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Solving a multi-objective sustainable waste collection problem considering a new collection network

  • Hamed Farrokhi-Asl
  • Ahmad Makui
  • Armin Jabbarzadeh
  • Farnaz Barzinpour
Original Paper
  • 117 Downloads

Abstract

Waste collection management is considered as one of the important issues in sustainable logistics design which is one of the new concepts in supply chain management. In recent years, researchers’ attentions are attracted to apply green and sustainable concepts in their researches. This paper presents a novel multi-objective mathematical model considering a new collection network for waste collection problem. We are interested in the location decisions in design phase of the network and waste collection decisions in operational phase. The problem consists of activities related to collection, treatment, recycling, and disposal of hazardous wastes in multi stage network. Three objective functions including operational cost and social costs are considered, simultaneously. The model is used to evaluate fuel consumption and carbon dioxide emission and its impact on environment. A new hybrid meta-heuristic algorithm is designed to solve the problem and a new way to represent solutions is provided. Finally, experimental results are conducted and the results obtained by proposed algorithm are compared to four well-known meta-heuristic algorithms with respect to five comparison metrics. The results show the efficiency of proposed algorithm in some comparison metrics.

Keywords

Metaheuristic algorithm Multi-objective Waste collection Sustainable logistics Location routing problem (LRP) 

Mathematical Subject Classification

90B06 90B50 90B80 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Alumur S, Kara BY (2007) A new model for the hazardous waste location-routing problem. Comput Oper Res 34(5):1406–1423CrossRefGoogle Scholar
  2. Azadeh A, Farrokhi-Asl H (2017) The close–open mixed multi depot vehicle routing problem considering internal and external fleet of vehicles. Transp Lett.  https://doi.org/10.1080/19427867.2016.1274468 Google Scholar
  3. Beamon BM (2008) Sustainability and the future of supply chain management. Oper Supply Chain Manag 1(1):4–18Google Scholar
  4. Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res Part B Methodol 45(8):1232–1250CrossRefGoogle Scholar
  5. Best C, Che X, Reynolds RG, Liu D (2010) Multi-objective cultural algorithms. In: 2010 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–9Google Scholar
  6. Brundtland G, Khalid M, Agnelli S, Al-Athel S, Chidzero B, Fadika L, Hauff V, Lang I, Shijun M, de Botero MM, Singh M (1987) Our common future (\’Brundtland report\’)Google Scholar
  7. Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation, CEC’02, 2002, vol 2. IEEE, pp 1051–1056Google Scholar
  8. Coello CAC, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, vol 242. Kluwer Academic, New YorkCrossRefGoogle Scholar
  9. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel problem solving from nature PPSN VI. Springer, Berlin Heidelberg, pp 849–858Google Scholar
  10. Dekker R, Fleischmann M, Inderfurth K, van Wassenhove LN (eds) (2013) Reverse logistics: quantitative models for closed-loop supply chains. Springer, BerlinGoogle Scholar
  11. Demir E, Bektaş T, Laporte G (2014) The bi-objective pollution-routing problem. Eur J Oper Res 232(3):464–478CrossRefGoogle Scholar
  12. Desrochers M, Laporte G (1991) Improvements and extensions to the Miller–Tucker–Zemlin subtour elimination constraints. Oper Res Lett 10(1):27–36CrossRefGoogle Scholar
  13. Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Transp Res Part E Log Transp Rev 48(1):100–114CrossRefGoogle Scholar
  14. Fagerholt K, Laporte G, Norstad I (2010) Reducing fuel emissions by optimizing speed on shipping routes. J Oper Res Soc 61(3):523–529CrossRefGoogle Scholar
  15. Farrokhi-Asl H, Tavakkoli-Moghaddam R, Asgarian B, Sangari E (2017) Metaheuristics for a bi-objective location-routing-problem in waste collection management. J Ind Prod Eng 34(4):239–252Google Scholar
  16. Forkenbrock DJ (2001) Comparison of external costs of rail and truck freight transportation. Transp Res Part A Policy Pract 35(4):321–337CrossRefGoogle Scholar
  17. Gatica G (2018) A biased-randomized heuristic for the waste collection problem in smart cities. Appl Math Comput Intell 730:255CrossRefGoogle Scholar
  18. Haynes W (2013) Student’s t-test. In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H (eds) Encyclopedia of systems biology. Springer, New York, pp 2023–2025CrossRefGoogle Scholar
  19. Hwang CL, Masud ASM (2012) Multiple objective decision making—methods and applications: a state-of-the-art survey, vol 164. Springer, BerlinGoogle Scholar
  20. JapaneseGovernmentWebsite. http://www.mlit.go.jp/common/000037099.pdf. Accessed 1 Jan 2016
  21. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics, 1997. Computational Cybernetics and Simulation, 1997, vol 5. IEEE, pp 4104–4108Google Scholar
  22. Kramer R, Subramanian A, Vidal T, Lucídio dos Anjos FC (2015) A matheuristic approach for the pollution-routing problem. Eur J Oper Res 243(2):523–539CrossRefGoogle Scholar
  23. Küçükoğlu İlker, Ene Seval, Aksoy Aslı, Öztürk Nursel (2015) A memory structure adapted simulated annealing algorithm for a green vehicle routing problem. Environ Sci Pollut Res 22(5):3279–3297CrossRefGoogle Scholar
  24. Laumanns M (2001) SPEA2: improving the strength Pareto evolutionary algorithm. Technical Report, Gloriastrasse 35Google Scholar
  25. Lin C, Choy KL, Ho GT, Chung SH, Lam HY (2014) Survey of green vehicle routing problem: past and future trends. Expert Syst Appl 41(4):1118–1138CrossRefGoogle Scholar
  26. Mahmoudsoltani F, Shahbandarzadeh H, Moghdani R (2018) Using Pareto-based multi-objective evolution algorithms in decision structure to transfer the hazardous materials to safety storage centre. J Clean Prod 184:893–911CrossRefGoogle Scholar
  27. Martínez-Salazar IA, Molina J, Ángel-Bello F, Gómez T, Caballero R (2014) Solving a bi-objective transportation location routing problem by metaheuristic algorithms. Eur J Oper Res 234(1):25–36CrossRefGoogle Scholar
  28. Nam D, Park CH (2000) Multiobjective simulated annealing: a comparative study to evolutionary algorithms. Int J Fuzzy Syst 2(2):87–97Google Scholar
  29. Nambiar JM, Gelders LF, Van Wassenhove LN (1981) A large scale location-allocation problem in the natural rubber industry. Eur J Oper Res 6(2):183–189CrossRefGoogle Scholar
  30. Oliver IM, Smith D, Holland JR (1987) Study of permutation crossover operators on the traveling salesman problem. In: Genetic algorithms and their applications: proceedings of the 2nd international conference on genetic algorithms: July 28–31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. L. Erlhaum Associates, Hillsdale, NJGoogle Scholar
  31. Rabbani M, Farrokhi-asl H, Rafiei H (2016) A hybrid genetic algorithm for waste collection problem by heterogeneous fleet of vehicles with multiple separated compartments. J Intell Fuzzy Syst 30(3):1817–1830CrossRefGoogle Scholar
  32. Rabbani M, Farrokhi-Asl H, Asgarian B (2017) Solving a bi-objective location routing problem by a NSGA-II combined with clustering approach: application in waste collection problem. J Ind Eng Int 13(1):13–27CrossRefGoogle Scholar
  33. Rabbani M, Heidari R, Farrokhi-Asl H, Rahimi N (2018) Using metaheuristic algorithms to solve a multi-objective industrial hazardous waste location-routing problem considering incompatible waste types. J Clean Prod 170:227–241CrossRefGoogle Scholar
  34. Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the 3rd annual conference on evolutionary programming, Singapore, pp 131–139Google Scholar
  35. Salimifard K, Shahbandarzadeh H, Raeesi R (2012) Green transportation and the role of operation research. In: International conference on traffic and transport engineering, vol 26, pp 74–79Google Scholar
  36. Samanlioglu F (2013) A multi-objective mathematical model for the industrial hazardous waste location-routing problem. Eur J Oper Res 226(2):332–340CrossRefGoogle Scholar
  37. Sbihi A, Eglese RW (2007) Combinatorial optimization and green logistics. 4OR 5(2):99–116CrossRefGoogle Scholar
  38. Tol RS (2005) The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33(16):2064–2074CrossRefGoogle Scholar
  39. Tseng SC, Hung SW (2014) A strategic decision-making model considering the social costs of carbon dioxide emissions for sustainable supply chain management. J Environ Manage 133:315–322CrossRefGoogle Scholar
  40. Xiao Y, Zhao Q, Kaku I, Xu Y (2012) Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput Oper Res 39(7):1419–1431CrossRefGoogle Scholar
  41. Zhao J, Zhao J (2010) Model and algorithm for hazardous waste location-routing problem. In: ICLEM—logistics for sustained economic development: infrastructure, information, integration, pp 2843–2849Google Scholar
  42. Zografros KG, Samara S (1989) Combined location-routing model for hazardous waste transportation and disposal. Transp Res Rec 1245:52Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Industrial EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Department of Automated Production EngineeringEcole de technologie superieure (ETS)MontrealCanada

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