Scientia Iranica

Scientia Iranica

A novel fuzzy bi-objective vehicle routing and scheduling problem with time window constraint for a distribution system: A case study

Document Type : Research Article

Authors
A. Esmaeilidouki 1
M. Mahzouni-Sani 2
A. Nikhalat Jahromi 3
F. Jolai 4
1 Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
2 Department of Electrical Engineering, Urmia University, Urmia, Iran
3 Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
4 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
10.24200/sci.2020.52461.2727
Abstract
In the process of hazardous material transportation, the risk is a significant factor that should be considered due to the potential severe consequence of an incident. Regardless of risks, time is a paramount concern that should be considered in hazardous material transportation. In this way, this paper introduces a bi-objective model for a vehicle routing and scheduling problem of hazardous material distribution problems under the fuzzy condition to minimize both total distribution time and risks. In the proposed model, the fuzzy inference system and fuzzy failure mode and effects analysis are applied to identify and calculate the high-level risks instead of the previous simple methods for the first time. Moreover, Jimenez method and fuzzy goal programming are respectively utilized to convert the fuzzy bi-objective model into the same crisp and single-objective one. Besides, to cope with the NP-hardness of the large-sized problems, two meta-heuristic algorithms namely invasive weeds optimization and genetic algorithm is used, and several sensitivity analyses are performed to prove the efficiency of the proposed approach. The performance of the proposed algorithms is also assessed through a comparative study. Finally, the proposed model is implemented to a real case study to prove the validity of the model.
Keywords
Hazardous material distribution problem
Vehicle routing and scheduling
fuzzy inference system and fuzzy failure mode and effects analysis
Time window constraint
Fuzzy goal programming

  • References

    • Tarantilis, C.D. and Kiranoudis, C.T. “Using the vehicle routing problem for the transportation of hazardous materials”, Operational Research, 1(1), p.67 (2001).
    • Zografos, K. G., & Androutsopoulos, K. N. “A decision support system for integrated hazardous materials routing and emergency response decisions”, Transportation Research Part C: Emerging Technologies, 16(6), 684-703 (2008).
    • Androutsopoulos, K. N., & Zografos, K. G. “Solving the bicriterion routing and scheduling problem for hazardous materials distribution”, Transportation Research Part C: Emerging Technologies, 18(5), 713-726 (2010).
    • Jalili Ball, E., Tavakoli Moghaddam, R., Javanshir, H. “Development of Multi Objective mathematical model of vehicle routing problem for transporting fuels by considering time windows and the environmental factors”, Accepted paper in Journal of Transportation Engineering (2015).
    • Pradhananga, R., Taniguchi, E., & Yamada, T. “Ant colony system based routing and scheduling for hazardous material transportation”, Procedia-Social and Behavioral Sciences, 2(3), 6097-6108 (2010).
    • Androutsopoulos, K. N., & Zografos, K. G. “A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution”, EURO Journal on Transportation and Logistics, 1(1-2), 157-183 (2012).
    • Qureshi, A. G., Taniguchi, E., & Yamada, T. “A microsimulation based analysis of exact solution of dynamic vehicle routing with soft time windows”, Procedia-Social and Behavioral Sciences, 39, 205-216 (2012).
    • Erkut, E., Tjandra, S. A., & Verter, V. “Hazardous materials transportation”, Handbooks in operations research and management science, 14, 539-621 (2007).
    • Bonvicini, S., Leonelli, P., & Spadoni, G. “Risk analysis of hazardous materials transportation: evaluating uncertainty by means of fuzzy logic”, Journal of Hazardous Materials, 62(1), 59-74 (1998).
    • Aksen, D., Kaya, O., Salman, F. S., & Akça, Y. “Selective and periodic inventory routing problem for waste vegetable oil collection”, Optimization Letters, 6(6), 1063-1080 (2012).
    • Kassem, S., & Chen, M. “Solving reverse logistics vehicle routing problems with time windows”, The International Journal of Advanced Manufacturing Technology, 68(1-4), 57-68 (2013).
    • Toumazis, I., & Kwon, C., “Routing hazardous materials on time-dependent networks using conditional value-at-risk”. Transportation Research Part C: Emerging Technologies, 37, 73-92 (2013).
    • Demirel, N., Özceylan, E., Paksoy, T., & Gökçen, H. “A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives”, International Journal of Production Research, 52(12), 3637-3664 (2014).
    • Saat, M. R., Werth, C. J., Schaeffer, D., Yoon, H., & Barkan, C. P. “Environmental risk analysis of hazardous material rail transportation”, Journal of hazardous materials, 264, 560-569 (2014)
    • Ardjmand, E., Weckman, G., Park, N., Taherkhani, P., & Singh, M. “Applying genetic algorithm to a new location and routing model of hazardous materials”, International Journal of Production Research, 53(3), 916-928 (2015).
    • Zohal, M., & Soleimani, H. “Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry”, Journal of Cleaner Production, 133, 314-337 (2016).
    • Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S., “A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry”, Journal of Cleaner Production, 113, 662-673 (2016).
    • Iassinovskaia, G., Limbourg, S., & Riane, F. “The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains”, International Journal of Production Economics, 183, 570-582 (2017).
    • Pamučar, D., Ljubojević, S., Kostadinović, D., & Đorović, B. "Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach”, Expert Systems with Applications, 65, 1-15 (2016).
    • Paydar, M. M., Babaveisi, V., & Safaei, A. S. “An engine oil closed-loop supply chain design considering collection risk”, Computers & Chemical Engineering, 104, 38-55 (2017).
    • Rayas, V. M., & Serrato, M. A. “A framework of the risk assessment for the supply chain of hazardous materials”, NETNOMICS: Economic Research and Electronic Networking, 18(2-3), 215-226 (2017).
    • Du, J., Li, X., Yu, L., Dan, R., & Zhou, J. “Multi-depot vehicle routing problem for hazardous materials transportation: a fuzzy bilevel programming”, Information Sciences, 399, 201-218 (2017).
    • Talarico, L., Springael, J., Sörensen, K., & Talarico, F. “A large neighbourhood metaheuristic for the risk-constrained cash-in-transit vehicle routing problem”, Computers & Operations Research, 78, 547-556 (2017).
    • Paredes-Belmar, G., Bronfman, A., Marianov, V., & Latorre-Núñez, G. “Hazardous materials collection with multiple-product loading”, Journal of cleaner production, 141, 909-919 (2017).
    • Wang, N., Zhang, M., Che, A., & Jiang, B. “Bi-Objective Vehicle Routing for Hazardous Materials Transportation with No Vehicles Travelling in Echelon”, IEEE Transactions on Intelligent Transportation Systems, 19(6), 1867-1879 (2018).
    • Rabbani, M., Heidari, R., Farrokhi-Asl, H., & Rahimi, N. “Using metaheuristic algorithms to solve a multi-objective industrial hazardous waste location-routing problem considering incompatible waste types”, Journal of Cleaner Production, 170, 227-241 (2018).
    • Timajchi, A., Al-e-Hashem, S. M. M., & Rekik, Y. “Inventory routing problem for hazardous and deteriorating items in the presence of accident risk with transshipment option”. International Journal of Production Economics (2018).
    • Bula, G. A., Afsar, H. M., González, F. A., Prodhon, C., & Velasco, N. “Bi-objective vehicle routing problem for hazardous materials transportation”, Journal of Cleaner Production, 206, 976-986 (2018).
    • Ma, C., Hao, W., Pan, F., & Xiang, W. “ Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm”, PloS one, 13(6), e0198931(2018).
    • Ma, C., Mao, B., Xu, Q., Hua, G., Zhang, S., & Zhang, T. (2018). Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation. Sustainability, 10(10), 3519.
    • Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
    • Yaghoobi, M. A., & Tamiz, M. (2007). A method for solving fuzzy goal programming problems based on MINMAX approach. European Journal of Operational Research, 177(3), 1580-1590.
    • Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433.
    • Rabbani, M., Rahimi, Y., Kazemi, S. M., & Samavati, M. (2014). Satisfying the energy demand of a rural area by considering the investment on renewable energy alternatives and depreciation costs. Journal of Renewable Energy, 2014.
    • Belmokaddem, M., Mekidiche, M., & Sahed, A. (2009). Application of a fuzzy goal programming approach with different importance and priorities to aggregate production planning. Journal of applied quantitative methods, 4(3).
    • Zimmermann, H. J. (1985). Applications of fuzzy set theory to mathematical programming. Information sciences, 36(1-2), 29-58.
    • Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.
Scientia Iranica
Volume 28, Issue 5
Transactions on Industrial Engineering (E)
September and October 2021
Pages 2868-2889

  • Receive Date 13 December 2018
  • Revise Date 13 October 2019
  • Accept Date 18 February 2020