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

Regular Issues

• Open Access

  Automated simulation testing for complex software environments using multi-agent reinforcement learning ( Free Full-text Access )

  by Qiuming Zhang, Jing Luo 
  Abstract: The growing complexity of software systems poses major challenges for automated testing in continuous integration and continuous delivery pipelines. This paper proposes multi-agent-based software automated testing, a multi-agent reinforcement learning framework that models testing tasks as a decentralized partially observable Markov decision process. Using the Q-network Mixing algorithm, the system enables coordinated decisions across testing agents. Evaluation on a TravisTorrent-based simulation environment shows multi-agent-based software automated testing achieves a 95.2% defect detection rate - showing a 4.7% improvement over the best multi-agent baseline - while reducing test execution time to 70% of conventional rule-based scheduling. Compared with multi-agent deep deterministic policy gradient, it demonstrates a large effect size (Cohen's d=0.89) in defect detection. These results demonstrate the framework's effectiveness in improving testing quality and efficiency, offering a viable solution for intelligent test automation in complex software environments.
  Keywords: multi-agent reinforcement learning; automated testing; continuous integration and continuous delivery; Jenkins.
  DOI: 10.1504/IJSPM.2025.10075279
   
  

• Open Access

  Discrete-event simulation modelling of inventory turnover under supply chain financial collaboration ( Free Full-text Access )

  by Zhifeng Qu 
  Abstract: This study addresses the critical gap between operational and financial objectives in supply chain inventory management by proposing a novel framework that integrates discrete-event simulation with deep reinforcement learning. We formulate a dual-objective reward function incorporating both traditional costs (holding, shortage, ordering) and the financial metric of cash conversion cycle. Trained and tested on the real-world M5 dataset, our model, cognitive load dynamic assessment model-proximal policy optimization, demonstrates superior performance. Results show it achieves a total cost of 285.4
  Keywords: supply chain finance; deep reinforcement learning; inventory optimization; cash conversion cycle; M5 dataset.
  DOI: 10.1504/IJSPM.2025.10075344
   
  

• Open Access

  Multimodal transformer-driven consistent environment design generation simulation modelling ( Free Full-text Access )

  by Zhuo Fan, Jinqi Wang 
  Abstract: Automated generation of physically plausible 3D environments is a key challenge in digital twins, the metaverse, and robot simulation. Current methods focus mainly on visual fidelity, often overlooking functional and physical rationality, limiting direct applicability to simulation tasks. To address this, we propose a multimodal Transformer-based framework for environment design generation and consistency simulation. Utilizing a cross-modal attention mechanism, our model integrates textual descriptions with prior knowledge from real 3D scenes. It incorporates fine-grained physical constraint losses including collision avoidance, support relations, and spatial accessibility optimisation during training to explicitly model physical consistency. Experiments on the Matterport3D dataset show our method outperforms existing baselines in visual quality and layout rationality. Notably, it shows significant gains in physical consistency: collision volume is greatly reduced, and navigation success reaches 89%, affirming high simulability and practicality of the generated environments.
  Keywords: multimodal transformer; environment design generation; physical consistency; simulation modeling; microphysically constrainable.
  DOI: 10.1504/IJSPM.2026.10075345
   
  
• 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