Z -trapezoidal risk assessment for multi-objective Hazmat routing model with time windows
RAIRO. Operations Research, Tome 56 (2022) no. 6, pp. 4229-4250

Hazardous material (Hazmat) transportation is an inseparable section of the industry, despite its major financial and health risks. In order to optimize Hazmat transportation, a multi-objective Hazmat routing model with time windows is employed where the risk and distance are minimized. Due to the uncertainty of Hazmat transportation risk, a Z-number fuzzy approach is used to estimate the risk, in which the probability of occurrence and the severity is considered in the context of Z-information. The severity of the event includes the affected population and depends on the amount of transported Hazmat and the number of individuals affected by the explosion. To tackle the proposed model, the present paper utilizes a multi-objective hybrid genetic algorithm, the validity of which is tested by Solomon’s problems. Furthermore, the optimization of a case study concerning the Hazmat distribution in Iran is analyzed using the suggested approach to assess the efficiency of the proposed fuzzy problem in real-world applications.

DOI : 10.1051/ro/2022197
Classification : 90B06, 90B50, 90C05, 90C29, 90C70, 68T20
Keywords: Vehicle routing problem with time windows, hazardous material, risk, Z-number
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     author = {Zandieh, Fatemeh and Ghannadpour, Seyed Farid},
     title = { $Z$-trapezoidal risk assessment for multi-objective {Hazmat} routing model with time windows},
     journal = {RAIRO. Operations Research},
     pages = {4229--4250},
     year = {2022},
     publisher = {EDP-Sciences},
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     number = {6},
     doi = {10.1051/ro/2022197},
     mrnumber = {4523951},
     zbl = {1532.90019},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ro/2022197/}
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Zandieh, Fatemeh; Ghannadpour, Seyed Farid. $Z$-trapezoidal risk assessment for multi-objective Hazmat routing model with time windows. RAIRO. Operations Research, Tome 56 (2022) no. 6, pp. 4229-4250. doi: 10.1051/ro/2022197

[1] H. Aboutorab, M. Saberi, M. R. Asadabadi, O. Hussain and E. Chang, ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. J. Expert Syst. Appl. 107 (2018) 115–125. | DOI

[2] K. N. Androutsopoulos and K. G. Zografos, A bi-objective time-dependent vehicle routing and scheduling problem for hazardous materials distribution. EURO J. Transp. Log. 1 (2012) 157–183. | DOI

[3] L. J. P. Araújo, A. Panesar, E. Özcan, J. Atkin, M. Baumers and I. Ashcroft, An experimental analysis of deepest bottom-left-fill packing methods for additive manufacturing. Int. J. Prod. Res. 58 (2020) 6917–6933. | DOI

[4] A.-H. H. Bacar and R. S. Charriffaini, An attractors-based particle swarm optimization for multiobjective capacitated vehicle routing problem. RAIRO:RO 55 (2021) 2599–2614. | MR | Zbl | Numdam | DOI

[5] O. Bahri, E.-G. Talbi and N. B. Amor, A generic fuzzy approach for multi-objective optimization under uncertainty. J. Swarm Evol. Comput. 40 (2018) 166–183. | DOI

[6] A. Baniamerian, M. Bashiri and R. Tavakkoli-Moghaddam, Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with cross-docking. Appl. Soft Comput. 75 (2019) 441–460. | DOI

[7] M. Bashiri, M. Mirzaei and M. Randall, Modeling fuzzy capacitated p -hub center problem and a genetic algorithm solution. J. Appl. Math. Model. 37 (2013) 3513–3525. | MR | Zbl | DOI

[8] J. Brito, F.J. Martínez, J. Moreno and J.-L. Verdegay, An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints. Appl. Soft Comput. 32 (2015) 154–163. | DOI

[9] M. Bruglieri, P. Cappanera and M. Nonato, The Gateway Location Problem: Assessing the impact of candidate site selection policies. Discrete Appl. Math. 165 (2014) 96–111. | MR | Zbl | DOI

[10] G. A. Bula, C. Prodhon, F. A. Gonzalez, H. M. Afsar and N. Velasco, Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. J. Hazard. Mater. 324 (2017) 472–480. | DOI

[11] G. A. Bula, H. M. Afsar, F. A. González, C. Prodhon and N. Velasco, Bi-objective vehicle routing problem for hazardous materials transportation. J. Clean. Prod. 206 (2019) 976–986. | DOI

[12] S.-M. Chen, A. Munif, G.-S. Chen, H.-C. Liu and B.-C. Kuo, Fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights. Expert Syst. Appl. 39 (2012) 6320–6334. | DOI

[13] Z. Chen, W. Zhang, S. Zhang and Y. Chen, Block-matrix-based approach for the vehicle routing problem with transportation type selection under an uncertain environment. Eng. Optim. 52 (2020) 987–1008. | MR | Zbl | DOI

[14] K. Deb, A. Pratap, S. Agarwal and T. J. I. T. O. E. C. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. 6 (2002) 182–197.

[15] M. Dehghan, S. R. Hejazi, M. Karimi-Mamaghan, M. Mohammadi and A. Pirayesh, Capacitated location routing problem with simultaneous pickup and delivery under the risk of disruption. RAIRO:RO 55 (2021). | MR | Zbl | Numdam | DOI

[16] J. Du, X. Li, L. Yu, R. Dan and J. Zhou, Multi-depot vehicle routing problem for hazardous materials transportation: a fuzzy bilevel programming. Inf. Sci. 399 (2017) 201–218. | DOI

[17] J. C. Figueroa-García, J. S. Tenjo-García and C. Franco, A global satisfaction degree method for fuzzy capacitated vehicle routing problems. Heliyon 8 (2022) e09767. | DOI

[18] S. F. Ghannadpour and A. Zarrabi, Multi-objective heterogeneous vehicle routing and scheduling problem with energy minimizing. Swarm Evol. Comput. 44 (2019) 728–747. | DOI

[19] S. F. Ghannadpour and F. Zandiyeh, An adapted multi-objective genetic algorithm for solving the cash in transit vehicle routing problem with vulnerability estimation for risk quantification. Eng. Appl. Artif. Intell. 96 (2020) 103964. | DOI

[20] S. F. Ghannadpour and F. Zandiyeh, A new game-theoretical multi-objective evolutionary approach for cash-in-transit vehicle routing problem with time windows (A Real life Case). Appl. Soft Comput. 93 (2020) 106378. | DOI

[21] S. F. Ghannadpour, F. Zandieh and F. Esmaeili, Optimizing triple bottom-line objectives for sustainable health-care waste collection and routing by a self-adaptive evolutionary algorithm: A case study from Tehran province in Iran. J. Clean. Prod. (2020) 125010.

[22] K. Ghoseiri and S. F. Ghannadpour, Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Appl. Soft Comput. 10 (2010) 1096–1107. | DOI

[23] S. J. Ghoushchi, S. Yousefi and M. Khazaeili, An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Appl. Soft Comput. 81 (2019) 105505. | DOI

[24] B. R. Guha-Sapir and D. Hoyois, PH., EM-DAT: The CRED/OFDA International Disaster Database. Advanced Search, Université Catholique de Louvain: Brussels, Belgium. http://www.emdat.be/advanced_search/index.html., in

[25] K. Hamdi-Dhaoui, N. Labadie and A. Yalaoui, The bi-objective two-dimensional loading vehicle routing problem with partial conflicts. Int. J. Prod. Res. 52 (2014) 5565–5582. | DOI

[26] L. Hulsey, Dayton Daily News (2017).

[27] W. Jiang, C. Xie, B. Wei and Y. Tang, Failure mode and effects analysis based on Z -numbers. Intell. Autom. Soft Comput. (2017) 1–8.

[28] A. Kheirkhah, H. Navidi and M. Messi Bidgoli, A bi-level network interdiction model for solving the hazmat routing problem. Int. J. Prod. Res. 54 (2016) 459–471. | DOI

[29] C. Le Hesran, A. Agarwal, A.-L. Ladier, V. Botta-Genoulaz and V. Laforest, Reducing waste in manufacturing operations: bi-objective scheduling on a single-machine with coupled-tasks. Int. J. Prod. Res. 58 (2020) 7130–7148. | DOI

[30] C. Lee and S. Park, Chebyshev center based column generation. Discrete Appl. Math. 159 (2011) 2251–2265. | MR | Zbl | DOI

[31] S. Majidi, S.-M. Hosseini-Motlagh, S. Yaghoubi and A. Jokar, Fuzzy green vehicle routing problem with simultaneous pickup–delivery and time windows. RAIRO:RO 51 (2017) 1151–1176. | MR | Zbl | Numdam | DOI

[32] K. Mearns and S. Yule, The role of national culture in determining safety performance: Challenges for the global oil and gas industry. Saf. Sci. 47 (2009) 777–785. | DOI

[33] J. Men, P. Jiang and H. Xu, A chance constrained programming approach for HazMat capacitated vehicle routing problem in Type-2 fuzzy environment. J. Clean. Prod. 237 (2019) 117754. | DOI

[34] K. S. Moghaddam and F. Azadian, Chance-constrained multi-objective approach for hazardous materials routing and scheduling under demand and service time uncertainty. J. Multi-Criteria Decis. Anal. 27 (2020) 318–336. | DOI

[35] S. S. Mohri, M. Mohammadi, M. Gendreau, A. Pirayesh, A. Ghasemaghaei and V. Salehi, Hazardous material transportation problems: A comprehensive overview of models and solution approaches. Eur. J. Oper. Res. 302 (2022) 1–38. | MR | Zbl | DOI

[36] H. Nozari, R. Tavakkoli-Moghaddam and J. Gharemani-Nahr, A Neutrosophic Fuzzy Programming Method to Solve a Multi-depot Vehicle Routing Model under Uncertainty during the COVID-19 Pandemic. Int. J. Eng. 35 (2022) 360–371. | DOI

[37] B. Ombuki, B. J. Ross and F. Hanshar, Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl. Intell. 24 (2006) 17–30. | DOI

[38] N. Ouertani, H. Ben-Romdhane and S. Krichen, A decision support system for the dynamic hazardous materials vehicle routing problem. Oper. Res. (2020) 1–26.

[39] R. Pradhananga, E. Taniguchi, T. Yamada and A. G. Qureshi, Bi-objective decision support system for routing and scheduling of hazardous materials. Socio-Econ. Plan. Sci. 48 (2014) 135–148. | DOI

[40] N. Radojičić, A. Djenić and M. Marić, Fuzzy GRASP with path relinking for the Risk-constrained Cash-in-Transit Vehicle Routing Problem. Appl. Soft Comput. 72 (2018) 486–497. | DOI

[41] D. Raeisi and S. Jafarzadeh Ghoushchi, A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions. Appl. Intell. 52 (2022) 13435–13455. | DOI

[42] M. Rahbari, A. Arshadi Khamseh, Y. Sadati-Keneti and M. J. Jafari, A risk-based green location-inventory-routing problem for hazardous materials: NSGA II, MOSA, and multi-objective black widow optimization. Environ. Dev. Sustain. 24 (2022) 2804–2840. | DOI

[43] Y. Shi, T. Boudouh and O. Grunder, A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand. Expert Syst. Appl. 72 (2017) 160–176. | DOI

[44] E. Shokrollahpour, M. Zandieh and B. Dorri, A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. Int. J. Prod. Res. 49 (2011) 3087–3103. | DOI

[45] V. P. Singh, K. Sharma and D. Chakraborty, Fuzzy Stochastic Capacitated Vehicle Routing Problem and Its Applications. Int. J. Fuzzy Syst. 24 (2022) 1478–1490. | DOI

[46] M. M. Solomon, Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35 (1987) 254–265. | MR | Zbl | DOI

[47] M. M. Solomon, Best Known Solutions (2005).

[48] C. D. Tarantilis and C. T. Kiranoudis, Using the vehicle routing problem for the transportation of hazardous materials. Oper. Res. 1 (2001) 67.

[49] H. Tikani, M. Setak and E. Demir, Multi-objective periodic cash transportation problem with path dissimilarity and arrival time variation. Expert Syst. Appl. (2020) 114015.

[50] B. Yan, C. Yan, F. Long and X.-C. Tan, Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm. J. Intell. Manuf. 29 (2018) 1273–1285. | DOI

[51] T. Yang, W. Wang and Q. Wu, Fuzzy Demand Vehicle Routing Problem with Soft Time Windows. Sustainability 14 (2022) 5658. | DOI

[52] Y. Y. Yew, R. C. Delgado, D. J. Heslop and P. A. González, The Yew Disaster Severity Index: a new tool in disaster metrics. Prehosp. Disaster Med. 34 (2019) 8–19. | DOI

[53] F. Yin and Y. Zhao, Optimizing vehicle routing via Stackelberg game framework and distributionally robust equilibrium optimization method. Inf. Sci. 557 (2021) 84–107. | MR | Zbl | DOI

[54] L. A. Zadeh, A note on Z -numbers. Inf. Sci. 181 (2011) 2923–2932. | MR | Zbl | DOI

[55] B. Zahiri, N. C. Suresh and J. De Jong, Resilient hazardous-materials network design under uncertainty and perishability, Comput. Ind. Eng. 143 (2020) 106401. | DOI

[56] F. Zandieh and S. F. Ghannadpour, A comprehensive risk assessment view on interval type-2 fuzzy controller for a time-dependent HazMat routing problem. Eur. J. Oper. Res. (2022). | MR | Zbl

[57] S. Zandkarimkhani, H. Mina, M. Biuki and K. Govindan, A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Ann. Oper. Res. (2020) 1–28. | MR

[58] S. Zhang, M. Chen, W. Zhang and X. Zhuang, Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations. Expert Syst. Appl. (2019) 113123.

[59] J. Zheng, A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow. IEEE Access 8 (2020) 39439–39444. | DOI

[60] G. Zheng, N. Zhu, Z. Tian, Y. Chen and B. Sun, Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf. Sci. 50 (2012) 228–239. | DOI

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