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

• Autorzy: Izdebski Mariusz

Identyfikatory

DOI

10.5604/01.3001.0053.7463

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

PL

firmy transportowe; przydział pojazdów; organizacja transportu; zarządzanie ryzykiem; algorytm heurystyczny; algorytm mrówkowy; optymalizacja

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2023
• Tom: Vol. 67, iss. 3
• Strony: 139--153
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Izdebski Mariusz

• Faculty of Transport, Warsaw University of Technology, Warsaw, Poland

• https://orcid.org/0000-0002-9157-7870

Bibliografia

• [1] Agrawal, A. K., Yadav, S., Gupta A.A., & Pandey, S. (2022). A genetic algorithm model for optimizing vehicle routing problems with perishable products under time-window and quality requirements. Decision Analytics Journal, 5, 100139. https://doi.org/10.1016/j.dajour.2022.100139
• [2] Batarliene, N. (2020). Essential Safety Factors for the Transport of Dangerous Goods by Road: A Case Study of Lithuania. Sustainability, 12(12), 4954. https://doi.org/10.3390/su12124954.
• [3] Dhouib, S. (2022). An Intelligent Assignment Problem Using Novel Heuristic: The Dhouib-Matrix-AP1 (DM-AP1): Novel Method for Assignment Problem. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 135-141. https://doi.org/10.18201/ijisae.2022.277.
• [4] Ebrahim, S., Milon, I., & Quazi S.H. (2021). A review on neural network techniques for the prediction of road traffic accident severity. Asian Transport Studies, 7, 100040. https://doi.org/10.1016/j.eastsj.2021.100040.
• [5] Fornalchyk, Y., Afonin, M., Postranskyy, T., & Boikiv, M. (2021). Risk assessment during the transportation of dangerous goods considering the functional state of the driver. Transport Problems, 16(1), 139-152. https://doi.org/10.21307/tp-2021-012 .
• [6] Fuentes, M., Cadarso, L., Vaze, V., & Barnhart, C. (2021). The Tail Assignment Problem: A Case Study at Vueling Airlines. Transportation Research Procedia, 52, 445-452. https://doi.org/10.1016/j.trpro.2021.01.052
• [7] Giovanni, C., Giuseppe, I., Michela, L.P., Pluchino, A., & Ignaccolo, M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transportation Research Procedia, 45, 234-241. https://doi.org/10.1016/j.trpro.2020.03.012
• [8] Haixing, W., & Qiangian, L. (2020). Risk Analysis and Route Optimization of Dangerous Goods Transportation Based on the Empirical Path Set. Journal of Advanced Transportation, 5(1), 1-13. https://doi.org/10.1155/2020/8838692
• [9] Holeczek, N. (2019). Hazardous materials truck transportation problems: A classification and state of the art literature review. Transportation Research Part D: Transport and Environment, 69, 305-328. https://doi.org/10.1016/j.trd.2019.02.010.
• [10] Hossaina, M., Abdel-Atyb, M., Quddusc M.A., Muromachid, Y., & Sadeeke, S.N. (2019). Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements. Accident Analysis and Prevention, 124, 66-84. https://doi.org/10.1016/j.aap.2018.12.022
• [11] Hosseini, S.D., & Verma M. (2021). Equitable routing of rail hazardous materials shipments using CVaR methodology. Computers & Operations Research, 129, 105222. https://doi.org/10.1016/j.cor.2021.105222
• [12] Huang, W., Li, L., Liu, H., Zhang, R., & Xu, M. (2021). Defense resource allocation in road dangerous goods transportation network: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach. Reliability Engineering & System Safety, 215, 107899. https://doi.org/10.1016/j.ress.2021.107899.
• [13] Izdebski, M. , Gołda, P., & Zawisza T. (2023). The Use of Simulation Tools to Minimize the Risk of Dangerous Events on the Airport Apron. W: Advanced Solutions and Practical Applications in Road Traffic Engineering : conference proceedings / Macioszek Elżbieta, Granà Anna, Sierpiński Grzegorz (red.), Lecture Notes in Networks and Systems, 604, Springer, 91-107, ISBN 978-3-031-22358-7.
• [14] Ji, X., & Yong, X. (2019). Application of Genetic Algorithm in Logistics Path Optimization. Academic Journal of Computing & Information Science, 2, 155-161. https://doi.org/10.25236/AJCIS.010030.
• [15] Jia, Z., Yu, J., Ai, X., Xu, X., & Yang, D. (2018). Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm. Aerospace Science and Technology, 76, 112-125. https://doi.org/10.1016/j.ast.2018.01.025
• [16] Karsu, Ö., Azizoğlu, M., & Alanl, K. (2021). Exact and Heuristic Solution Approaches for the Airport Gate Assignment Problem. Omega, 103, 102422. https://doi.org/10.1016/j.omega.2021.102422.
• [17] Kukulski, J., Lewczuk, K., Góra, I., & Wasiak, M. (2023). Methodological aspects of risk map-ping in multimode transport systems. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 25(1), 19. https://doi.org/10.17531/ein.2023.1.19.
• [18] Lyu, Z., & Andrew, J.Y. (2021). Consultant Assignment and Routing Problem With Priority Matching. Computers & Industrial Engineering, 151(11), 106921. https://doi.org/10.1016/j.cie.2020.106921.
• [19] Mahdi, R., Amirarsalan, Mehrara, M., & Khled, K. (2020). Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. International Journal of Transportation Science and Technology, 9(2), 89-99. https://doi.org/10.1016/j.ijtst.2019.10.002.
• [20] Mohri, S.S., Mohammadi M., Gendreau M., Pirayesh M., Ghasemaghaei A., & Salehi V. (2022). Hazardous material transportation problems: A comprehensive overview of models and solution approaches. European Journal of Operational Research, 302(1),1, 1-38. https://doi.org/10.1016/j.ejor.2021.11.045.
• [21] Mokhtarimousavi, S., Anderson, J.C., Azizinamini A., & Hadi, M. (2020). Factors affecting injury severity in vehicle-pedestrian crashes: A day-of-week analysis using random parameter ordered response models and Artificial Neural Networks. International Journal of Transportation Science and Technolog, 9(2), 100-115. https://doi.org/10.1016/j.ijtst.2020.01.001.
• [22] Mujalli, R.O., Al-Masaeid, H. & Alamoush, S. (2023). Modeling Traffic Crashes on Rural and Suburban Highways Using Ensemble Machine Learning Methods. KSCE Journal of Civil Engineering, 27, 814-825. https://doi.org/10.1007/s12205-022-0658-4.
• [23] Munapo, E. (2020). Development of an accelerating Hungarian method for assignment problems. Eastern-European Journal of Enterprise Technologies, 4(4), 6-13. https://doi.org/10.15587/1729-4061.2020.209172.
• [24] Ongcunaruk, W., Ongkunaruk, P., & Janssens, G.K. (2021). Genetic algorithm for a delivery problem with mixed time windows. Computers & Industrial Engineering, 159(1), 107478. https://doi.org/10.1016/j.cie.2021.107478
• [25] Semenov, I., & Jacyna, M. (2022). The synthesis model as a planning tool for effective supply chains resistant to adverse events. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 24 (1), 140-152. https://doi.org/10.17531/ein.2022.1.16
• [26] Stojanovic, N., Boskovic B., Petrovic M., Grujic I., & Abdullah, O.I. (2023). The impact of accidents during the transport of dangerous good, on people, the environment, and infrastructure and measures for their reduction: a review. Environmental Science and Pollution Research, 30, 32288-32300. https://doi.org/10.1007/s11356-023-25470-2
• [27] Szaciłło, L., Jacyna, M., Szczepański, E., & Izdebski, M. (2021). Risk assessment for rail freight transport operations, Eksploatacja i Niezawodność, Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne, 23(3), 476-488. https://doi.org/10.17531/ein.2021.3.8.
• [28] Tian, Q., Li, J., Huang, G., & Yuan, W. (2022). Solving an airport ground service task assignment problem with an exact algorithm. PLoS ONE, 17(12): e0279131. https://doi.org/10.1371/journal.pone.0279131.
• [29] Timajchi, A., Al-e-Hashem, S.M.M., & Rekik Y. (2019). Inventory routing problem for hazardous and deteriorating items in the presence of accident risk with transshipment option. International Journal of Production Economics, 209, 302-315. https://doi.org/10.1016/j.ijpe.2018.01.018.
• [30] Wei, Q., Yan-Ning, S., Zi-Long, Z., Zhi-Yao, L., & Yao-Ming Z. (2021). Multiagent reinforcement learning-based dynamic task assignment for vehicles in urban transportation system. International Journal of Production Economics, 240, 108251. https://doi.org/10.1016/j.ijpe.2021.108251
• [31] Yu, J., Zhitao H., Zhenyu L., & Honghai Z. (2023). Optimization of multi-objective airport gate assignment problem: considering fairness between airlines. Transportmetrica B: Transport Dynamics, 11(1), 196-210.https://doi.org/10.1080/21680566.2022.2056542
• [32] Zabielska, A, Jacyna, M., Lasota, M., & Nehring. K. (2023). Evaluation of the efficiency of the delivery process in the technical object of transport infrastructure with the application of a simulation model. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 25(1), 1. https://doi.org/10.17531/ein.2023.1.1

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)