Make A Decision Research Articles

Machine learning based prediction of biogas generation from municipal solid waste: A data-driven approach

This study aims to imply different machine learning (ML) (artificial neural network (ANN), linear regression, XGBoost, random forest (RF), support vector machine (SVM)) models to predict and optimize biogas production yield from organic fractions of municipal solid waste (MSW). The data set for six key input variables, including moisture content, C/N ratio, lignocellulose content and MSW age was analyzed and processed using ML models. Further, data exploratory analysis (EDA) analysis was performed using various steps such as data preprocessing, feature selection, train-test-validation splitting, model testing and evaluation. The results revealed that XGBoost and RF regression model exhibited superior performance efficacy. Both the models achieved a higher R2 values of 0.88 and 0.68, and low root mean square error (RMSE) values of 305 and 496, respectively. Furthermore, feature importance analysis was performed to identify the relative significance of input variables in biogas prediction. SVM model revealed significant contributions of moisture content (20.86 %), cellulose (60.21 %), hemicellulose (34.91 %), lignin content (62.84 %), and MSW age (17.23 %) in predicting biogas production. The findings of the study can be a resource for techno-crates/researchers to develop an efficient ML based decision-making tools for accurate predictions and process optimization.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use.

Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits

Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R–CR–R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:q$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:r$$\\end{document}. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Artificial intelligence tools and concepts give access to authenticity and quality information in Brazilian olive oil volatilome

Brazil is the world's second largest importer of olive oil and stared to produce its own oil quite recently in two distinct geographic areas. The production is still very small, and studies regarding oil composition are scarce. Determination of discriminant differences are useful to support appeals regarding origin indication and to detect fraud and adulteration with oils from other countries. In this work the Brazilian extra virgin olive oils (EVOOs) volatilome is explored by Artificial Intelligence (AI) tools developed on multiple headspace solid-phase microextraction (MHS-SPME) combined with comprehensive 2D gas chromatography-mass spectrometry and flame ionization detection (GC×GC-MS/FID) data. Using MHS-SPME, external standard calibration, and FID predicted relative response factors (RRF), the accurate quantification of 51 informative volatile compounds was carried out and the odorants responsible for key-positive sensory attributes were used to generate distinctive aroma blueprints aligned with the AI smelling based on sensomics. As authenticity and origin assessment decision-making tool, augmented visualization by computer vision was applied on volatilome 2D fingerprints. By this approach, extra-virgin olive oil samples (n=35), from two olive cultivars (Arbequina and Koroneiki) harvested in the main producing regions in Brazil (Rio Grande do Sul and Serra da Mantiqueira) in 2021 and 2022, were effectively discriminated and mislabeled products regarding their geographical origin were, for the first time, promptly identified.

Ontology-Based Landslide Knowledge Representation and Hazard Assessment Research

The occurrence of landslides is characterized by suddenness and unpredictability, with many complex relationships among various influencing factors. This study, by introducing the ontology of knowledge engineering, summarizes the characteristics of landslide knowledge and constructs a multidisciplinary, multi-level, and multi-perspective ontology of the landslide domain. Through in-depth analysis of existing landslide research systems and methods, the study defines the basic concepts, attributes, and their complex relationships and rules related to landslides. This research expresses landslide knowledge using the Web Ontology Language (OWL), enabling effective storage and retrieval of landslide knowledge, thus promoting the systematization and standardization of knowledge in the landslide domain. Furthermore, leveraging the high knowledge density feature of the constructed landslide domain ontology, this study extends traditional landslide evaluation and prediction methods by introducing more evaluation indicators based on the constructed landslide ontology. The comprehensive evaluation of landslide risk is conducted using the contribution-weighting method, providing new insights and tools for landslide risk management and decision-making.

Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty.

Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures. Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases. The exact accuracies of the surgeon's template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size. The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.

A sustainable network design for municipal solid waste management considering waste-to-energy conversion under uncertainty

This research proposes a sustainable municipal solid waste management (MSWM) system to address population growth, consumerism, and resource scarcity. It introduces a two-phase decision structure combining multi-attribute decision-making (MADM) tools. Phase one develops a hybrid risk-oriented fuzzy MADM tool for selecting optimal waste-to-energy technologies, considering environmental and technological factors. Phase two uses a fuzzy bi-objective multi-period mixed-integer linear programming model to design the MSWM supply chain efficiently under uncertainty. A case study in North Tehran demonstrates the framework’s practical applicability and effectiveness in real-world scenarios, highlighting that effective source separation boosts recycling, reduces costs, and provides social benefits. Sensitivity analyses offer insights to enhance waste segregation practices. The study emphasizes integrating economic, environmental, and social sustainability in waste management decision-making. By offering a novel, holistic approach, this research addresses existing gaps in systematic decision-making processes and provides a robust tool for municipalities to optimize strategies under uncertainty.

RESIDE: REPRESENTATIVE DATABASES FOR MULTI-CRITERIA DECISION TOOL

RESIDE is a software that allows designers and decision-makers to simultaneously consider issues related to energy consumption for environmental conditioning and water heating, construction costs and public policies from the initial planning stages of residential developments. However, its use is conditional on the existence of a database for the context analyzed. Understanding the growing importance of addressing the innate complexity in sustainability-oriented design activities, this article presents a procedure to identify representative databases for the state of Minas Gerais, in order to enable the use of the RESIDE multi-criteria decision tool.

ISETECH-Statistics and Machine Learning Based Decision-Making for Diet Beverage Choice and Recommendation through Software Application

Positioned within the realm of ubiquitous beverage chains acclaimed for their diverse coffee and tea offerings, this investigation deeply explores the intricate nutritional dynamics and caloric compositions of these widely consumed beverages. At its core, the research aims to streamline decision-making for individuals actively seeking health-conscious beverage alternatives. This comprehensive endeavour unfolds through an integrated methodology that combines hypothesis testing and other statistical techniques with the establishment of a robust decision support system, complemented by powerful machine learning techniques. Distinctively, the paper integrates an intuitive React application (ReactApp) into the robust Fast API framework. Users engage seamlessly with the decision support system, as Fast API efficiently manages data processing, interfaces with machine learning algorithms, and delivers personalized recommendations to the ReactApp front. In essence, this interdisciplinary initiative epitomizes the fusion of nutritional science, statistical analysis, machine learning, and modern web technologies, providing a holistic and pioneering solution for selecting health-conscious beverage alternatives. One of the most noteworthy outcomes of our research lies in the compelling results we've achieved. Our predictive model has demonstrated exceptional performance: We achieved an impressive Accuracy Rate of 83.67%, signifying the high precision of our recommendations in identifying health-conscious beverage alternatives. Simultaneously, the F1-Score, which harmonizes precision and recall, stands at a commendable 82.21%, indicating a well-balanced and effective decision support system. Our model's ability to recall health-conscious choices is also strong, with a Recall rate of 83.67%. Furthermore, we assessed the model's agreement with observed data using Cohen's Kappa, which yielded a substantial score of 78.01%. This indicates not only the model's predictive power but also its ability to capture agreement beyond what might occur by chance alone. These results, seamlessly integrated into our research narrative, underscore the effectiveness and reliability of our approach. They constitute a rare and valuable contribution to the field, promising a novel pathway for informed and health-driven consumer choices, firmly supported by our rigorous methodology and advanced machine learning outcomes.

Smart Agriculturing Based on KSK Approach: A Novel AI-Driven-IoT(AIIoT) Based Decision-Making Approach

An enormous change is taking place in the agriculture industry as a result of the introduction of the Internet of Things (AIIoT), which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The purpose of this research is to present a comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making. The concept of smart agricultural decision-making refers to a breakthrough method that enables farmers to optimise their farm operations while also making decisions that are well-informed. Farmers are able to increase crop yields while simultaneously reducing costs and hazards when they make use of advanced analytics, real-time data, and decision-making tools. The development of smart agriculture will result in farmers having increased decision-making authority, which will ultimately lead to an agricultural sector that is more sustainable and productive. The Internet of Things (IoT) and artificial intelligence (AI) in smart agriculture is a cutting-edge technology that has the potential to be a game-changer in terms of how we produce and consume food. Farmers may be able to improve agricultural yields and quality, streamline operations, and contribute to a food supply chain that is more efficient and sustainable if they use artificial intelligence and the internet of things. In spite of the fact that there are still certain problems that need to be cleared up, the Internet of Things (IoT) offers numerous advantages for smart agriculture, and its implementation is anticipated to increase in the years to come. The KSK technique, also known as the Knowledge-Sensors-Knowledge approach, is a suggestion made by Dr. Kutubuddin S. Kazi, who is also using his name. The output of the KSK technique results in an accuracy of 99.9% and a recall of 97.9%, respectively.

EXPLORING THE RELATIONSHIP BETWEEN RAINFALL AND CROP YIELD AND BEST PRACTICES ADOPTION USING PARTICIPATORY APPROACH.

Crop yield is a standard measurement for the amount of agricultural production. Sustainable agriculture demands an increase in crop yield. This study deals with rainfed agriculture; hence, precipitation becomes the driving factor for crop yield. Heat maps are used to examine the rainfall and crop yield correlations. ML is an essential tool in decision-making, and many ML algorithms are available for prediction. This study uses the ML algorithms to predict whether the crop yield will increase with increased rainfall. Logistic regression, Decision tree classifier, Random Forest classifier, and XGBoost classifier are the algorithms chosen for analysis. Altogether this region consists of forty crops but focuses on five predominant annual crops. Implementation-based results are the universal goal of every research which society needs. The chances of implementation are associated with two major components: the reliability of the results and society's willingness. Analysis of these components needs ground truthing and Participatory Rural Appraisal, respectively. Farmers and villagers filled out a questionnaire about the details required for this study. The survey was an active approach to collecting necessary information from the participants. The survey showed positive results among one hundred and fifty samples from six blocks. Finally, cashew nut, sugarcane, and turmeric showed good dependency on the precipitation, and around 88% of villagers are ready to implement the results derived from ML algorithms.

Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.

Development of the World Federation of Hemophilia Shared Decision-Making Tool.

The use of shared decision-making (SDM) in clinical settings is becoming more prevalent. The evolving and increasingly complex treatment landscape of haemophilia management has augmented the need and desire for SDM between patients and their healthcare team. SDM tools have been used in other chronic conditions and can be an effective form of education for patients and clinicians. The World Federation of Hemophilia (WFH) partnered with people with haemophilia (PWH), patient advocacy groups, and healthcare practitioners to form an expert working group to develop an educational tool for PWH and their caregivers. The primary objectives included educating PWH on the available prophylactic treatments and facilitating discussion between PWH and their healthcare team. The tool was proposed and developed by the expert working group, workshopped at conference round tables, and evaluated in two focus groups. The interactive WFH SDM Tool guides users through the SDM treatment journey and provides an opportunity for reflection on current disease impact and treatment preferences, educational fact sheets and videos, and a comparison between treatment classes. Two forms of the SDM Tool are available: an online platform with a summary page that may be printed and shared and a printable workbook. All evidence in the tool is based on the prescribing information or phase III clinical trial publications. The Tool will be updated twice each year. The WFH SDM Tool is the first available resource that translates published guidance on SDM in haemophilia into a practical, user-friendly tool aimed at facilitating patient-centred treatment decisions.

Ecohydrological risk assessment model for the Pra River Basin using GIS and multi-criteria decision making

ABSTRACT The Pra River Basin in Ghana is a major source of ecological, cultural, and economic benefits. However, it has been experiencing degradation, and logistical constraints have limited the implementation of Integrated Water Resources Management. This study presents a GIS-based ecohydrological risk model that identifies ecohydrological units with the highest degradation risk. First, the PRB was divided into 25 eco-hydrological units. Second, seven drivers of degradation were identified and mapped. The drivers were reclassified into five categories with risk scores of 1–5, with 5 indicating higher risk scores. The variables were ranked using the Analytical Hierarchy Process, the degradation risk model was calculated using the weighted sum method, and the multi-criteria decision tool was used to identify the ecohydrological units with the highest degradation risk. Of the 25 ecohydrological units delineated, the Buama, Lower Offin, Ayensu, and Oda ecohydrological units were found to be at the highest risk of degradation due to high artisanal small-scale gold mining, high population density, low forest/vegetation cover, and high encroachment in waterways. The results can be used as a guide to prioritize management intervention, starting with the areas classified as high risk and moving down to those classified as moderate risk.

Development of a Formula for Calculating the Probability of the Spread of African Swine Fever

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives forecasting epidemics. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. A formula has been developed for calculating the probability of spreading african swine fever from infected animals of a threatened pig farm to other farms. The formula allows you to calculate the probability of an outbreak of infection for pigs of each of the pig farms located in the analyzed area. This takes into account the possible ways of spreading the pathogen in the presence of mechanisms and factors of transmission of african swine fever in real time.

Systematic Review of Consumer Price Index Methods and Software: Evidence from ASEAN Countries and Scholarly Research

This comprehensive systematic review provides an in-depth analysis of consumer price index (CPI) methods and software use in Asian countries. The study looks at the practises used by countries to calculate CPI, with an overwhelming preference for monthly calculations, reflecting the dynamic economic growth of the region. The use of household expenditure surveys and weighting sources ensures that CPI accurately captures changes in consumer spending patterns and the relative importance of different goods and services. Applications of CPI in Asian countries cover a wide range, including indexation of wages, pensions and social security contributions, as well as its central role as a main indicator for monetary policy decisions, macroeconomic modelling and various analytical purposes. The systematic review highlights various calculation methods for CPI, including the Carli index, the Laspeyres index, the chained Jevons index and the Dutot index, each of which has different strengths and weaknesses that affect the accuracy and reliability of CPI's measurements. In terms of software, the study encounters a range of applications, including Microsoft Excel, SAS, STATA, R, Python, EViews and SPSS, each offering versatile capacities for statistical analysis and econometric modelling. In addition, the study explores potential software alternatives such as Gretl, Julia, EpiData, GNU Octave, Tableau, Matplotlib, Seaborn and QGIS, whose suitability for CPI calculations should be considered. The research findings highlight the importance of transparent and accurate CPI calculations in shaping effective policy decisions. Policy makers can use these findings to refine CPI measurements to enable well-informed strategies to steer the economy. The study suggests avenues for future research that include assessing specific impacts of CPI calculations, further developing software tools, and incorporating consumer perceptions into CPI development.

Promised hassle time management in online retailing with fraudulent and regretful customers

Fraudulent customer return behavior, called “wardrobing,” incurs significant product return costs for online retailers. However, customers can also behave regretfully in online retailing, causing high product return costs. Consequently, we explore the combined effect of fraudulent and regretful customers in product returns. Research suggests implementing restrictive return policies to curb huge return costs due to fraudulent and regretful customer behaviors. One of the ways to implement a restrictive policy is by introducing hassle time. Consequently, retailers commit promised hassle times to customers, during which the retailers can approve the product returns and issue refunds. As a result, we determine how an online retailer can respond to regretful and fraudulent customers through promised hassle time while considering the stochastic nature of hassle time. In some situations, an online retailer might know that hassle time follows a specific probability distribution; in others, a retailer might not know the distribution. Therefore, we determine the optimal promised hassle time an online retailer must promise to regretful and fraudulent customers in each situation. For the first situation, we determine that a specific ratio, which we call the customer ratio, determines how the optimal promised hassle time changes with customers' fraud and regret levels. If the customer ratio is one, the optimal promised hassle time does not change; if it is greater than one, the optimal promised hassle time increases; and if it is less than one, the optimal promised hassle time decreases, with an increase in fraud and a decrease in regret levels. For the second situation, we propose a novel decision-making tool for online retailers, enabling them to optimize the promised hassle time. Finally, we generalize the results of our study to order cancellations with regretful customers, and shared mobility services.