International Journal of Simulation and Process Modelling (4 papers in press)

Regular Issues

• Open Access

  Deep learning-driven simulation modelling for mine geological risk assessment integrating multi-source data ( Free Full-text Access )

  by Fuming Zhao, Chao Xie 
  Abstract: Mining geological risk assessment is crucial for ensuring production safety, yet traditional methods relying on single data sources and expert experience suffer from low accuracy and delayed early warning, often failing to capture dynamic risk characteristics. This paper proposes a simulation-driven intelligent assessment framework that integrates multi-source monitoring data including geological, subsidence, hydrological, and microseismic information - with advanced deep learning techniques. The framework is designed to dynamically simulate risk evolution processes and construct an end-to-end predictive model. Validation on public datasets demonstrates an accuracy of 91.3%, significantly surpassing the 78.5% achieved by traditional methods (p < 0.01), with high stability and generalisation ability. This process-modelling paradigm effectively overcomes the bottlenecks of information incompleteness and response delay, providing reliable technical support for geological hazard prevention and a foundational tool for intelligent mine construction, thereby supporting dynamic safety management.
  Keywords: multi-source data fusion; deep learning; mine geological hazards; intelligent assessment.
  DOI: 10.1504/IJSPM.2026.10076608
   
  

• Open Access

  Deep learning and multi-agent simulation for spatiotemporal inventory management in e-commerce ( Free Full-text Access )

  by Haibo Zhu, Hongmei Wu 
  Abstract: E-commerce inventory management faces the dual challenge of handling complex spatiotemporal demand variations and coordinating decisions across distributed warehouse networks. To address this, we propose an integrated simulation framework that combines deep learning for demand forecasting with multi-agent reinforcement learning for inventory optimisation. Our approach employs a spatiotemporal graph network to capture demand dependencies and a value-decomposition network for collaborative decision-making. Validated on the real-world M5 dataset, the framework demonstrates significant improvements in both forecast accuracy and operational efficiency compared to traditional and state-of-the-art methods. Notably, it achieves a substantial reduction in total costs while maintaining high service levels. This work contributes a novel, scalable solution for intelligent inventory management, with direct implications for enhancing supply chain resilience in e-commerce.
  Keywords: inventory management; deep reinforcement learning; DRL; spatiotemporal forecasting; multi-agent systems; simulation optimisation.
  DOI: 10.1504/IJSPM.2026.10077092
   
  
• Simulation-driven deep learning framework for early poultry disease detection using faecal image classification with optimised CNN architectures  
  by Nonita Sharma, Monika Mangla, Manik Rakhra, Baljinder Kaur, Raj Kumar Mohanta, Bijay Kumar Paikaray 
  Abstract: The present investigation aims to devise an optimized Convolutional Neural Network (CNN) framework to identify prominent poultry diseases based on faecal images. The proposed research model uses a tri-convolutional layer architecture to enhance the accuracy of poultry disease classification and improve feature extraction. Three major diseases, namely coccidiosis, salmonella, and Newcastle, have been considered. The prime objective of current research is to achieve early detection by employing advanced deep-learning techniques. The proposed model uses fecal images to identify pathological conditions using the TriConvLayer architecture accurately. In this work, custom CNN models, viz. SoloConvLayer, TriConvLayer, and FiveConvLayer models are used to achieve an accuracy of 97%, 98%, and 98%, respectively. The achieved result advocates the efficacy of the proposed approach. It thus has the potential to revolutionize early disease detection in poultry farming, a major step towards improving animal health and farm productivity.
  Keywords: deep learning; poultry disease detection; convolutional neural networks; CNN; faecal image analysis; poultry health monitoring; disease classification; simulation framework.
  DOI: 10.1504/IJSPM.2025.10073525
   
  
• Process-oriented simulation and optimisation of 2D trabecular bone via fractal modelling  
  by Liu Yuhong, J.I.A. Liyan, Wang Zheng, Jincai Chang 
  Abstract: A process-oriented method for simulating and reconstructing 2D trabecular bone microstructures is proposed, based on Fractional Brownian Motion (FBM) and multi-parameter morphological con-straints. By adjusting the Hurst exponent, the Fractal Dimensions(FD) of trabecular structures can be controlled, enabling the reconstruction of biomimetic porous geometries with tunable porosity and thickness. A complete modelling framework is developed, including image preprocessing, pa-rameter extraction, simulation generation, and post-optimisation, and is validated using real femoral cross-sectional images. Comparative results show that this method outperforms existing Graph Convolutional Networks GCN-based approaches in preserving topological fidelity and geometric fidelity. This study provides a novel, simulation-driven pathway for customizable bone scaffold design and process modelling in bioengineering.
  Keywords: fractal modelling; trabecular bone structure; 2D geometric reconstruction; parameter-driven simulation; morphological optimisation.
  DOI: 10.1504/IJSPM.2025.10076820