34 research outputs found
Algorithme de commande pour les systemes energetiques "presque bilineaires" distribues (SPB) conductifs - convectifs - radiatifs
No full textStabilization of two-echelon supply networks with uncertain demand, multiple delays and switching topology using robust control
No full textInnovative Maintenance Model: Methodology of Decision Making and Global Economic Cost – Based Choice Using Fuzzy Logic Concepts
No full textBackground:In this paper, we propose the Development of a knowledge-based expert system applied to Electricity distribution Network maintenance.Method:The expert system utilizes the Matlab®platform, using its proprietary Fuzzy Logic Toolbox. The inputs to the expert system are the different situations related to cost variables to and the implementation of one of the maintenance optimizing solutions pertaining to the Live Work-Redundancy-Simultaneity-Security model as defined by the maintenance unit during a the global life cycle of the equipment. The output of the system indicates the economic feasibility and benefit of the solution to be adopted of for our Maintenance model. Application of the expert system methodology is shown as a simulation.Conclusion:The expert system may be of valuable assistance to utility engineers or asset managers in making strategic maintenance decisions such as “Economic solution”.</jats:sec
Assessing naive Bayes and support vector machine performance in sentiment classification on a big data platform
Get PDFNowadays, mining user reviews becomes a very useful mean for decision making in several areas. Traditionally, machine learning algorithms have been widely and effectively used to analyze user’s opinions on a limited volume of data. In the case of massive data, powerful hardware resources (CPU, memory, and storage) are essential for dealing with the whole data processing phases including, collection, pre-processing, and learning in an optimal time. Several big data technologies have emerged to efficiently process massive data, like Apache Spark, which is a distributed framework for data processing that provides libraries implementing several machine learning algorithms. In order to evaluate the performance of Apache Spark's machine learning library (MLlib) on a large volume of data, classification accuracies and processing time of two machine learning algorithms implemented in spark: naive Bayes and support vector machine (SVM) are compared to the performance achieved by the standard implementation of these two algorithms on large different size datasets built from movie reviews. The results of our experiment show that the performance of classifiers running under spark is higher than traditional ones and reaches F-measure greater than 84%. At the same time, we found that under spark framework, the learning time is relatively low
Battery Sizing Based on the Energy Lack and Surplus Analysis in a Stand-Alone Photovoltaic System
No full textResilient Emergency Procurement Strategies and Quantitative Metrics: A Review
No full textThe growing frequency and complexity of both natural and man- made crises like pandemics have highlighted the need for robust emergency logistics strategies during the last decade. Procurement is one of the critical areas impacting any emergency supply chain. Dealing with disruptions usually involves multi-sourcing, option contracts, and back-up suppliers as resilient strategies. The proposed review classifies emergency procurement strategies into three capacities following the capacity resilience framework: absorptive, adaptive, and restorative. Also, quantitative resilience metrics, modelling methods, and objectives for practical scenarios are discussed. Conclusions are extracted, and future research agendas outlined
AI-based demand forecasting models: A systematic literature review
No full textIn today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts.
This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search.
The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models
Lean Manufacturing and Six Sigma Critical Success Factors -A Case Study of the Moroccan Aeronautic Industry
Get PDFCompanies are constantly striving to achieve a highest rate of competitiveness. Thus, they implement either Lean manufacturing or Six sigma approach or both. However, their successful implementation is a real challenge. Hence, one of the proven tools to take up this challenge is a set of critical success factors (CSFs). This study provides a comparative statement of the recommendations suggested by every CSF from the perspective of each approach and examines how far the CSFs determine the successful implementation of Lean manufacturing and Six sigma in the Moroccan aeronautics industry. Also, based on a qualitative research methodology it assesses the importance assigned to each CSFs for each approach. The results would provide aeronautics managers with indicators and guidelines for a successful simultaneous implementation of Lean principles and Six Sigma techniques
A hybrid hw-rfr forecasting model: case of moroccan pharmaceutical sector
Get PDFSales forecasting is an essential element of effective supply chain management, particularly in the pharmaceutical sector where continuous availability of drugs is crucial. This article examines sales forecasts for fluoxetine, an antidepressant available on the Moroccan market under six trade names and 14 different forms. The main objective of this study is to compare the effectiveness of four forecasting models, namely Prophet Facebook, ARIMA, GRU and Holt-Winters through their accuracy, and to propose a hybrid model that will contribute to improving the accuracy of demand forecasts. Each model was applied individually to predict future sales, and evaluated using MAPE, MAE and RMSE metrics. Next, a hybrid model, integrating Holt-Winters and Random Forest Regressor methods, was developed to leverage the robustness of traditional models while improving predictive performance through machine learning techniques. The results of the study show that traditional models, such as ARIMA and Holt-Winters, offer a solid basis for sales forecasting. However, the hybrid HW-RFR (Holt-Winters Random Forest Regressor) model stands out for a significant improvement in forecast accuracy, demonstrating great robustness to fluctuations in fluoxetine demand. This article highlights the potential of hybrid models for forecasting pharmaceutical sales. The improved forecast accuracy achieved with the HW-RFR model provides stakeholders with more reliable information, enabling them to make informed decisions to optimize pharmaceutical supply chain managemen
Strategic Analysis of IoT Integration in 3PL Competition: A Simulation-Based Study
No full textDigital transformation is crucial for businesses to thrive in today’s rapidly evolving marketplace. It is a strategic choice that enables organizations to improve customer service, strengthen supplier relationships, and boost sales and business growth, ultimately enhancing their competitive stance. The Internet of Things (IoT) has become a transformative force across various domains, leveraging interconnected devices and sensors to gather and analyse data, thus enhancing decision making, efficiency, and innovation. This paper analyses the strategic competition between two 3PL firms integrating IoT technologies. Based on a game-theoretic model, the study uses Monte Carlo simulation and K-means clustering to identify distinct strategic groups and optimal adoption ranges. The findings highlight risks of over- or under-investments as well as asymmetric outcomes. Also, a set of recommendations and managerial insights are provided for better decision making in a tech-competitive setting
