Open Access
RAIRO-Oper. Res.
Volume 56, Number 6, November-December 2022
Page(s) 4229 - 4250
Published online 21 December 2022
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [EDP Sciences] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • J. Brito, F.J. Martnez, 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [MathSciNet] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [MathSciNet] [Google Scholar]
  • 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. [Google Scholar]
  • 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). [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [PubMed] [Google Scholar]
  • S.F. Ghannadpour and A. Zarrabi, Multi-objective heterogeneous vehicle routing and scheduling problem with energy minimizing. Swarm Evol. Comput. 44 (2019) 728–747. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • B.R. Guha-Sapir and D. Hoyois, PH., EM-DAT: The CRED/OFDA International Disaster Database. Advanced Search, Université Catholique de Louvain: Brussels, Belgium., in [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • L. Hulsey, Dayton Daily News (2017). [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • C. Lee and S. Park, Chebyshev center based column generation. Discrete Appl. Math. 159 (2011) 2251–2265. [CrossRef] [MathSciNet] [Google Scholar]
  • 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. [CrossRef] [EDP Sciences] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [PubMed] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [Google Scholar]
  • M.M. Solomon, Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35 (1987) 254–265. [Google Scholar]
  • M.M. Solomon, Best Known Solutions (2005). [Google Scholar]
  • C.D. Tarantilis and C.T. Kiranoudis, Using the vehicle routing problem for the transportation of hazardous materials. Oper. Res. 1 (2001) 67. [Google Scholar]
  • 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. [Google Scholar]
  • 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. [Google Scholar]
  • T. Yang, W. Wang and Q. Wu, Fuzzy Demand Vehicle Routing Problem with Soft Time Windows. Sustainability 14 (2022) 5658. [CrossRef] [Google Scholar]
  • 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. [CrossRef] [PubMed] [Google Scholar]
  • F. Yin and Y. Zhao, Optimizing vehicle routing via Stackelberg game framework and distributionally robust equilibrium optimization method. Inf. Sci. 557 (2021) 84–107. [CrossRef] [Google Scholar]
  • L.A. Zadeh, A note on Z-numbers. Inf. Sci. 181 (2011) 2923–2932. [CrossRef] [Google Scholar]
  • B. Zahiri, N.C. Suresh and J. de Jong, Resilient hazardous-materials network design under uncertainty and perishability, Comput. Ind. Eng. 143 (2020) 106401. [CrossRef] [Google Scholar]
  • 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). [Google Scholar]
  • 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. [Google Scholar]
  • 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. [Google Scholar]
  • J. Zheng, A Vehicle Routing Problem Model With Multiple Fuzzy Windows Based on Time-Varying Traffic Flow. IEEE Access 8 (2020) 39439–39444. [CrossRef] [Google Scholar]
  • 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. [Google Scholar]

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