Decision Analytics Journal最新文献

{"title":"A novel behavioral penalty function for interval goal programming with post-optimality analysis","authors":"Mohamed Sadok Cherif","doi":"10.1016/j.dajour.2024.100511","DOIUrl":"10.1016/j.dajour.2024.100511","url":null,"abstract":"<div><p>Goal programming (GP) is a multi-objective extension of linear programming. Interval GP (IGP) is one of the earliest methods to expand the range of preferred structures in GP. The decision maker’s (DM’s) utility or preference in IGP is investigated by incorporating a widening range of underlying utility functions, commonly known as penalty functions. The basic idea of these functions is that undesirable deviations from the target levels of the goals are penalized regarding a constant or variable penalty value. The main concern with introducing the penalty functions is providing a wide range of a priori preference structures. Yet, the evaluation of how undesirable deviations are penalized based on DM’s behavioral preferences is not sufficiently addressed in the penalty function types developed in the GP literature. In real-world scenarios involving risk, the achievement levels of decision-making attributes are typically associated with the behavior of the DM. In such scenarios, the DM’s unavoidable attitude toward risk should be integrated into the decision-making process. We introduce the concept of behavioral penalty functions into the IGP approach, incorporating a risk aversion parameter tailored to the nature of each attribute to address this gap. This concept offers an innovative framework for capturing the preferences of the DMs and their various attitudes toward risk within the IGP approach. In this paper, we first introduce the concept of behavioral penalty functions. Next, we develop a behavioral utility-based IGP model. Finally, we present a portfolio selection case study to demonstrate the applicability and efficacy of the proposed procedure, followed by a post-optimality analysis and comparisons with other GP approaches.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001152/pdfft?md5=54776e19883a23fdb187347a6c1d0b14&pid=1-s2.0-S2772662224001152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048892","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 optimized ensemble model for predicting average localization error of wireless sensor networks","authors":"Isaac Kofi Nti,&nbsp;Sidharth Sankar Rout,&nbsp;Jones Yeboah","doi":"10.1016/j.dajour.2024.100510","DOIUrl":"10.1016/j.dajour.2024.100510","url":null,"abstract":"<div><p>Wireless sensor networks (WSNs) are widely utilized in various applications due to their compact size, cost-effectiveness, and ease of deployment. Nonetheless, one of the biggest problems in WSNs is getting a reasonable estimate of the average location error of a node at the setup in the least amount of time. Wireless sensor networks can undergo changes over time due to various external and internal factors, such as environmental conditions, network congestion, hardware failures, or software updates. When these changes occur, the network may require redesigning, which can incur significant expenses. Traditional WSNs approaches, on the other hand, have been explicitly programmed, which makes it hard for networks to respond dynamically. Therefore, machine learning (ML) techniques can be used to respond appropriately in such scenarios. In this work, we proposed an optimized ML ensemble model for (i) identifying the critical network parameters for node localization when setting up wireless sensor networks with the accuracy needed in a short amount of time and (ii) predicting the average localization error of wireless sensor networks. We used the random forest algorithm with optimized hyperparameters from different optimization techniques to predict average localization error (ALE) using independent features like node density, anchor ratio, transmission range, and iterations.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100510"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001140/pdfft?md5=6b942c3ed78c4a9da78b61d1b79fa86c&pid=1-s2.0-S2772662224001140-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048890","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 simulation-based Digital Twin for smart warehouse: Towards standardization","authors":"Zakka Ugih Rizqi ,&nbsp;Shuo-Yan Chou ,&nbsp;Winda Nur Cahyo","doi":"10.1016/j.dajour.2024.100509","DOIUrl":"10.1016/j.dajour.2024.100509","url":null,"abstract":"<div><p>Cyber–physical systems are developed to meet the need to improve process flexibility, optimality, and transparency through Digital Twin (DT). Unfortunately, the application of DT is still limited in practice, and there is no standard way to achieve integration. An Asset Administration Shell (AAS) appears as a promising concept for realizing DT in a standard manner. A literature review shows that most studies only reached static DT and only considered a few specific assets, especially unmovable ones. This study contributes to DT development by proposing 3D-based computer simulation technology as dynamic DT based on the AAS Framework. The proposed concept enables DT to conduct dynamic monitoring, optimization, and direct controlling. A smart warehouse in Taiwan is used to verify the proposed concept. Assets considered include Automated Guided Vehicles (AGVs), Operator, Conveyor, Forklift, and Storage Rack. AAS structure, simulation model built in FlexSim software, and system integration architecture have been constructed. Two advantages of using simulation for DT are demonstrated: multi-objective simulation–optimization for AGV capacity planning and creating dynamic dashboards. Based on the proposed concept, industry 4.0 scenarios can be integrated comprehensively.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100509"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001139/pdfft?md5=837afc2d20f92e8bcf3d40b38cfacc99&pid=1-s2.0-S2772662224001139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048891","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 analysis of metaheuristic algorithms in interval-valued sustainable economic production quantity inventory models using center-radius optimization","authors":"Mamta Keswani","doi":"10.1016/j.dajour.2024.100508","DOIUrl":"10.1016/j.dajour.2024.100508","url":null,"abstract":"<div><p>The real-world production inventory systems involve uncertainties surrounding demand, production, defectiveness, and costs, which pose significant challenges. Various methodologies, including interval, fuzzy, stochastic, and fuzzy-stochastic approaches, have been developed to address these challenges. Among these, the interval approach offers a realistic representation of uncertainty. This study develops a green production model within an interval-based framework, incorporating interval-valued representations of defective rates and demand, which is also stochastic in nature. Differential equations governing inventory levels are formulated in an interval format and solved using advanced parametric techniques. The study extends to profit optimization within this interval-based framework, with the profit maximization problem transformed into a crisp form using interval order relations and center-radius optimization. The optimized solution is obtained through various metaheuristic algorithms. A numerical example validates the proposed model, and sensitivity analyses explore variations in different algorithms and inventory parameters. Additionally, statistical analysis using ANOVA tests is performed. This research contributes to production inventory management by providing a robust framework for handling uncertainty and optimizing performance in real-world scenarios.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100508"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001127/pdfft?md5=ff0908942e5fe5a475ed089698f2f3a9&pid=1-s2.0-S2772662224001127-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006764","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 deep learning model to assist visually impaired in pothole detection using computer vision","authors":"Arjun Paramarthalingam ,&nbsp;Jegan Sivaraman ,&nbsp;Prasannavenkatesan Theerthagiri ,&nbsp;Balaji Vijayakumar ,&nbsp;Vignesh Baskaran","doi":"10.1016/j.dajour.2024.100507","DOIUrl":"10.1016/j.dajour.2024.100507","url":null,"abstract":"<div><p>Visually impaired individuals encounter numerous impediments when traveling, such as navigating unfamiliar routes, accessing information, and transportation, which can limit their mobility and restrict their access to opportunities. However, assistive technologies and infrastructure solutions such as tactile paving, audio cues, voice announcements, and smartphone applications have been developed to mitigate these challenges. Visually impaired individuals also face difficulties when encountering potholes while traveling. Potholes can pose a significant safety hazard, as they can cause individuals to trip and fall, potentially leading to injury. For visually impaired individuals, identifying and avoiding potholes can be particularly challenging. The solutions ensure that all individuals can travel safely and independently, regardless of their visual abilities. An innovative approach that leverages the You Only Look Once (YOLO) algorithm to detect potholes and provide auditory or haptic feedback to visually impaired individuals has been proposed in this paper. The dataset of pothole images was trained and integrated into an application for detecting potholes in real-time image data using a camera. The app provides feedback to the user, allowing them to navigate potholes and increasing their mobility and safety. This approach highlights the potential of YOLO for pothole detection and provides a valuable tool for visually impaired individuals. According to the testing, the model achieved 82.7% image accuracy and 30 Frames Per Second (FPS) accuracy in live video. The model is trained to detect potholes close to the user, but it may be hard to detect potholes far away from the user. The current model is only trained to detect potholes, but visually impaired people face other challenges. The proposed technology is a portable option for visually impaired people.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100507"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001115/pdfft?md5=6ba37c16b7b3913b63959c8ebb277ada&pid=1-s2.0-S2772662224001115-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953679","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 Cognitive Reliability and Error Analysis Method (CREAM) and optimization for enhancing human reliability in blockchain","authors":"Azam Modares ,&nbsp;Vahideh Bafandegan Emroozi ,&nbsp;Hadi Gholinezhad ,&nbsp;Azade Modares","doi":"10.1016/j.dajour.2024.100506","DOIUrl":"10.1016/j.dajour.2024.100506","url":null,"abstract":"<div><p>Minor errors in smart contract coding on the blockchain can lead to significant and irreversible economic losses for transaction parties. Therefore, mitigating the risk posed by coding errors is crucial, necessitating the development of approaches to enhance human reliability in coding. The Cognitive Reliability and Error Analysis Method (CREAM) is one such approach, examining how environmental conditions affect the human error probability (HEP). Within CREAM, Common Performance Conditions (CPCs) influence error probability. This study ranks CPCs in smart contract coding based on their importance in coding reliability using the Bayesian Best Worst Method (BWM). Two methods are developed based on basic CREAM. In the first method, experts specify the control mode based on their opinions, and the probability of experts’ coding errors is determined according to the control level. In the second method, an optimization problem is formulated to select the most suitable programs, enhancing experts’ coding reliability. The proposed model considers energy, cost, and organizational budget factors to identify the optimal smart contract while minimizing the risks and costs associated with human errors. A case study in the electronics supply chain validates the applicability and efficacy of the proposed methods. Results from the first method indicate an opportunistic control mode. In contrast, the proposed model shows that improving CPC levels has a more significant effect, shifting the control mode towards a tactical control and reducing HEP to 0.00249.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100506"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001103/pdfft?md5=fb2cb6c291465368aa5428981750d253&pid=1-s2.0-S2772662224001103-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951320","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 evaluation framework for Environmental, Social, and Governance-driven social media performance through Multi-criteria Decision-Analysis","authors":"Frank Stevens,&nbsp;Evangelos Grigoroudis ,&nbsp;Constantin Zopounidis,&nbsp;Konstantinos P. Tsagarakis","doi":"10.1016/j.dajour.2024.100505","DOIUrl":"10.1016/j.dajour.2024.100505","url":null,"abstract":"<div><p>Social media is crucial in providing data for networking with individuals, companies, or industries. This study deals with the topic of Environmental, Social and Governance (ESG). These can be seen as a set of standards which impacts a corporation’s structure and performance. Twenty years ago, when ESG started to gain notoriety, ESG initiatives were labeled as a tool to ensure ‘ethical’ and/or ‘responsible’ investing. This study collects data from LinkedIn company profiles on the industry, the staff, the number of followers, and the state where the company has its headquarters. In addition, the media’s take on pending legislation on ESG was assessed. Based on data from 537 companies, indicators were created, either normalized, the median, or ranked, and analyzed with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The current work provides information on this variation of ESG activity within the different states in the USA. Multi-criteria Decision Analysis (MCDA) was applied by weighing four criteria. The overall assessment was presented depending on the combined weighted performance score of the four criteria. The results showed that 36% of the companies active within the ESG landscape represent the “Financial Services” and “Management Consulting” industries. 65% of the investigated sample represented companies with up to 10 employees. Moreover, eight states perform well on both median of staff and followers (in alphabetical order: Michigan, Mississippi, Missouri, New Hampshire, New Mexico, Ohio, Oklahoma, and Wisconsin). Lastly, through the MCDA method, it was also observed that there are not many variations when different weights are applied to the four criteria: i.e., “the normalized number of companies by state,” “the median of the followers of the company per state,” “the median of the staff of the company per state,” and the “sentiment of the legislation.” Finally, the top five highly ranked states considering ESG visibility on LinkedIn through their companies are Ohio, Michigan, Delaware, Georgia, and Pennsylvania. The results indicate that companies can more proactively advertise themselves as compliant with current ESG principles. This methodology can also be applied to other topics and regions to assess online activity in line with existing indicators.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001097/pdfft?md5=4522494ca16b652cf82e2a46fe5f33c7&pid=1-s2.0-S2772662224001097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839784","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 AHP-TOPSIS approach for bamboo product evaluation and selection in rural communities","authors":"Wirachchaya Chanpuypetch ,&nbsp;Jirawan Niemsakul ,&nbsp;Walailak Atthirawong ,&nbsp;Tuangyot Supeekit","doi":"10.1016/j.dajour.2024.100503","DOIUrl":"10.1016/j.dajour.2024.100503","url":null,"abstract":"<div><p>This study introduces a decision support model integrating the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select an economic tree product champion (bamboo) to benefit rural communities. An extensive literature review and expert discussions identified sixteen sub-criteria distributed across five main criteria. The study proposes four categories of bamboo products as alternatives, emphasizing community-level production capacity. The AHP determines priority weights, while TOPSIS prioritizes alternatives conducive to becoming the product champion within a community case study. The findings affirm the efficacy of Multi-Criteria Decision-Making (MCDM) in identifying a champion, with “Value addition potential,” “Domestic market demand,” and “International market (export) demand” identified as pivotal criteria. Bamboo culm-based products for energy-related applications emerged as the chosen product champion in a community case study in Thailand. This study offers practical implications for rural communities and potential investors in economic tree ventures, allowing the customization of decision criteria and alternatives for specific contexts. Socially, the focus on bamboo highlights diverse benefits along the entire supply chain, from upstream to downstream. The research pioneers a decision support model, providing insights into market opportunity analysis and supply chain network design based on the selected product champion.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100503"},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001073/pdfft?md5=80c7243b46569562344ccee4a077ca34&pid=1-s2.0-S2772662224001073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845644","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 stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide","authors":"Ying-Chih Sun ,&nbsp;Ozlem Cosgun ,&nbsp;Raj Sharman ,&nbsp;Pavankumar Mulgund ,&nbsp;Dursun Delen","doi":"10.1016/j.dajour.2024.100504","DOIUrl":"10.1016/j.dajour.2024.100504","url":null,"abstract":"<div><p>As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100504"},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001085/pdfft?md5=5575a0c4ac70d0a61890121fcff2341f&pid=1-s2.0-S2772662224001085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841674","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 systematic review of Digital Twins in efficient pandemic management with challenges and emerging trends","authors":"Ettilla Mohiuddin Eumi","doi":"10.1016/j.dajour.2024.100502","DOIUrl":"10.1016/j.dajour.2024.100502","url":null,"abstract":"<div><p>In recent years, Digital Twins (DTs) implementation has significantly impacted various sectors like industry, healthcare, engineering, and technology. However, the examination of these areas concerning pandemic management is still in its early stages. To bridge this gap, a systematic literature review was conducted here spanning from 2017 to March 2024, with a specific focus on COVID-19-related issues from 2020 to March 2024. Employing a five-step filtering process, nearly 10,000 articles were initially identified based on specific search strings. Subsequently, 297 publications were selected and examined across pre-pandemic, pandemic, and post-pandemic phases to discern emerging patterns, limitations, and future research directions. Drawing from these insights, a concept for a ‘Digital-Twin-based Smart Pandemic City’ was proposed, aiming to ensure contemporary amenities while preparing for potential pandemics by leveraging advanced cloud storage and blockchain technology for secure data aggregation. Anticipated challenges that may arise in implementing this model in the future were also explored in this review study.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100502"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001061/pdfft?md5=7d8d793ea8cd137f12979c7693e88bba&pid=1-s2.0-S2772662224001061-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630519","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}