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Modelling and Solving the Inventory Routing Problem with CO2 Emissions Consideration and Transshipment Option

  • Misagh Rahbari
  • Bahman Naderi
  • Mohammad Mohammadi
Original Article
  • 21 Downloads

Abstract

This paper introduces a multi-period, multi-product green inventory routing problem with transshipment option, where capacitated vehicles distribute products from multiple suppliers to one customer to meet the given demand of products. The demand associated with the customer is assumed to be time-varying and deterministic. Greenhouse gas emissions from transport activities in a supply chain are a main reason for global warming. One of the main types of greenhouse gas is CO2 from vehicles and its impact on the environment. Inventory and routing decisions can help in the reduction of CO2 emissions if these emissions are taken into account by researchers. Also, as one of the main topics of this paper, the transshipment option is considered in the proposed model. The model is a mixed-integer programming (MIP) which has been solved and validated by General Algebraic Modeling System (GAMS). Finally, small and large-scale test problems are randomly generated and solved by the simulated annealing algorithm (SA). The computational results for different test problems showed that the proposed SA performs well and converges fast to reasonable solutions compared with GAMS. According to the results, it is determined that the transshipment option reduces CO2 emissions and costs by shortening the distance traveled.

Keywords

Inventory routing problem Green supply chain Mixed integer programming Transshipment Simulated annealing algorithm Global warming Carbon dioxide emissions 

References

  1. Abdelmaguid TF, Dessouky MM, Ordóñez F (2009) Heuristic approaches for the inventory routing problem with backlogging. Comput Ind Eng 56(4):1519–1534CrossRefGoogle Scholar
  2. Al Shamsi A, Al Raisi A, Aftab M (2014) Pollution-inventory routing problem with perishable goods. In: Golinska, P. (Ed.), Logistics Operations: Supply Chain Management and Sustainability. Springer International Publishing Switzerland, pp. 585–596.  https://doi.org/10.1007/978-3-319-07287-6
  3. Alkawaleet N, Hsieh YF, Wang Y (2014) Inventory routing problem with CO2 emissions consideration. In: Golinska, P. (Ed.), Logistics Operations: Supply Chain Management and Sustainability. Springer International Publishing Switzerland, pp. 611–619.  https://doi.org/10.1007/978-3-319-07287-6
  4. Andersson H, Hoff A, Christiansen M, Hasle G, Løkketangen A (2010) Industrial aspects and literature survey: combined inventory management and routing. Comput Oper Res 37(9):1515–1536CrossRefGoogle Scholar
  5. Archetti C, Bertazzi L, Laporte G, Speranza MG (2007) A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transp Sci 41(3):382–391CrossRefGoogle Scholar
  6. Bard J, Nananukul N (2009) Heuristics for a multiperiod inventory routing problem with production decisions. Comput Ind Eng 57:713–723CrossRefGoogle Scholar
  7. Bell WJ, Dalberto LM, Fisher ML (1983) Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer. Interfaces 13(6):4–23CrossRefGoogle Scholar
  8. Bertazzi L, Bosco A, Guerriero F, Demetrio L (2013) A stochastic inventory routing problem with stock-out. Transp Res C Emerg Technol 27:89–107CrossRefGoogle Scholar
  9. Cheng C, Qi M, Wang X, Zhang Y (2016) Multi-period inventory routing problem under carbon emission regulations. Int J Prod Econ 182:263–275CrossRefGoogle Scholar
  10. Cheng C, Yang P, Qi M, Rousseau L (2017) Modeling a green inventory routing problem with a heterogeneous fleet. Transp Res E 97:97–112CrossRefGoogle Scholar
  11. Coelho LC, Cordeau JF, Laporte G (2012a) The inventory-routing problem with transshipment. Comput Oper Res 39(11):2537–2548CrossRefGoogle Scholar
  12. Coelho LC, Cordeau JF, Laporte G (2012b) Thirty years of inventory routing. Transp Sci 48(1):1–19CrossRefGoogle Scholar
  13. Coelho LC, Laporte G (2014) Optimal joint replenishment, delivery and inventory management policies for perishable products. Comput Oper Res 47:42–52CrossRefGoogle Scholar
  14. Cordeau J, Lagana D, Musmanno R, Vocaturo F (2015) A decomposition-based heuristic for the multiple-product inventory-routing problem. Comput Oper Res 61(2):313–321Google Scholar
  15. Dekker R, Bloemhof J, Mallidis I (2012) Operations research for green logistics–an overview of aspects, issues, contributions and challenges. Eur J Oper Res 219(3):671–679CrossRefGoogle Scholar
  16. Federgruen A, Zipkin P (1984) A combined vehicle routing and inventory allocation problem. Oper Res 32(5):1019–1037CrossRefGoogle Scholar
  17. GAMS Development corporation (2013) General algebraic modeling system (GAMS) release 24.2.2. Washington, DC, USAGoogle Scholar
  18. Glover F, Kochenberger G (2003) Handbook of Metahuristics. Kluwer Academic Publishers, New YorkGoogle Scholar
  19. Hua G, Cheng T, Wang S (2011) Managing carbon footprints in inventory management. Int J Prod Econ 132(2):178–185CrossRefGoogle Scholar
  20. Huang SH, Lin PC (2010) A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty. Transp Res E 46:598–611CrossRefGoogle Scholar
  21. Jemai Z, Rekik Y, Kalaï R (2012) Inventory routing problems in a context of vendor-managed inventory system with consignment stock and transshipment. Prod Plan Control: Manag Oper 24:671–683.  https://doi.org/10.1080/09537287.2012.666844 CrossRefGoogle Scholar
  22. Juan A, Grasman S, Cruz J, Bektas T (2014) A simheuristic algorithm for the single-period stochastic inventory-routing problem with stock-outs. Simul Model Pract Theory 46:40–52CrossRefGoogle Scholar
  23. Li K, Chen B, Sivakumar A, Wu Y (2014) An inventory–routing problem with the objective of travel time minimization. Eur J Oper Res 236:936–945CrossRefGoogle Scholar
  24. Liu SC, Lee WT (2011) A heuristic method for the inventory routing problem with time windows. Expert Syst Appl 38:13223–13231CrossRefGoogle Scholar
  25. Malek M, Guruswamy M, Owens N, Pandya, M (1989) A hybrid algorithm technique, Technical Report, Dept. of Computer Sciences, The University of Texas at Austin, TR-89-06Google Scholar
  26. Mirzaei S, Seifi A (2015) Considering lost sale in inventory routing problems for perishable goods. Comput Ind Eng 87:213–227CrossRefGoogle Scholar
  27. Mirzapour Al-e-hashem SMJ, Rekik Y (2014) Multi-product multi-period inventory routing problem with a transshipment option: a green approach. Int J Prod Econ 157:80–88CrossRefGoogle Scholar
  28. Mjirda A, Jarboui B, Macedo R, Hanafi S, Mladenovic N (2014) A two phase variable neighborhood search for the multi-product inventory routing problem. Comput Oper Res 52:291–299CrossRefGoogle Scholar
  29. Moin NH, Salhi S, Aziz NAB (2011) An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. Int J Prod Econ 133:334–343CrossRefGoogle Scholar
  30. Naderi B, Fatemi Ghomi SMT, Aminnayeri M (2010) A high performing metaheuristic for job shop scheduling with sequence-dependent setup times. Appl Soft Comput 10:703–710CrossRefGoogle Scholar
  31. Naderi B, Zandieh M, Khaleghi Ghoshe Balagh A, Roshanaei V (2009) An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert Syst Appl 36:9625–9633CrossRefGoogle Scholar
  32. Peres I, Repolho H, Martinelli R, Monterio N (2017) Optimization in inventory-routing problem with planned transshipment: a case study in the retail industry. Int J Prod Econ 193:748–756CrossRefGoogle Scholar
  33. Rahbari M, Jahed A (2017) A hybrid simulated annealing algorithm for travelling salesman problem with three neighbor generation structures. The 10th international conference of Iranian operations research societyGoogle Scholar
  34. Rahimi M, Baboli A, Rekik Y (2016) Sustainable inventory routing problem for perishable products by considering reverse logistic. IFAC-Papers OnLine 49:949–954CrossRefGoogle Scholar
  35. Santos E, Ochi L, Simonetti L, Gonzalez P (2016) A hybrid heuristic based on iterated local search for multivehicle inventory routing problem. Electron Notes Discrete Math 61(2):313–321Google Scholar
  36. Shaabani H, Kamalabadi I (2016) An efficient population-based simulated annealing algorithm for the multi-product multi-retailer perishable inventory routing problem. Comput Ind Eng 99:189–201CrossRefGoogle Scholar
  37. Shen Q, Chu F, Chen H (2011) A Lagrangian relaxation approach for a multi-mode inventory routing problem with transshipment in crude oil transportation. Comput Chem Eng 35:2113–2123CrossRefGoogle Scholar
  38. Shukla N, Tiwari MK, Ceglarek D (2013) Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand. Int J Prod Res 51(1):118–137CrossRefGoogle Scholar
  39. Solyalı O, Cordeau JF, Laporte G (2012) Robust inventory routing under demand uncertainty. Transp Sci 46(3):327–340CrossRefGoogle Scholar
  40. Soysal M, Bloemhof-Ruwaard JM, Haijema R, Van der Vorst JG (2015) Modeling an inventory routing problem for perishable products with environmental considerations and demand uncertainty. Int J Prod Econ 164:118–133CrossRefGoogle Scholar
  41. Soysal M, Bloemhof-Ruwaard JM, Haijema R, Van der Vorst JG (2016) Modeling a green inventory routing problem for perishable products with horizontal collaboration. Comput Oper Res 89:168–182CrossRefGoogle Scholar
  42. Treitl S, Nolz PC, Jammernegg W (2012) Incorporating environmental aspects in an inventory routing problem. A case study from the petrochemical industry. Flex Serv Manuf J 26:1–27Google Scholar
  43. Vidovic M, Popovic D, Ratkovic B (2014) Mixed integer and heuristics model for the inventory routing problem in fuel delivery. Int J Prod Econ 61(2):313–321Google Scholar
  44. Zhong Y, Aghezzaf E (2011) Combining DC-programming and steepest-descent to solve the single-vehicle inventory routing problem. Comput Ind Eng 61(2):313–321CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Misagh Rahbari
    • 1
  • Bahman Naderi
    • 1
  • Mohammad Mohammadi
    • 1
  1. 1.Department of Industrial Engineering, Faculty of EngineeringKharazmi UniversityTehranIran

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