FindingsThe study found that the population proportion residing in a food desert is positively correlated with the COVID-19 prevalence. Similarly, the population proportion registered to SNAP is positively correlated with the COVID-19 prevalence. The findings demonstrate that commonly used pro-rata vaccine allocation can overlook vulnerable communities, which can eventually create disease hot-spots.Practical implicationsThe proposed methodology provides a rapid and effective vaccine prioritization scoring. However, this scoring can also be considered for other humanitarian programs such as food aid and rapid test distribution in response to the current and future pandemics.Originality/valueHumanitarian logistics domain predominantly relies on equity measures, where each jurisdiction receives resources proportional to their population. This study provides a tool to rapidly identify and prioritize vulnerable communities while determining vaccination schedules.

A Decision-making Framework to Select a Maintenance Technology: A Case of Transmission Line

The purpose of our study is to propose a decision-making framework to select a maintenance technology. The combination of interpretive structural modeling and analytic network processes with the benefits, opportunities, costs, and risk model was applied to the prioritization of alternatives. The priorities were then calculated by zero-one goal programming to suggest the most appropriate maintenance technology. The proposed framework was implemented in the case of transmission line maintenance to test its usability and receive expert feedback.

An analytics approach to decision alternative prioritization for zero-emission zone logistics

Urban freight transportation requires wise management considerations since it is one of the most challenging issues cities face to attain sustainability. To help with the challenging decision process, an integrated two-stage decision analysis approach is proposed. In the first stage, the Defining Interrelationships Between Ranked criteria (DIBR) method is used to consolidate the experts’ opinions to compute the weights of the predetermined decision criteria. In the second stage, a novel approach that integrates Combined Compromise Solution (CoCoSo) with the context of type-2 neutrosophic numbers is used to identify the most optimal management decision alternative. A case study is developed to show the viability and practicability of the proposed methodology. The results indicated that “building a logistics center (for fast and cheap delivery)” is the highest-ranked decision alternative, followed by “optimized and integrated operation of urban logistics,” and “zero-emission zone implementation,” respectively. The proposed methodology can be used as a decision analysis framework for urban city authorities while selecting the most optimal policies and related solution alternatives towards achieving and sustaining low-emission urban freight transportation.

Safe E-scooter operation alternative prioritization using a q-rung orthopair Fuzzy Einstein based WASPAS approach

E-scooters globally have proven an increasingly popular form of dockless micro-mobility, while also contributing to sustainable urban transportation forms. However, some safety issues arise with e-scooter use in the cities. This study aims to propose a decision-making model based on q-rung orthopair fuzzy sets for prioritizing the safe e-scooter operation alternative. The proposed model consists of two stages: weighting the criteria and ranking the alternatives. First, a fuzzy logarithmic additive assessment of the weight coefficients methodology and fuzzy Einstein weighted averaging operator were applied to define the reference relationships between the criteria and determine their weights. Second, a q-rung orthopair fuzzy sets based decision-making model integrating q-rung orthopair fuzzy Einstein average and q-rung orthopair fuzzy Hamacher geometric mean operator was used to rank the alternatives. A fictional case study is presented to show the practicality of the proposed model. The contribution of the work is as a decision-support system for evaluating safe e-scooter strategies, including infrastructure placement, user behavior and how e-scooters interact with other transportation means, showing that applicability of the proposed model to real-world problems.

Ontologies representing multidisciplinary decision-making rationales for sustainable infrastructure developments

• Two ontologies are developed for multidisciplinary decision-making. • A tuple of 〈DMs〉, 〈DC〉, 〈DAs〉, and 〈SPIs〉 is proposed to represent MDM rationales. • They specify heterogeneous decision preferences in evaluating sustainability. • They are validated using case studies as formal, comprehensive, and reusable. • They serve as a decision support system for infrastructure professionals. A multidisciplinary decision-making (MDM) process is required for sustainable infrastructure developments in early design stage of integrated project delivery. Decision makers from multiple disciplines employ heterogeneous decision criteria when evaluating decision alternatives for the project developments. This results in uncertainty in alternative prioritization as the scoring ranges of sustainable performance indexes for those alternatives are indistinguishable. This study has formalized the rationales of decision coordinators for converting scores of sustainability performance indexes assessed by decision makers to the normalized ones based on their preferred decision criteria in a consistent and collective manner. Two ontologies have been formalized, which include 45 classes, 119 properties, 50 sub-properties and their facets. The ontologies are validated using three cases of sustainable infrastructure developments. Validation results indicate that they are formal, comprehensive, and reusable in representing MDM rationales, i.e., the interrelationships among decision makers, decision criteria, decision alternatives, and sustainability performance index. Theoretically, these two ontologies contribute to the Triple Bottom Line theory and social choice theory by specifying heterogeneous decision preferences in evaluating sustainable infrastructure developments. For practical implications, the developed ontologies serve as a computer-readable decision support tool to systematically store and communicate MDM information between multidisciplinary professionals in infrastructure project management.