Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study DOI Open Access

Khaoula Khlie,

Zoubida Benmamoun, Widad Fethallah

et al.

Journal of Infrastructure Policy and Development, Journal Year: 2024, Volume and Issue: 8(8), P. 6639 - 6639

Published: Aug. 26, 2024

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer efficiently. This paper aims Investigate on the application of generative AI models in forecasting. Two were used: Long Short-Term Memory (LSTM) networks Variational Autoencoder (VAE), results compared select optimal model terms performance accuracy. The difference actual predicted values also ascertain LSTM’s ability identify latent features basic trends data. Further, some research works focused computational efficiency scalability proposed methods providing guidelines implementation complicated techniques Based these results, LSTM have a promising enhancing consequently helpful decision-making process regarding control other resource allocation.

Language: Английский

Transformation of supply chain resilience research through the COVID-19 pandemic DOI
Dmitry Ivanov

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: March 30, 2024

Supply chain resilience is on the agenda of academia and industry like never before. One strong instigator for this phenomenon has been COVID-19 pandemic, which opened era global uncertainties vulnerabilities. In paper, we analyse transformation supply research through pandemic. Methodologically, use a hybrid approach based combination elements bibliometric expert analysis to compare main topics before, during, after Along with an expected observation about exponential growth literature in 2020, observe major shift from preparedness disruption predictions pre-pandemic towards recovery proactive adaptation pandemic post-pandemic research. Our systematically reveals some new topics, management practices, future areas resilience. particular, digital technology, viability, cross-industry ripple effect, intertwined networks have become impactful during Further developments these are be continued future. Managerial theoretical implications said conclude paper.

Language: Английский

Citations

40
Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space DOI Creative Commons
Siva Sai,

Utkarsh Yashvardhan,

Vinay Chamola

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 53497 - 53516

Published: Jan. 1, 2024

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact cyber threats have continued rise today's world. ever-evolving threat landscape poses challenges for organizations security professionals who continue looking better solutions tackle these threats. GAI technology provides an effective way them address issues automated manner with increasing efficiency. It enables work on more critical aspects which require human intervention, while systems deal general situations. Further, can detect novel malware threatening situations than humans. feature GAI, when leveraged, lead higher robustness system. Many tech giants like Google, Microsoft etc., are motivated by this idea incorporating elements their make efficient dealing tools Google Cloud Security Workbench, Copilot, SentinelOne Purple come into picture, leverage develop straightforward robust ways emerging perils. With advent domain, one also needs take account limitations drawbacks that such have. some periodically giving wrong results, costly training, potential being used malicious actors illicit activities etc.

Language: Английский

Citations

24
Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review DOI Open Access
Wenwen Chen, Yangchongyi Men, Noelia Fuster

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9145 - 9145

Published: Oct. 22, 2024

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview diverse AI models and algorithms employed in optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, their synergistic combinations traditional simulation methods. By employing capabilities, stakeholders can enhance decision-making processes, optimize resource utilization, minimize environmental impacts. Moreover, this paper identifies analyzes prominent challenges within logistics, such as reducing carbon emissions, minimizing waste generation, optimizing transportation routes while considering ecological factors. Furthermore, explores emerging trends AI-driven real-time data analytics, blockchain technology, autonomous systems, which hold immense potential for enhancing efficiency sustainability. Finally, outlines future research directions, emphasizing need further exploration hybrid approaches, robust frameworks, scalable solutions that accommodate dynamic uncertain environments.

Language: Английский

Citations

19
Addressing challenges to cloud manufacturing in industry 4.0 environment using an integrated approach: Implications for sustainability DOI Creative Commons
Hasin Md. Muhtasim Taqi,

Ibteahaz Nayeem,

A.B.M. Mainul Bari

et al.

Green Technologies and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100166 - 100166

Published: Jan. 1, 2025

Language: Английский

Citations

4
Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization DOI Open Access

N. Ramesh Babu,

Vidya Kamma,

R. Logesh Babu

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Language: Английский

Citations

4
ALPOA: Adaptive Learning Path Optimization Algorithm for Personalized E-Learning Experiences DOI Open Access

R. T. Subhalakshmi,

S. Geetha,

S. Dhanabal

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 5, 2025

In this study, we propose the Adaptive Learning Path Optimization Algorithm (ALPOA) to enhance personalized e-learning experiences by tailoring content delivery based on individual learner profiles. ALPOA employs a hybrid optimization framework combining Genetic (GA) and Particle Swarm (PSO) dynamically adjust learning paths. The algorithm considers multiple factors such as proficiency, speed, engagement level, difficulty. Experimental results demonstrate that outperforms traditional static models, achieving 25% improvement in efficiency, 30% increase engagement, 20% reduction redundancy. model was tested dataset of 1,500 learners, showing 97% accuracy predicting optimal paths 15% higher knowledge retention rate compared benchmark algorithms. ALPOA’s scalability adaptability make it promising solution for education systems, fostering improved outcomes satisfaction. Future work will focus integrating real-time feedback mechanisms expanding support diverse environments.

Language: Английский

Citations

3
A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do? DOI Creative Commons
Yang Chen, Samuel N. Kirshner, Антон Овчінніков

et al.

Manufacturing & Service Operations Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Problem definition: Large language models (LLMs) are being increasingly leveraged in business and consumer decision-making processes. Because LLMs learn from human data feedback, which can be biased, determining whether exhibit human-like behavioral decision biases (e.g., base-rate neglect, risk aversion, confirmation bias, etc.) is crucial prior to implementing into contexts workflows. To understand this, we examine 18 common that important operations management (OM) using the dominant LLM, ChatGPT. Methodology/results: We perform experiments where GPT-3.5 GPT-4 act as participants test these vignettes adapted literature (“standard context”) variants reframed inventory general OM contexts. In almost half of experiments, Generative Pre-trained Transformer (GPT) mirrors biases, diverging prototypical responses remaining experiments. also observe GPT have a notable level consistency between standard OM-specific well across temporal versions model. Our comparative analysis reveals dual-edged progression GPT’s making, wherein advances accuracy for problems with well-defined mathematical solutions while simultaneously displaying increased preference-based problems. Managerial implications: First, our results highlight managers will obtain greatest benefits deploying workflows leveraging established formulas. Second, displayed high response standard, inventory, non-inventory operational provides optimism offer reliable support even when details problem change. Third, although selecting models, like GPT-4, represents trade-off cost performance, suggest should invest higher-performing particularly solving objective solutions. Funding: This work was supported by Social Sciences Humanities Research Council Canada [Grant SSHRC 430-2019-00505]. The authors gratefully acknowledge Smith School Business at Queen’s University providing funding Y. Chen’s postdoctoral appointment. Supplemental Material: online appendix available https://doi.org/10.1287/msom.2023.0279 .

Language: Английский

Citations

2
A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do? DOI
Yang Chen, Meena Andiappan, Tracy A. Jenkin

et al.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Large language models (LLMs) such as ChatGPT have garnered global attention recently, with a promise to disrupt and revolutionize business operations. As managers rely more on artificial intelligence (AI) technology, there is an urgent need understand whether are systematic biases in AI decision-making since they trained human data feedback, both may be highly biased. This paper tests broad range of behavioral commonly found humans that especially relevant operations management. We although can much less biased accurate than problems explicit mathematical/probabilistic natures, it also exhibits many possess, when the complicated, ambiguous, implicit. It suffer from conjunction bias probability weighting. Its preference influenced by framing, salience anticipated regret, choice reference. struggles process ambiguous information evaluates risks differently humans. produce responses similar heuristics employed humans, prone confirmation bias. To make these issues worse, overconfident. Our research characterizes ChatGPT's behaviors showcases for researchers businesses consider potentialAI developing employing

Language: Английский

Citations

37
Benchmarking operations and supply chain management practices using Generative AI: Towards a theoretical framework DOI
Rameshwar Dubey, Angappa Gunasekaran, Θάνος Παπαδόπουλος

et al.

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2024, Volume and Issue: 189, P. 103689 - 103689

Published: July 25, 2024

Language: Английский

Citations

17
Cash flow dynamics in the supply chain during and after disruptions DOI Creative Commons
Dmitry Ivanov

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2024, Volume and Issue: 185, P. 103526 - 103526

Published: April 16, 2024

Supply chain resilience and the ripple effect have been widely studied, mostly focusing on material flow-related practices. The financial flow adjustments to cope with supply disruptions received much less attention. We contribute literature by examining impact of adapting payment terms during after disruptions. In particular, we perform a discrete event simulation analysis in anyLogistix for complex network investigate adjusting cash flows. Our results suggest that collaboratively is an effective strategy coping contrast, ad hoc immediate returns pre-disruption schemes do not yield visible improvements. Positive effects loans are observed if adjustment occurs proactively coordinated manner, especially when expediting payments downstream slowing down upstream. from our sensitivity accelerating/decelerating conversion cycles favour shorter deduce useful managerial insights reveal some new theoretical tensions related flows chains.

Language: Английский

Citations

16