Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm

Mariusz Izdebski

doi:10.5604/01.3001.0053.7463

Authors

Mariusz Izdebski Faculty of Transport, Warsaw University of Technology, Warsaw Author https://orcid.org/0000-0002-9157-7870

DOI:

https://doi.org/10.5604/01.3001.0053.7463

Keywords:

transport companies, allocation of vehicles, organization of transport, risk management, heuristic algorithm, ant algorithm, optimization

Abstract

The work deals with the issue of assigning vehicles to tasks in transport companies, taking into account the minimization of the risk of dangerous events on the route of vehicles performing the assigned transport tasks. The proposed risk management procedure based on a heuristic algorithm reduces the risk to a minimum. The ant algorithm reduces it in the event of exceeding the limit, which differs from the classic methods of risk management, which are dedicated only to risk assessment. A decision model has been developed for risk management. The decision model considers the limitations typical of the classic model of assigning vehicles to tasks, e.g. window limits and additionally contains limitations on the acceptable risk on the route of vehicles' travel. The criterion function minimizes the probability of an accident occurring along the entire assignment route. The probability of the occurrence of dangerous events on the routes of vehicles was determined based on known theoretical distributions. The random variable of the distributions was defined as the moment of the vehicle's appearance at a given route point. Theoretical probability distributions were determined based on empirical data using the STATISTICA 13 package. The decision model takes into account such constraints as the time of task completion and limiting the acceptable risk. The criterion function minimizes the probability of dangerous events occurring in the routes of vehicles. The ant algorithm has been validated on accurate input data. The proposed ant algorithm was 95% effective in assessing the risk of adverse events in assigning vehicles to tasks. The algorithm was run 100 times. The designated routes were compared with the actual hours of the accident at the bottom of the measurement points. The graphical interpretation of the results is shown in the PTV Visum software. Verification of the algorithm confirmed its effectiveness. The work presents the process of building the algorithm along with its calibration.

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