Forthcoming and Online First Articles

International Journal of Simulation and Process Modelling

International Journal of Simulation and Process Modelling (IJSPM)

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International Journal of Simulation and Process Modelling (8 papers in press)

Regular Issues

  • The experimental study of logging residue stock on logging sites following clear-cutting using a sorting machine system   Order a copy of this article
    by Igor Grigorev, Michael Zyryanov, Sergey Medvedev, Aleksander Mokhirev, Sergey Egipko, Pavel Perfiliev, Irina Savvateeva 
    Abstract: The study aimed to evaluate stand structure, timber volume, and logging residues following sorting felling, explicitly focusing on the predominant species, pine. A comprehensive inventory of stumps was conducted within the designated sample plot. The average diameter of pine trees measured 21.8 cm. The wood volume in pine bark was calculated to be 1,068.9 m3, with the wood volume increasing with increasing thickness steps. The wood stock at the logging site mostly appeared to be pine, making it the dominant species. The discovery of a sizeable quantity of logging residues, accounting for 11.3% of the total stem wood volume, is an essential outcome of the study. The findings have practical implications for optimising forest management, preserving ecological equilibrium, and sustaining sustainable regional forestry development.
    Keywords: forest industry; forest management; forestry; logging residues; stock distribution; sustainable development.
    DOI: 10.1504/IJSPM.2023.10062370
  • Intelligent optimisation scheduling of raw material area in sausage production line   Order a copy of this article
    by Zhonghua Han, Guan Mingpeng, Liu Hangyu, Chang Daliang, Liangliang Sun 
    Abstract: The production process in the raw material area of the sausage production line needs to be further optimised to improve production efficiency and reduce the waiting time for emulsifying fillings suck. In this study, we propose an improved Q-learning method for scheduling in raw material area (RMSPS-IQL). To overcome the problem of low learning efficiency caused by multi-operation and multi-tasking in the production line scheduling process, a priority stranding probability is added to the action selection method based on the epsilon-greedy strategy. In addition, to determine the execution time of the action during continuous working, a stranding action starting control method based on the probability of filling deterioration was designed. Comparative analysis of the results of multiple simulation schemes suggested that the RMSPS-IQL effectively reduces the waiting time for suctioning fillings from emulsifying pot, and improves the productivity of the production line.
    Keywords: sausage production line; raw material area; intelligent scheduling; probability of filling spoilage; Q-learning.
    DOI: 10.1504/IJSPM.2023.10062416
  • Impact of supplier concentration on low-carbon innovation investment in heavily polluting manufacturing firms: moderating role of cash holdings   Order a copy of this article
    by Qiang Meng, Nartraphee Tancho, Kusuma Dampitakse, Yu Chen 
    Abstract: It is particularly important to promote low-carbon innovation investment of heavily polluting manufacturing firms through the supplier concentration. A pivotal factor in this relationship is the cash holdings of these entities. This study employs a multiple linear regression analysis to scrutinise the influence of supplier concentration on low-carbon innovative investment in heavily polluting manufacturing firms, while concurrently examining the moderating impact of cash holdings. The results indicate an inverted U-shaped relationship between supplier concentration and low-carbon innovation investment among heavily polluting manufacturing firms in China. Prior to reaching a critical threshold, there is an enhancement in low-carbon innovative investment concomitant with an escalation in supplier concentration. However, beyond this point, there is a subsequent, gradual diminution. Cash holdings seem to have narrowed the range of this U-shaped curve, indicating its moderating effect. Specifically, it has led to a rapid increase in low-carbon innovative investment on the left and accelerates the downward trend on the right side.
    Keywords: supplier concentration; low-carbon innovation investment; cash holdings; U-shaped relationship; heavily polluting manufacturing firms.
    DOI: 10.1504/IJSPM.2023.10062568
  • Simulating the effects of access improvement strategies in an outpatient memory clinic with high follow-up volumes   Order a copy of this article
    by Esmaeil Bahalkeh, Tze C. Chiam, Y. Yih, James M. Ellison 
    Abstract: Demand for treatment services related to neurocognitive disorders such as dementia is growing due to the aging of our population, increased life expectancy, and the high prevalence of cognitive symptoms. These services are often provided by outpatient memory clinics. In the studied outpatient memory clinic, average monthly demand, and average patient wait times to receive their first evaluation increased by five folds and three folds between 2011 and 2017. In this paper, we investigated clinics operations and identified overbooking, repatriation referring stable follow-up patients from specialty care to primary care and increasing provider slots as potential strategies to improve access and long wait time issues. We then evaluated their effectiveness using an empirical simulation-optimisation model. Our results suggest that despite increasing wait times in the clinic, increasing provider slots is not always an effective strategy. In fact, overbooking and repatriation can result in more significant performance improvements.
    Keywords: wait time; access; outpatient memory clinic; overbooking; discrete event simulation; repatriation.
    DOI: 10.1504/IJSPM.2024.10062833
  • Numerical simulation and GPU computing for the 2D wave equation with variable coefficient   Order a copy of this article
    by Arshyn Altybay, Dauren Darkenbayev, Nurbapa Mekebayev 
    Abstract: In this paper, we present parallel numerical implementations of a 2D wave equation with a variable coefficient on GPU. We considered wave propagation simulations in shallow water areas caused by underwater movement and performed some numerical simulations at different time steps. The sequential algorithm is based on the implicit finite difference scheme and the parallel cyclic reduction (PCR) method. The parallel code was developed using CUDA technology and tested on different domain sizes. Performance analysis showed that our parallel approach showed a good speedup compared to sequential CPU code. The proposed parallel visualization simulator can be served as a good tool for numerous water management systems in engineering practices.
    Keywords: numerical simulation; GPU; CUDA technology; wave equation; parallel computing.
    DOI: 10.1504/IJSPM.2024.10062867
  • Artificial bee colony assisted pipe auto-routing in the built environment   Order a copy of this article
    by Changtao Wang, Yiming Zhang, Dan Shan, Baolong Yuan 
    Abstract: In addressing challenges such as numerous pipe elbows, prolonged layout time, and unsuitable position laying in pipe auto-routing inbuilt environment (PABE), this study introduces an artificial bee colony (ABC) to solve the typical NP-hard optimisation problems. The intermediate point method is employed for initial route generation, aiming to expedite the process. To enhance the adaptability of the layout to the actual environment and mitigate the local optima limitation of the conventional artificial bee colony algorithm, the Levy flight method and population co-evolution are integrated into the improved algorithm for multi-pipe automatic layout. Simulation results demonstrate that the proposed probabilistic selection-based initial route generation, coupled with the enhanced artificial bee colony algorithm, effectively resolves issues in PABE, aligning with design requirements for PABE.
    Keywords: pipe auto-routing; PABE; artificial bee colony; ABC; Levy flight; co-evolution; multi-pipe automatic layout; loop-free operation; probabilistic selection.
    DOI: 10.1504/IJSPM.2024.10062881
  • A novel variable drive modelling approach for general framework of chemical production scheduling   Order a copy of this article
    by Yuandong Chen, Jinhao Pang, Zhen Jiang, Yuchen Gou, Dewang Chen 
    Abstract: In this paper, we present a modelling approach named as variable drive modelling (VDM) to develop general modelling framework of chemical production scheduling. VDM builds model from variable (i.e., variable-based), while the traditional method building model from rule aspect (i.e., rule-based) with constraint blocks such as allocation constraints, mass balance, capacity constraints and operation rule constraints. In this paper, we analyse three shortcomings of the traditional rule-based model building method. A novel scheduling time axis is presented, while the confusing position of variables at time axis can be avoided. A systematic method to define variables and description rules of driving functions are given. At last, a crude oil scheduling case was given out to illustrate how to implement this approach. The results show less variable numbers, less constraints, less branching nodes, and more less solution time of the proposed approach against the existing model.
    Keywords: simulation and modelling; scheduling; variable drive modelling; VDM; chemical processes; refinery.
    DOI: 10.1504/IJSPM.2024.10063225
  • A fusion model of gated recurrent unit and convolutional neural network for online ride-hailing demand forecasting   Order a copy of this article
    by Xijin Cui, Mingxia Huang, Lei Shi 
    Abstract: This paper collects and analyses the impact of weather, air quality and point of interest data on residents’ daily travel, establishes a fusion model combined the convolutional neural network based on point of interest data and gated recurrent neural network prediction model to investigate the influence of weather and air quality on the demand of online ride-hailing, uses Pearson correlation coefficient to calculate the correlation between various external factors and ride-hailing order data. Analyse the important factors affecting ride-hailing order volume through correlation analysis. In order to improve the stability of the network, a residual module is added. The results show that the models constructed in this paper has good prediction accuracy. The study shows the incorporation of multi-source data can effectively improve the prediction accuracy of the online ride-hailing prediction model.
    Keywords: online ride-hailing demand; gated recurrent unit; GRU; convolutional neural network; CNN; travel demand.
    DOI: 10.1504/IJSPM.2024.10063423