Engineering Proceedings

Order results

Result details

Select all

Export citation of selected articles as:

Other

15 pages, 1705 KiB

Open AccessProceeding Paper

Hybrid LSTM-DES Models for Enhancing the Prediction Performance of Rail Tourism: A Case Study of Train Passengers in Thailand

by Piyaphong Supanyo, Prakobsiri Pakdeepinit, Pannanat Katesophit, Supawat Meeprom and Anirut Kantasa-ard

Eng. Proc. 2025, 97(1), 1; https://doi.org/10.3390/engproc2025097001 - 4 Jun 2025

Abstract

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

► Show Figures

Figure 1

7 pages, 979 KiB

Open AccessProceeding Paper

Toward a Demand-Driven Supply Chain: BLR Evaluating the Impact of AI and ML Integration in the Healthcare and Pharmaceutical Industry

by Majda Boualam and Imane Ibn El Farouk

Eng. Proc. 2025, 97(1), 2; https://doi.org/10.3390/engproc2025097002 - 5 Jun 2025

Abstract

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric literature review, how the integration of these technologies can support the implementation of a demand-driven supply chain approach in the global healthcare and pharmaceutical supply chains, which are facing remarkable challenges in ensuring demand-driven operations, especially in light of sudden disruptions such as the COVID-19 pandemic. Full article

► Show Figures

Figure 1

8 pages, 402 KiB

Open AccessProceeding Paper

Closing the Loop: Achieving a Sustainable Future for Plastics Through Eco-Design and Recycling

by Youssef Moujoud, Hafida Bouloiz and Maryam Gallab

Eng. Proc. 2025, 97(1), 3; https://doi.org/10.3390/engproc2025097003 - 6 Jun 2025

Abstract

The global plastic pollution crisis demands a shift to a circular economy focused on sustainability and resource efficiency. This study examines the current linear approach to plastic use, which depends heavily on fossil fuels and waste materials and often leads to poor waste management. We highlight the importance of eco-design and Design for Recycling (DfR) as key solutions. These approaches aim to make plastic products easier to recycle, extend their usability, and support a more circular system, while also creating new opportunities in the recycling sector. Achieving this shift requires cooperation between stakeholders, including industry, government, and the public. Supportive policies and the development of new technologies are also crucial. By addressing challenges like complex product designs and limited recycling infrastructure, this study offers practical ways to reduce the environmental and economic impacts of plastic use. Full article

► Show Figures

Figure 1

12 pages, 530 KiB

Open AccessProceeding Paper

Corporate Social Responsibility Commitment: Is Regulatory Pressure a Necessary Driver?

by El Aziz Ouissal and Asdiou Abdelkarim

Eng. Proc. 2025, 97(1), 4; https://doi.org/10.3390/engproc2025097004 - 6 Jun 2025

Abstract

Corporate social responsibility and its communication are practices that have been developed for several years in developed and emerging countries, which have aroused great interest around the world and particularly in Morocco. Therefore, our main objective is to study the impact of ESG disclosure imposed by the market regulator on the extent of corporate commitment. This paper aims to understand one of the factors explaining the CSR commitment of Moroccan companies, namely the regulatory environment. Its main strength lies in the in-depth examination of the current regulatory environment and its real impact on corporate social responsibility strategies and actions. To answer our research question, we conducted an empirical study based on a single hypothesis tested through secondary data processing. The results show that engagement, under institutional pressure, has a positive impact on the annual publication of ESG reports on the CSR engagement of Moroccan companies. Full article

► Show Figures

Journal Menu

Journal Browser

Eng. Proc., 2025, SMILE 2025

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI