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Optimizing Strategic Placement of Railroad Accident Relief Equipment: A Simulation-Based Decision Tool
This study presents a simulation based method for the comparative evaluation of solutions for location of relief equipment on railway networks—a domain that has been notably neglected in scholarly exploration. To fill this gap a comprehensive simulation framework is introduced that utilizes mathematical modelling and optimization techniques to evaluate the performance of the location solution in different real time circumstances over a long-time horizon. This transportation model considers the demand-supply problem for each instance of accident on a railway network. The model integrates constraints mirroring the practical and operational restrictions associated with moving relief equipment within the network. Application of this transportation model is demonstrated with historical data of accidents and the demand of equipment during each scenario, presenting a practical case study to validate the proposed methodology. Computational experiments are conducted to compare three existing location solutions available in the literature. These location solutions are critically examined to assess their efficacy in addressing the challenges of relief facility placement within a railway network along with analysis of their strengths and weaknesses. It is noted that there is approximately 1.5% less cost of attention and 60% less penalty in the case of the solution obtained through multi-objective problem when compared with two other solutions obtained through ‘Set- Covering Model’ and ‘Existing locations’ adopted by the railways considered in the study. By using real-life scenarios, advanced simulation techniques, and comparative analysis of existing solutions, this work not only addresses the current gap in academic research but also sets the stage for further advancements in the optimization of relief operations within complex railway networks.
Keywords
Train accidents, Relief-facilities, Monte carlo simulation, Transportation, Optimization
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