Decision Analytics Journal最新文献

{"title":"A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm","authors":"Sushovan Khatua ,&nbsp;Debashis De ,&nbsp;Somnath Maji ,&nbsp;Samir Maity ,&nbsp;Izabela Ewa Nielsen","doi":"10.1016/j.dajour.2024.100486","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100486","url":null,"abstract":"<div><p>A distributed machine learning technique called federated learning allows numerous Internet of Things (IoT) edge devices to work together to train a model without sharing their raw data. Internet of Vehicular Things (IoVT) are an important tool in smart cities for moving objects, such as knowing the traffic patterns, road conditions, vehicle behavior, etc. To enhance traffic management and optimize routes, federated learning, and IoT must work jointly, which may achieve sustainable development goals (SDG) in many ways. This research outlines a system for federated learning in vehicular networks in smart cities. The suggested architecture considers the difficulties presented by such situations’ restricted network connectivity, privacy issues, and security concerns. The framework employs a hybrid methodology integrating federated learning on a centralized server with local training on individual cars. The proposed framework is assessed based on a real-world dataset from a smart city through IoT devices. The findings demonstrate that the suggested method successfully increases model accuracy while preserving the confidentiality and security of the data. In this investigation, we incorporated the Federated Learning model, which can fetch all the information between arbitrary nodes and derive the Traffic, Fuel Cost, Safety, Parking Cost, and Transportation cost for a better routing approach. The suggested framework can be utilized to increase the effectiveness of the transportation system, decrease congestion in smart cities, and improve traffic management. We employ an improved genetic algorithm (iGA) with generation-dependent even mutation to tackle the emission in the smart environment.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100486"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000900/pdfft?md5=eeb6ddcfcdd52908ced09f3dcc2c8155&pid=1-s2.0-S2772662224000900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive analytics approach for forecasting bike rental demand","authors":"Meerah Karunanithi,&nbsp;Parin Chatasawapreeda,&nbsp;Talha Ali Khan","doi":"10.1016/j.dajour.2024.100482","DOIUrl":"10.1016/j.dajour.2024.100482","url":null,"abstract":"<div><p>The demand for rental bikes in urban areas fluctuates, leading to localized surpluses and shortages. To address this challenge, effective bike relocation strategies are essential for ensuring equitable distribution and maximizing customer satisfaction. This study aims to employ advanced machine learning techniques to forecast bike rental demand in urban areas, thereby enhancing the efficiency and accessibility of bike rental services and contributing to sustainable urban mobility. The study comprehensively analyzes various influencing factors using machine learning models, including Ordinary Least Squares regression, MLP Regression, Gradient Boosting Regression, Random Forest Regression, Polynomial Regression, and Decision Tree Regression. The primary objective is to identify the most accurate predictor by comparing key metrics such as <span><math><mi>R</mi></math></span>-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient. Insights gained from this analysis aid in identifying influential variables and ensure the development of resource-efficient and adaptable models, leading to more informed decision-making for rental bike businesses. Additionally, future research directions involve the implementation of artificial intelligence technology to predict overall bike demand based on urban cities’ criteria, including the number of national and international tourists. By addressing these objectives, this study seeks to provide valuable insights and tools for rental bike businesses to optimize operations, make strategic decisions, and enhance customer experience in competitive urban markets.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100482"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000869/pdfft?md5=9f633b17c816d861bc20e729e9695586&pid=1-s2.0-S2772662224000869-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141144749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection","authors":"Sahar Varchandi ,&nbsp;Ashkan Memari ,&nbsp;Mohammad Reza Akbari Jokar","doi":"10.1016/j.dajour.2024.100488","DOIUrl":"10.1016/j.dajour.2024.100488","url":null,"abstract":"<div><p>Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100488"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000924/pdfft?md5=eebd793dba470c62dffccfd7c312e60d&pid=1-s2.0-S2772662224000924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems","authors":"Saman Nessari ,&nbsp;Reza Tavakkoli-Moghaddam ,&nbsp;Hessam Bakhshi-Khaniki ,&nbsp;Ali Bozorgi-Amiri","doi":"10.1016/j.dajour.2024.100485","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100485","url":null,"abstract":"<div><p>The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm’s effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm’s consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm’s faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm’s potential in providing flexible, robust solutions. The proposed algorithm’s unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100485"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000894/pdfft?md5=7212b786690a27bfbd5ce4f691eeda54&pid=1-s2.0-S2772662224000894-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production","authors":"Kanisha Pujaru ,&nbsp;Sayani Adak ,&nbsp;T.K. Kar ,&nbsp;Sova Patra ,&nbsp;Soovoojeet Jana","doi":"10.1016/j.dajour.2024.100481","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100481","url":null,"abstract":"<div><p>Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100481"},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000857/pdfft?md5=f6fc9505e0ef1af7e8473e41b98f7d0a&pid=1-s2.0-S2772662224000857-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated optimization and ANOVA approach for reinforcing concrete beams with glass fiber polymer","authors":"Younes Nouri ,&nbsp;Mohammad Ali Ghanbari ,&nbsp;Pouyan Fakharian","doi":"10.1016/j.dajour.2024.100479","DOIUrl":"10.1016/j.dajour.2024.100479","url":null,"abstract":"<div><p>Concrete beams are commonly used in construction projects to provide structural support. Reinforcing these beams with steel and Glass Fiber Reinforced Polymer (GFRP) can increase their strength and durability. Steel reinforcement is a traditional method used for decades, while GFRP is a newer material that offers several advantages, including corrosion strength and lighter weight. Combining both materials to reinforce concrete beams can result in a stronger and more resilient structure, making it an ideal choice for many construction projects. In this study, the behavior of reinforced concrete beams with steel and GFRP reinforcement is numerically investigated, and the effect of steel percentage and GFRP percentage on the mechanical strength and energy response of the beam is determined using the Response Surface Methodology (RSM). Using the ABAQUS software, a widely used Finite-Element Analysis (FEA), the numerical modeling is first verified, and then targeted analyses are performed on the beam using the tests specified by the RSM. Then, based on the strength and energy of the beam, a two-variable Analysis of Variance (ANOVA) and two-objective optimization are performed to investigate the effect of changing each parameter of steel percentage and GFRP percentage on beam strength and energy. Several laboratory studies have been conducted on the behavior of concrete beams with steel and GFRP bars, but no research has statistically and numerically examined their behavior. We also simultaneously optimized both strength and energy parameters based on the ratio of steel and GFRP and compared them with numerical values. We show an interaction relationship between steel and GFRP ratio in the strength and energy of concrete beams. In the beam with hybrid reinforcement, with the increase of steel and GFRP ratios, the amount of strength and energy does not increase linearly, and there is a quadratic relationship between them.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100479"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000833/pdfft?md5=01fdba0a19dae39816b9f27a153709f9&pid=1-s2.0-S2772662224000833-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141047462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative assessment of machine learning algorithms in the IoT-based network intrusion detection systems","authors":"Milan Samantaray ,&nbsp;Ram Chandra Barik ,&nbsp;Anil Kumar Biswal","doi":"10.1016/j.dajour.2024.100478","DOIUrl":"10.1016/j.dajour.2024.100478","url":null,"abstract":"<div><p>The rapid increase in online risks is a reflection of the exponential growth of Internet of Things (IoT) networks. Researchers have proposed numerous intrusion detection techniques to mitigate the harm caused by these threats. Enterprises use intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) to keep their networks safe, stable, and accessible. Network intrusion detection solutions have lately integrated powerful Machine Learning (ML) techniques to safeguard IoT networks. Selecting the proper data features for effectively training such ML models is critical to maximizing detection accuracy and computational efficiency. However, the efficiency of these systems degrades in high-dimensional data spaces, and it is crucial to have a suitable feature extraction method to eliminate extraneous data from the classification procedure. The detection accuracy and false positive rate of many ML-based IDSs also rise when the samples used to train the models are unbalanced. This study provides a detailed overview of the UNSW-NB15(DS-1) and NF-UNSWNB15(DS-2) datasets for intrusion detection, which will be utilized to develop and evaluate our models. In addition, this model uses the MaxAbsScaler algorithm to implement a filter-based feature scaling strategy . Then, use the condensed feature set to perform several ML techniques, including Support Vector Machines (SVM), K-nearest neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF), considering multiclass classification. Accuracy tests for the multiclass classification scheme were improved from 60% to 94% using the MaxAbsScaler-based feature scaling method.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000821/pdfft?md5=3385a086eaa10827b799d9bde51e99ac&pid=1-s2.0-S2772662224000821-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel feature selection method with transition similarity measure using reinforcement learning","authors":"Younes Bouchlaghem ,&nbsp;Yassine Akhiat ,&nbsp;Kaouthar Touchanti ,&nbsp;Souad Amjad","doi":"10.1016/j.dajour.2024.100477","DOIUrl":"10.1016/j.dajour.2024.100477","url":null,"abstract":"<div><p>Feature selection identifies the relevant features and removes the irrelevant and redundant ones, intending to obtain the best-performing feature subset. This paper proposes a new feedback feature selection system with a reinforcement learning-based method to identify the best feature subset. The proposed system mainly includes three parts. First, decision tree branches are used to traverse the state space to discover new rules and select the best feature subset. Second, a transition similarity measure is introduced to ensure that the system keeps exploring the state space by creating diverse branches to overcome the redundancy problem. Finally, the informative features are the most involved in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results in terms of AUC score, accuracy, and running time demonstrate the effectiveness of the proposed system, as it selects the fewest number of relevant features in less computational time.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100477"},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266222400081X/pdfft?md5=6894016745c552e9790e4b39f4193a03&pid=1-s2.0-S277266222400081X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intuitionistic fuzzy approach for prey–predator harvesting system with toxicity and time delay","authors":"M. Mukherjee ,&nbsp;D. Pal ,&nbsp;S.K. Mahato","doi":"10.1016/j.dajour.2024.100476","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100476","url":null,"abstract":"<div><p>This study presents an intuitionistic fuzzy prey–predator model with time delay, where several toxic substances from external sources contaminate the prey and predator species. The system consists of one prey and one predator, with the logistic growth of prey species and predation terms proportional to prey density. This system incorporates Holling type-II functional response and introduces a time delay in harvesting prey species over a precise size or age. The primary focus of this work is to investigate the impact of age or size-selective delay on the system’s stability within the intuitionistic fuzzy environment. The model employs triangular intuitionistic fuzzy numbers to account for imprecise biological parameters, and it is converted into an intuitionistic fuzzy model using the Hukuhara derivative. The fuzziness of the model is then resolved using Yager’s Ranking method. Without delay, the paper delves into the system’s dynamics, examining local and global stability and bionomic considerations. Numerical simulations are performed using MATLAB to validate the theoretical findings.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100476"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000808/pdfft?md5=6c8bb8c7ab2f650ee919eafac8f256db&pid=1-s2.0-S2772662224000808-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140914026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty","authors":"Yasaman Fallahpour ,&nbsp;Majid Rafiee ,&nbsp;Adel Elomri ,&nbsp;Vahid Kayvanfar ,&nbsp;Abdelfatteh El Omri","doi":"10.1016/j.dajour.2024.100475","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100475","url":null,"abstract":"<div><p>Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100475"},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000791/pdfft?md5=82c2880b0fa7eade14bbf3fa950d1917&pid=1-s2.0-S2772662224000791-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}