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A decision support system for the dynamic hazardous materials vehicle routing problem

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Abstract

The problem of delivering hazardous materials to a set of customers under a dynamic environment is both relevant and challenging. The objective is to find the best routes that minimize both the transportation cost and the travel risk in order to meet the customers’ demands or needs, within predefined time windows. Aside from the difficulties involved in the modeling of the problem, the solution should take into consideration the demands revealed overtime. To deal with this problem, a solution approach is required to continuously adapt the planned routes in order to respond the customers’ demands. In this paper, the dynamic variant of the Hazardous Materials Vehicle Routing Problem with Time Windows (DHVRP) is introduced. Besides, a decision support system is developed for the DHVRP in order to generate the best routes, based on two new meta-heuristics: a bi-population genetic algorithm and a hybrid approach combining the genetic algorithm and the variable neighborhood search. An experimental investigation is conducted to evaluate the proposed algorithms, using Solomon’s 56 benchmarks instances and through several performance measures. We show through computational experiments, that the new approaches are highly competitive with regards to two state-of-the-art algorithms.

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References

  • Alba E, Ben-Romdhane H, Krichen S, Sarasola B (2013) Bipop: a new algorithm with explicit exploration/exploitation control for dynamic optimization problems. In: Yang S, Yao X (eds) Evolutionary computation for dynamic optimization problems. Springer, Berlin, pp 171–191

    Google Scholar 

  • Arjmand M, Najafi AA (2015) Solving a multi-mode bi-objective resource investment problem using meta-heuristic algorithms. Adv Comput Tech Electromag 1:41–58

    Google Scholar 

  • Arnold F, Gendreau M, Srensen K (2019) Efficiently solving very large-scale routing problems. Comput Oper Res 107:32–42

    Google Scholar 

  • Asefi H, Jolai F, Rabiee M, Tayebi Araghi ME (2014) A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem. Int J Adv Manuf Technol 75(5):1017–1033

    Google Scholar 

  • Baños R, Ortega J, Gil C, Fernández A, De Toro F (2013) A simulated annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Syst Appl 40(5):1696–1707

    Google Scholar 

  • Baradaran V, Shafaei A, Hosseinian AH (2019) Stochastic vehicle routing problem with heterogeneous vehicles and multiple prioritized time windows: mathematical modeling and solution approach. Comput Ind Eng 131:187–199

    Google Scholar 

  • Ben-Romdhane H, Alba E, Krichen S (2016) Towards a dynamic modeling of the predator prey problem. Appl Intell 44(4):755–770

    Google Scholar 

  • Ben-Romdhane H, Krichen S (2017) An adaptive approach for monitoring evolutionary algorithms behavior for dynamic environments. In: Artificial life conference proceedings, vol 14. MIT Press, pp 460–466

  • Bula GA, Afsar HM, Gonzalez FA, Prodhon C, Velasco N (2019) Bi-objective vehicle routing problem for hazardous materials transportation. J Clean Prod 206:976–986

    Google Scholar 

  • Bulhões T, Hà MH, Martinelli R, Vidal T (2018) The vehicle routing problem with service level constraints. Eur J Oper Res 265(2):544–558

    Google Scholar 

  • Campbell AM, Wilson JH (2014) Forty years of periodic vehicle routing. Networks 63(1):2–15

    Google Scholar 

  • Cordeiro FG, Bezerra BS, Peixoto ASP, Ramos RAR (2016) Methodological aspects for modeling the environmental risk of transporting hazardous materials by road. Transp Res D Transp Environ 44:105–121

    Google Scholar 

  • Cuneo V, Nigro M, Carrese S, Ardito CF, Corman F (2018) Risk based, multi objective vehicle routing problem for hazardous materials: a test case in downstream fuel logistics. Transp Res Procedia 30:43–52

    Google Scholar 

  • Ditta A, Figueroa O, Galindo G, Yie-Pinedo R (2018) A review on research in transportation of hazardous materials. Socio Econ Plan Sci 100665

  • Du Z, Zhang S, Zhou Q, Yuen KF, Wong YD (2018) Hazardous materials analysis and disposal procedures during ship recycling. Resour Conserv Recycl 131:158–171

    Google Scholar 

  • Eksioglu B, Vural AV, Reisman A (2009) The vehicle routing problem: a taxonomic review. Comput Ind Eng 57(4):1472–1483

    Google Scholar 

  • Erkut E, Ingolfsson A (2000) Catastrophe avoidance models for hazardous materials route planning. Transp Sci 34(2):165–179

    Google Scholar 

  • Erkut E, Tjandra SA, Verter V (2007) Hazardous materials transportation. In: Handbooks in operations research and management science, vol 14, pp 539–621

  • Erkut E, Verter V (1998) Modeling of transport risk for hazardous materials. Oper Res 46(5):625–642

    Google Scholar 

  • Freeman RA (1990) Ccps guidelines for chemical process quantitative risk analysis. Plant/Oper Prog 9(4):231–235

    Google Scholar 

  • Gendreau M, Guertin F, Potvin JY, Taillard E (1999) Parallel tabu search for real-time vehicle routing and dispatching. Transp Sci 33(4):381–390

    Google Scholar 

  • Ghoseiri K, Ghannadpour SF (2010) Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Appl Soft Comput 10(4):1096–1107

    Google Scholar 

  • Goh CK, Tan KC (2009) A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. Trans Evol Comput 13(1):103–127

    Google Scholar 

  • Hansen P, Mladenovi N (1997) Variable neighborhood search for the p-median. Location Sci 5(4):207–226

    Google Scholar 

  • Huang B, Fery P (2005) Aiding route decision for hazardous material transportation. Transportation Research Board 2005

  • Kaabachi I, Yahyaoui H, Krichen S, Dekdouk A (2019) Measuring and evaluating hybrid metaheuristics for solving the multi-compartment vehicle routing problem. Measurement 141:407–419

    Google Scholar 

  • Kara B, Erkut E, Verter V (2003) Accurate calculation of hazardous materials transport risks. Oper Res Lett 31(4):285–292

    Google Scholar 

  • Karakatič S, Podgorelec V (2015) A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl Soft Comput 27:519–532

    Google Scholar 

  • Kazantzi V, Kazantzis N, Gerogiannis VC (2011) Risk informed optimization of a hazardous material multi-periodic transportation model. J Loss Prev Process Ind 24(6):767–773

    Google Scholar 

  • Larsen A, Madsen OB, Solomon MM (2008) Recent developments in dynamic vehicle routing systems. In: The vehicle routing problem: latest advances and new challenges. Springer, Boston, MA, pp 199–218

    Google Scholar 

  • Mahmoudabadi A, Seyedhosseini SM (2014) Developing a bi-level objective model of risk-cost trade-off for solving locating-routing problem in transportation of hazardous material. Int J Transp Eng 1:173–182

    Google Scholar 

  • Melian-Batista B, Santiago AD, AngelBello F, Alvarez A (2014) A bi-objective vehicle routing problem with time windows: a real case in tenerife. Appl Soft Comput 17:140–152

    Google Scholar 

  • Minocha B, Tripathi S, Mohan C (2011) Solving vehicle routing and scheduling problems using hybrid genetic algorithm. In: 2011 3rd international conference on electronics computer technology, vol 2, pp 189–193

  • Montemanni R, Gambardella LM, Rizzoli AE, Donati AV (2005) Ant colony system for a dynamic vehicle routing problem. J Comb Optim 10(4):327–343

    Google Scholar 

  • Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24

    Google Scholar 

  • Noll G, Hildebrand M, Schnepp R, Rudner G (2012) Hazardous materials: managing the incident. Jones & Bartlett Publishers, Burlington

    Google Scholar 

  • Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30

    Google Scholar 

  • Parsopoulos KE, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2004) Vector evaluated differential evolution for multiobjective optimization. In: Proceedings of the 2004 congress on evolutionary computation, vol 1, pp 204–211

  • Pillac V, Gendreau M, Guret C, Medaglia AL (2013) A review of dynamic vehicle routing problems. Eur J Oper Res 225(1):1–11

    Google Scholar 

  • Pohanish RP (2017) Sittig’s handbook of toxic and hazardous chemicals and carcinogens. Elsevier, Amsterdam

    Google Scholar 

  • Poku-Boansi M, Tornyeviadzi P, Adarkwa KK (2018) Next to suffer: population exposure risk to hazardous material transportation in ghana. J Transp Health 10:203–212

    Google Scholar 

  • Pradhananga R, Taniguchi E, Yamada T, Qureshi AG (2014) Bi-objective decision support system for routing and scheduling of hazardous materials. Socio Econ Plan Sci 48(2):135–148

    Google Scholar 

  • Pradhananga R, Taniguchi E, Yamada T, Qureshi AG (2014) Environmental analysis of pareto optimal routes in hazardous material transportation. Procedia Soc Behav Sci 125:506–517. In: 8th international conference on city logistics 17–19 June 2013, Bali, Indonesia

  • Psaraftis HN (1995) Dynamic vehicle routing: status and prospects. Ann Oper Res 61(1):143–164

    Google Scholar 

  • Psaraftis HN, Wen M, Kontovas CA (2016) Dynamic vehicle routing problems: three decades and counting. Networks 67(1):3–31

    Google Scholar 

  • Rabbani M, Heidari R, Yazdanparast R (2019) A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. Eur J Oper Res 272(3):945–961

    Google Scholar 

  • Ritzinger U, Puchinger J, Hartl RF (2016) A survey on dynamic and stochastic vehicle routing problems. Int J Prod Res 54(1):215–231

    Google Scholar 

  • Saïd H, Jasmina L, Nenad M, Christophe W, Igor C (2016) New variable neighbourhood search based 0–1 mip heuristics. Yugoslav J Oper Res 25(3):343–360

    Google Scholar 

  • Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st international conference on genetic algorithms, pp 93–100. L. Erlbaum Associates Inc., Hillsdale, NJ

  • Shafiee M, Roshanaee M (2011) The modeling of optimization and decline transportation costs in supply chain system. In: International conference on economics and finance research, IACSIT Press, Singapore

  • Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265

    Google Scholar 

  • Taillard É, Badeau P, Gendreau M, Guertin F, Potvin JY (1997) A tabu search heuristic for the vehicle routing problem with soft time windows. Transp Sci 31(2):170–186

    Google Scholar 

  • Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, Hoboken

  • Tan K, Lee T, Khor E (2002) Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons. Artif Intell Rev 17(4):251–290

    Google Scholar 

  • Tan KC, Chew YH, Lee LH (2005) A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Comput Optim Appl 34(1):115

    Google Scholar 

  • Tas D, Jabali O, Woensel TV (2014) A vehicle routing problem with flexible time windows. Comput Oper Res 52:39–54

    Google Scholar 

  • Torretta V, Rada EC, Schiavon M, Viotti P (2017) Decision support systems for assessing risks involved in transporting hazardous materials: a review. Saf Sci 92:1–9

    Google Scholar 

  • Toth P, Vigo D (2002) The vehicle routing problem. SIAM, Philadelphia

    Google Scholar 

  • Toth P, Vigo D (2014) Vehicle routing: problems, methods, and applications. SIAM, Philadelphia

    Google Scholar 

  • Toumazis I, Kwon C (2013) Routing hazardous materials on time-dependent networks using conditional value-at-risk. Transp Res C Emerg Technol 37:73–92

    Google Scholar 

  • Uddin MS (2015) Hybrid genetic algorithm and variable neighborhood search for dynamic facility layout problem. Open J Optim 4(04):156–168

    Google Scholar 

  • Ujihara AM, Gough M (2015) Managing ash from municipal waste incinerators. Routledge, Abingdon

    Google Scholar 

  • Voß S, Martello S, Osman IH, Roucairol C (2012) Meta-heuristics: advances and trends in local search paradigms for optimization. Springer, Berlin

    Google Scholar 

  • Xu T, Yang F, Li J, Yuan WA (2013) bi-objective mathematical model for hazmat vehicle routing problem with path-based risk estimation. In: 2013 6th international conference on business intelligence and financial engineering, pp 643–646

  • Yang Z, van Osta JP, van Veen B, van Krevelen R, van Klaveren R, Stam A, Kok J, Báck T, Emmerich M (2017) Dynamic vehicle routing with time windows in theory and practice. Nat Comput 16(1):119–134

    Google Scholar 

  • Zhang H, Zhang Q, Ma L, Zhang Z, Liu Y (2019) A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows. Inf Sci 490:166–190

    Google Scholar 

  • Zhang S, Zhang W, Gajpal Y, Appadoo SS (2019) Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem. Springer, Singapore, pp 251–260

    Google Scholar 

  • Zhao J, Verter V (2015) A bi-objective model for the used oil location-routing problem. Comput Oper Res 62:157–168

    Google Scholar 

  • Zografos KG, Androutsopoulos KN (2004) A heuristic algorithm for solving hazardous materials distribution problems. Eur J Oper Res 152(2):507–519

    Google Scholar 

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  1. Université de Tunis, Institut Supérieur de Gestion de Tunis, Tunis, Tunisia

    Nasreddine Ouertani, Hajer Ben-Romdhane & Saoussen Krichen

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  1. Nasreddine Ouertani
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  2. Hajer Ben-Romdhane
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  3. Saoussen Krichen
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Correspondence to Nasreddine Ouertani.

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Ouertani, N., Ben-Romdhane, H. & Krichen, S. A decision support system for the dynamic hazardous materials vehicle routing problem. Oper Res Int J 22, 551–576 (2022). https://doi.org/10.1007/s12351-020-00562-w

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