Remaining useful life predictions of aero-engines using dilated LSTM with attention mechanism Abdeltif Boujamza, Saâd Lissane Elhaq Franklin Open, 2026 Prognostics and Health Management (PHM) is a pivotal field that focuses on predicting the future health and operational life of systems and components. One of the key aspects of PHM in aviation is predicting the Remaining Useful Life (RUL) of aircraft engines. Accurate RUL predictions enable airlines and maintenance crews to schedule timely interventions, minimizing the risk of unexpected failures and optimizing maintenance schedules. This paper proposes a novel integration of Dilated Long Short Term Memory with an Attention mechanism (DLSTM+A) for RUL prediction of aircraft engines, addressing the limitations of existing approaches in capturing long-term temporal dependencies. Unlike traditional LSTM models that struggle with long-range dependencies and conventional Dilated RNNs that suffer from gradient issues with simple RNN cells, our approach combines the multi-scale temporal modeling capabilities of dilated connections with the superior memory retention of LSTM cells and the selective focus of attention mechanisms. The methodology consists of two primary phases: data preparation with distribution-driven feature selection and model development with hyperparameter optimization. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, our DLSTM+A model demonstrates significant improvements over state-of-the-art methods: 56% reduction in RMSE and 43% reduction in score function compared to standard LSTM, and 46% improvement in RMSE and 30% improvement in score function compared to LSTM with Attention. The model achieves the lowest RMSE (7.54) and score function (223) among all evaluated approaches, establishing a new benchmark for RUL prediction accuracy in turbofan engines while maintaining computational efficiency through dilated skip connections.
A Novel Truck Appointment System for Container Terminals Fatima Bouyahia, Sara Belaqziz, Youssef Meliani, Saâd Lissane Elhaq, Jaouad Boukachour Sustainability Switzerland, 2025 Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO2 emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations.
An integrated approach based on classification and forecasting intermittent demand model for urban pick-up: a case study of Moroccan carrier Leila Bourrich, Saâd Lissane Elhaq International Journal of Logistics Systems and Management, 2025 Pick-up links play a crucial role in logistics chains. It is the most expensive and polluting part of urban logistics. Management and decision-making must be optimised to improve their performance, and develop urban logistics sustainably. Several factors make its management difficult. Due to that, this process produces intermittent demand series. Our aim in this paper is to improve the pick-up chain by anticipating customers' requests. Based on K-means clustering, the integrated approach proposes two novel estimation models for demand occurrence, followed by a forecasting model derived from benchmarking studies between three methods: SES, Croston, and SBA on a real dataset. Our approach demonstrates the value of the classification model and the outperformance of SBA over other methods. This area has not been researched. Thus, this study contributes to urban logistics durability and freight transportation. Consequently, carriers will be provided with new-and-improved benefits in the future based on this relevant context.
Predicting Oil Temperature in Electrical Transformers Using Neural Hierarchical Interpolation Abdeltif Boujamza, Saâd Lissane Elhaq Journal of Engineering United Kingdom, 2025 Effective electricity consumption planning is critical for power distribution. Ensuring the distribution network aligns with expected demand fluctuations is a challenging task influenced by various time‐related and seasonal variables. This study focuses on improving transformer oil temperature forecasting, an indicator of transformer health, using the neural hierarchical interpolation for time series (NHITS) model. The NHITS model’s architecture is designed to handle long‐term forecasting efficiently, making it ideal for capturing extended trends in transformer oil temperature. It incorporates multirate signal sampling via MaxPool layers and hierarchical interpolation to merge predictions across different time scales. The proposed methodology involves two key phases: data preparation and model development. In the data preparation phase, the electricity transformer temperature (ETT) datasets are used, normalized with a standard scaler, and essential features such as oil temperature and external power load are selected. During the model development phase, the proposed NHITS model is trained and its hyperparameters are optimized for optimal performance. The study evaluates the model’s performance under various conditions, including the comparison of multivariate and univariate time series, the effects of short and long‐term forecasting horizons, and the impact of temporal resolution. The model was validated using the ETT dataset, and our results were benchmarked against a previous study that employed the same dataset and used the Informer model. The results indicate that the NHITS model outperforms the Informer model, showing an average decrease of 51.37% in mean squared error (MSE) and 37.83% in mean absolute error (MAE). These findings highlight the model’s ability to capture both long‐term and short‐term characteristics of time series data, making it a promising solution for forecasting transformer oil temperatures.
SDAPI: a systematic approach to integrating Industry 4.0 and lean manufacturing for SME improvement Hafsa El Kaime, Saad Lissane Elhaq International Journal of Industrial and Systems Engineering, 2025 Many businesses, particularly small and medium-sized enterprises (SMEs), seek to improve productivity and reduce resource usage. Lean manufacturing (LM) is a popular method for optimising processes by eliminating non-value-added activities and improving efficiency and flexibility. However, in today's rapidly changing technological and market environment, companies must also adopt innovative production management approaches to stay competitive. The Fourth Industrial Revolution and related technologies offer the opportunity to take current manufacturing systems to the next level. While previous research has explored the concept of 'Lean 4.0', which combines Industry 4.0 and LM, there has been less focus on the relationship between methodological approaches and technological concepts. This research aims to fill this gap by presenting a methodological-technological framework for implementing Industry 4.0 technologies in SMEs in order to achieve the objectives of LM. The proposed methodology, called SDAPI, is developed through a literature reviews, it consists of five steps: specify, detect, analyse, propose, and implement.
Applying Semi-AutoML for Vessel Traffic Flow Prediction: Case Studies of Two Ports Abdeltif Boujamza, Mohamed El Hafta, Saâd Lissane Elhaq, Ahmed Loukili Journal of Engineering United Kingdom, 2025 Accurate prediction of vessel traffic flow plays a crucial role in maritime supply chain operations, enabling efficient resource allocation and timely delivery of goods. This study contributes to the field of maritime logistics and transportation management by employing a semiautomated machine learning (semi‐AutoML) approach and presenting a comparative analysis to predict vessel traffic flow in two distinct port settings. The proposed approach involves automatically evaluating the performance of a set of preselected models to identify the best‐fitting models for the dataset. This is followed by a manual tuning phase to further optimize the performance of the selected models. The proposed methodology is implemented on limited‐scale datasets from two separate case studies: Mohammedia port and Los Angeles port, with the latter serving as a benchmark against an existing study. The performance of the models in predicting vessel traffic flow was evaluated using different metrics. The findings indicate improvements in forecast accuracy, with an RMSE of 2.22 for Mohammedia port and 11.65 for Los Angeles port. The results for Los Angeles port showcase a notable improvement of up to 70.95% in RMSE compared to the outcomes of a previous study, emphasizing the superior efficacy of the proposed methodology in predicting vessel traffic flow. The workflow presented in this study was implemented using the PyCaret framework, and the Python code implementation is publicly available on Colab (https://colab.research.google.com/drive/1Wk5Y_1uFSEYJLx49nXPaoPnGUgLGY6cT).
Optimizing Remaining Useful Life Predictions for Aircraft Engines: A Dilated Recurrent Neural Network Approach Abdeltif Boujamza, Saâd Lissane Elhaq IFAC Papersonline, 2024 Predicting the remaining useful life (RUL) plays a crucial rule in the field of prognostics and health management (PHM) for mechanical systems. Specifically within the domain of turbofan engines, predicting RUL plays a vital role in strategically planning maintenance activities. Consequently this aids in optimizing the overall performance of the energy system by reducing downtime and improving sustainability and efficiency. This research endeavors to forecast the RUL of turbofan engines. It employs a Dilated Recurrent Neural Network (D-RNN) Approach, a neural network structure that integrates dilated convolutions into the recurrent layers. The model underwent fine-tuning through a random grid search optimization and was tested using the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset. The results showcase the superior performance of the proposed D-RNN, outperforming the accuracy of other research studies.
A Review and Current Practices on the Valorization of Ship-Generated Oily Waste Hafsa Elkaime, Saad Lissane elhaq, Ahmed Loukili IFAC Papersonline, 2024 The valorization of ship-generated oily waste involves the processing and recovery of hydrocarbons and other materials present in these wastes, which is crucial for reducing marine pollution and risks to coastal ecosystems. However, the literature on this subject is currently scarce and underdeveloped. Our aim is to explain and classify the different types of ship wastes, to detail how hydrocarbon residues are produced on ships, and to examine various methods of treating and valorizing these wastes. This approach will facilitate our future research efforts to digitize a carefully selected technology for valorizing ship-generated oily waste.
Nonlinear control based on a combination between sliding mode and Backstepping of grid connected photovoltaic system Proceedings of the International Conference on Industrial Engineering and Operations Management, 2017
Adaptive control of glucose concentration in diabetic subject's blood IFAC Proceedings Series, 1989
RECENT SCHOLAR PUBLICATIONS
Artificial intelligence application in municipal and port waste management: systematic literature review A Hamraoui, H Ech-cheikh, A Douraid, A Loukili, S Lissane Elhaq, ... Clean Technologies and Environmental Policy 28 (5), 123 , 2026 2026 Citations: 1
Remaining useful life predictions of aero-engines using dilated LSTM with attention mechanism A Boujamza, SL Elhaq Franklin Open, 100514 , 2026 2026 Citations: 1
A Novel Truck Appointment System for Container Terminals F Bouyahia, S Belaqziz, Y Meliani, S Lissane Elhaq, J Boukachour Sustainability 17 (13), 5740 , 2025 2025 Citations: 2
A review on ship-generated oily waste management at ports: current practices, challenges and future directions B Abdellaoui, H Ech-Cheikh, M Sadik, A Rachid, S Lissane Elhaq, ... Environment, Development and Sustainability 27 (3), 5925-5980 , 2025 2025 Citations: 9
SDAPI: a systematic approach to integrating Industry 4.0 and lean manufacturing for SME improvement H El-Kaime, SL Elhaq International Journal of Industrial and Systems Engineering 50 (3), 281-302 , 2025 2025
A Novel Truck Appointment System for Container Terminals. Sustainability 2025, 17, 5740 F Bouyahia, S Belaqziz, Y Meliani, S Lissane Elhaq, J Boukachour 2025
Applying Semi‐AutoML for Vessel Traffic Flow Prediction: Case Studies of Two Ports A Boujamza, M El Hafta, SL Elhaq, A Loukili Journal of Engineering 2025 (1), 4652508 , 2025 2025
Predicting Oil Temperature in Electrical Transformers Using Neural Hierarchical Interpolation A Boujamza, S Lissane Elhaq Journal of Engineering 2025 (1), 9714104 , 2025 2025 Citations: 1
An integrated approach based on classification and forecasting intermittent demand model for urban pick-up: a case study of Moroccan carrier L Bourrich, SL Elhaq International Journal of Logistics Systems and Management 51 (1), 1-41 , 2025 2025
Towards Smart Ports: Design and Prototyping of an IoT-Based Ship-Generated Oily Waste Collection System B Abdellaoui, SL Elhaq, M Sadik 2024 International Conference on Ubiquitous Networking (UNet) 10, 1-8 , 2024 2024 Citations: 1
Module Setting for Use in IoT-Based Maritime Petroleum Waste Recovery Prototype System B Abdellaoui, A Ennajih, SL Elhaq, A Mounadel Ubiquitous Networking: 9th International Symposium, UNet 2023, Clermont … , 2024 2024
Forecasting Delivered Ship Waste to Petroleum Ports Using RNN Models Z Boufakri, A Boujamza, S Lissane Elhaq, A Loukili International Conference on Connected Objects and Artificial Intelligence, 47-52 , 2024 2024 Citations: 1
Long-Term Vessel Arrival Forecasting at Port with Long Short-Term Memory: A Case Study A Boujamza, Z Boufakri, SL Elhaq, A Loukili International Conference on Connected Objects and Artificial Intelligence, 67-72 , 2024 2024
Optimizing remaining useful life predictions for aircraft engines: A dilated recurrent neural network approach A Boujamza, SL Elhaq IFAC-PapersOnLine 58 (13), 811-816 , 2024 2024 Citations: 19
Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review A Mounadel, H Ech-Cheikh, S Lissane Elhaq, A Rachid, M Sadik, ... Environmental technology reviews 12 (1), 316-336 , 2023 2023 Citations: 29
Ensemble forecasting for predicting petroleum products traffic in oil terminal A Boujamza, SL Elhaq, A Loukili 2023 14th International Conference on Intelligent Systems: Theories and … , 2023 2023 Citations: 1
Toward Best Performance of Node's Radio Module Setting for Use in IoT-Based Maritime Petroleum Waste Recovery Prototype System B Abdellaoui, A Ennajih, SL Elhaq, A Mounadel, M Sadik International Symposium on Ubiquitous Networking, 139-148 , 2023 2023
Delivery Planning Application For An Ice Distribution Company: A Case Study Y Meliani, Y Hani, SL Elhaq, A El Mhamedi 2023 International Conference on Networking, Sensing and Control (ICNSC) 1, 1-6 , 2023 2023
Digging Into Mining 4.0: Applying Systems Engineering to a Digital Spare Parts Management System H Lafquih, I Krimi, SL Elhaq IEEE Engineering Management Review 51 (4), 191-204 , 2023 2023 Citations: 3
Improvement of ship-generated oily waste collection process from ports through the use of virtual Internet of Things system B Abdellaoui, H Ech-Cheikh, M Sadik, A Rachid, S Lissane Elhaq, ... Environmental Monitoring and Assessment 195 (7), 896 , 2023 2023 Citations: 5
MOST CITED SCHOLAR PUBLICATIONS
Attention-based LSTM for remaining useful life estimation of aircraft engines A Boujamza, SL Elhaq IFAC-PapersOnLine 55 (12), 450-455 , 2022 2022 Citations: 58
A developed Tabu Search algorithm for heterogeneous fleet vehicle routing problem Y Meliani, Y Hani, SL Elhaq, A El Mhamedi IFAC-PapersOnLine 52 (13), 1051-1056 , 2019 2019 Citations: 46
Modelling, identification and control of sugar evaporation–theoretical design and experimental evaluation SL Elhaq, F Giri, H Unbehauen Control Engineering Practice 7 (8), 931-942 , 1999 1999 Citations: 44
A tabu search based approach for the heterogeneous fleet vehicle routing problem with three-dimensional loading constraints Y Meliani, Y Hani, SL Elhaq, A El Mhamedi Applied Soft Computing 126, 109239 , 2022 2022 Citations: 39
Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review A Mounadel, H Ech-Cheikh, S Lissane Elhaq, A Rachid, M Sadik, ... Environmental technology reviews 12 (1), 316-336 , 2023 2023 Citations: 29
A model integrating a smart approach to support the national port strategy for a horizon of 2030 A LOUKILI, SL ELHAQ 2018 International Colloquium on Logistics and Supply Chain Management … , 2018 2018 Citations: 26
Modelling methodology for the simulation of the manufacturing systems R Tajini, SL Elhaq, A Rachid International Journal of Simulation and Process Modelling 9 (4), 285-305 , 2014 2014 Citations: 21
Optimizing remaining useful life predictions for aircraft engines: A dilated recurrent neural network approach A Boujamza, SL Elhaq IFAC-PapersOnLine 58 (13), 811-816 , 2024 2024 Citations: 19
Intelligent transportations systems: review of current challenges and success factors: the case of developing countries Y El Mokaddem, F Jawab, LE Saad 2019 international colloquium on logistics and supply chain management … , 2019 2019 Citations: 19
Review of good practices in urban freight transportation and benchmarking city logistics schemes O Meryem, LE Saâd, K Mohamed, J Fouad 2019 International Colloquium on Logistics and Supply Chain Management … , 2019 2019 Citations: 17
Methodology for work measurement of the human factor in industry R Tajini, SL Elhaq International Journal of Industrial and Systems Engineering 16 (4), 472-492 , 2014 2014 Citations: 17
Methodology for implementation of industry 4.0 technologies in supply chain for SMEs H El-kaime, SL Elhaq International Conference on Artificial Intelligence & Industrial … , 2020 2020 Citations: 12
A review on ship-generated oily waste management at ports: current practices, challenges and future directions B Abdellaoui, H Ech-Cheikh, M Sadik, A Rachid, S Lissane Elhaq, ... Environment, Development and Sustainability 27 (3), 5925-5980 , 2025 2025 Citations: 9
The vehicle routing problem with Time Window and Stochastic Demands (VRPTW-SD) M Boujlil, SL Elhaq 2020 IEEE 13th International Colloquium of Logistics and Supply Chain … , 2020 2020 Citations: 9
The development of controllers for a multiple-effect evaporator in sugar industry SL Elhaq, F Giri, H Unbehauen 1997 European Control Conference (ECC), 3318-3322 , 1997 1997 Citations: 9
Simulating demand uncertainty and inventory control variability of multi-echelon distribution supply chain H Ech-Cheikh, SL Elhaq, A Rachid, A Douraid 2014 International Conference on Logistics Operations Management, 27-34 , 2014 2014 Citations: 8
Optimisation conjointe des coûts de transport et de stock dans une chaîne logistique de distribution multi niveaux: Une approche basée sur la simulation. K Eddoug, SL El Haq Xème Conférence Internationale: Conception et Production Intégrées , 2015 2015 Citations: 7
A conceptual and UML models of procurement process for simulation framework A Douraid, SL Elhaq, H Ech-cheikh International Journal of Computer Science Issues (IJCSI) 9 (6), 120 , 2012 2012 Citations: 7
Tabu Search for urban freight VRP: Fundamental aspects and parameters tuning evaluation Y Meliani, SL Elhaq, Y Hani, A El Mhamedi 2019 International Colloquium on Logistics and Supply Chain Management … , 2019 2019 Citations: 6
Performance evaluation of complex multi-echelon distribution supply chain K Eddoug, SL ElHaq, H Echcheikh 2018 4th International Conference on Logistics Operations Management (GOL), 1-10 , 2018 2018 Citations: 6
