Multi-period inventory routing problem under carbon emission regulations

International Journal of Production Economics

Volumn 182, Issue , 2016, Pages 263-275

  Cheng, Chun ^a   Qi, Mingyao ^a   Wang, Xingyi ^a   Zhang, Ying ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Carbon emission regulation; Green logistics; Hybrid genetic algorithm; Inventory routing problem

Indexed keywords

CRASHWORTHINESS; FLEET OPERATIONS; FUELS; GENETIC ALGORITHMS; INTEGER PROGRAMMING; LOGISTICS; NONLINEAR PROGRAMMING;

CARBON EMISSIONS; FUEL CONSUMPTION RATES; GREEN LOGISTICS; HYBRID GENETIC ALGORITHMS; INVENTORY ROUTING PROBLEMS; LINEARIZATION METHODS; MIXED INTEGER NONLINEAR PROGRAMMING MODELS; NEAR-OPTIMAL SOLUTIONS;

COSTS;

EID: 84987819090     PISSN: 09255273     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijpe.2016.09.001     Document Type: Article

References (52)

1. Abdelmaguid, T.F., Dessouky, M.M., A genetic algorithm approach to the integrated inventory-distribution problem. Int. J. Prod. Res. 44:21 (2006), 4445–4464, 10.1080/00207540600597138.
2. Andersson, H., Hoff, A., Christiansen, M., et al. Industrial aspects and literature survey: Combined inventory management and routing. Comput. Oper. Res. 37:9 (2010), 1515–1536, 10.1016/j.cor.2009.11.009.
3. Anily, S., Federgruen, A., One warehouse multiple retailer systems with vehicle routing costs. Manag. Sci. 36:1 (1990), 92–114, 10.1287/mnsc.36.1.92.
4. Anily, S., Federgruen, A., Two-echelon distribution systems with vehicle routing costs and central inventory. Oper. Res. 41:1 (1993), 37–47, 10.1287/opre.41.1.37.
5. Anily, S., Bramel, J., An asymptotic 98.5%-effective lower bound on fixed partition policies for the inventory routing problem. Discret. Appl. Math. 145:1 (2004), 22–39, 10.1016/j.dam.2003.09.005.
6. Baker, B.M., Ayechew, M.A., A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30:5 (2003), 787–800, 10.1016/S0305-0548(02)00051-5.
7. Benjaafar, S., Li, Y., Daskin, M., Carbon footprint and the management of supply chains: Insights from simple models. IEEE Trans. Autom. Sci. Eng. 10:1 (2013), 99–116, 10.1109/TASE.2012.2203304.
8. Berger, J., Barkaoui, M., A parallel hybrid genetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 31:12 (2004), 2037–2053, 10.1016/S0305-0548(03)00163-1.
9. Borade, A.B., Sweeney, E., Decision support system for vendor managed inventory supply chain: a case study. Int. J. Prod. Res. 53:16 (2014), 4789–4818, 10.1080/00207543.2014.993047.
10. Bozorgi, A., Multi-product inventory model for cold items with cost and emission consideration. Int. J. Prod. Econ. 176 (2016), 123–142, 10.1016/j.ijpe.2016.03.011.
11. Cachon, G.P., Retail store density and the cost of greenhouse gas emissions. Manag. Sci. 60:8 (2014), 1907–1925, 10.1287/mnsc.2013.1819.
12. Chinese State Council, 2014. Long-Term Logistics Industry Development Planning (2014–2020). 〈http://www.gov.cn/zhengce/content/2014-10/04/content_9120.htm〉, (accessed on 04.10.14).
13. Cho, D.W., Lee, Y.H., Lee, T.Y., Gen, M., An adaptive genetic algorithm for the time dependent inventory routing problem. J. Intell. Manuf. 25:5 (2014), 1025–1042, 10.1007/s10845-012-0727-5.
14. Coelho, L.C., Laporte, G., A branch-and-cut algorithm for the multi-product multi-vehicle inventory-routing problem. Int. J. Prod. Res. 51:23–24 (2013), 7156–7169, 10.1080/00207543.2012.757668.
15. Coelho, L.C., Cordeau, J.F., Laporte, G., Thirty years of inventory routing. Transp. Sci. 48:1 (2014), 1–19, 10.1287/trsc.2013.0472.
16. Custódio, A.L., Oliveira, R.C., Redesigning distribution operations: a case study on integrating inventory management and vehicle routes design. Int. J. Logist.: Res. Appl. 9:2 (2006), 169–187, 10.1080/13675560600649982.
17. Dekker, R., Bloemhof, J., Mallidis, I., Operations research for green logistics–an overview of aspects, issues, contributions and challenges. Eur. J. Oper. Res. 219:3 (2012), 671–679, 10.1016/j.ejor.2011.11.010.
18. Dye, C.Y., Yang, C.T., Sustainable trade credit and replenishment decisions with credit-linked demand under carbon emission constraints. Eur. J. Oper. Res. 244:1 (2015), 187–200, 10.1016/j.ejor.2015.01.026.
19. Elhedhli, S., Merrick, R., Green supply chain network design to reduce carbon emissions. Transp. Res. Part D: Transp. Environ. 17:5 (2012), 370–379, 10.1016/j.trd.2012.02.002.
20. Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning. 1989, Addison-Wesley Publishing Company Inc.
21. Ho, W., Ho, G.T., Ji, P., Lau, H.C., A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21:4 (2008), 548–557, 10.1016/j.engappai.2007.06.001.
22. Hoen, K.M.R., Tan, T., Fransoo, J.C., et al. Effect of carbon emission regulations on transport mode selection in supply chains. (Working paper), 2010, Eindhoven University of Technology, Netherlands (Working paper) 〈http://alexandria.tue.nl/repository/books/672727.pdf〉.
23. Hua, G., Cheng, T.C.E., Wang, S., Managing carbon footprints in inventory management. Int. J. Prod. Econ. 132:2 (2011), 178–185, 10.1016/j.ijpe.2011.03.024.
24. Hua, G., Qiao, H., Jian, L., 2011b. Optimal Order Lot Sizing and Pricing with Carbon Trade. 〈http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1796507〉, (accessed on 22.02.11).
25. Huang, S., Lin, P., A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty. Transp. Res. Part E: Logist. Transp. Rev. 46:5 (2010), 598–611, 10.1016/j.tre.2010.01.006.
26. IPCC. Climate Change 2014 Synthesis Report–Longer Report 2014. 〈http://www.ipcc.ch/pdf/assessment-report/ar5/syr/SYR_AR5_LONGERREPORT_Corr2.pdf〉, (accessed on 01.11.14).
27. Konur, D., Carbon constrained integrated inventory control and truckload transportation with heterogeneous freight trucks. Int. J. Prod. Econ. 153 (2014), 268–279, 10.1016/j.ijpe.2014.03.009.
28. Konur, D., Schaefer, B., Integrated inventory control and transportation decisions under carbon emissions regulations: LTL vs. TL carriers. Transp. Res. Part E: Logist. Transp. Rev. 68 (2014), 14–38, 10.1016/j.tre.2014.04.012.
29. Lee, C.G., Bozer, Y.A., White, C.C. Ⅲ, A Heuristic Approach and Properties of Optimal Solutions to the Dynamic Inventory Routing Problem. 2003, University of Toronto, Toronto, Canada (Working paper) 〈http://www.mie.utoronto.ca/labs/ilr/papers/IRP(05062003).pdf〉.
30. Letmathe, P., Balakrishnan, N., Environmental considerations on the optimal product mix. Eur. J. Oper. Res. 167:2 (2005), 398–412, 10.1016/j.ejor.2004.04.025.
31. Martí, J.M.C., Tancrez, J., Seifert, R.W., Carbon footprint and responsiveness trade-offs in supply chain network design. Int. J. Prod. Econ. 166 (2015), 129–142, 10.1016/j.ijpe.2015.04.016.
32. McCormick, G.P., Computability of global solutions to factorable nonconvex programs: Part I—Convex underestimating problems. Math. Program. 10:1 (1976), 147–175, 10.1007/BF01580665.
33. Mirzapour Al-E-Hashem, S.M.J., Rekik, Y., Multi-product multi-period inventory routing problem with a transshipment option: a green approach. Int. J. Prod. Econ. 157 (2014), 80–88, 10.1016/j.ijpe.2013.09.005.
34. Mjirda, A., Jarboui, B., Macedo, B., et al. A two phase variable neighborhood search for the multi-product inventory routing problem. Comput. Oper. Res. 52 (2014), 291–299, 10.1016/j.cor.2013.06.006.
35. Moin, N.H., Salhi, S., Inventory routing problems: a logistical overview. J. Oper. Res. Soc. 58:9 (2007), 1185–1194, 10.1057/palgrave.jors.2602264.
36. Moin, N.H., Salhi, S., Aziz, N., An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. Int. J. Prod. Econ. 133:1 (2011), 334–343, 10.1016/j.ijpe.2010.06.012.
37. Palak, G., Ekşioğlu, S.D., Geunes, J., Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel supply chain. Int. J. Prod. Econ. 154 (2014), 198–216, 10.1016/j.ijpe.2014.04.019.
38. Pan, S., Ballot, E., Fontane, F., The reduction of greenhouse gas emissions from freight transport by pooling supply chains. Int. J. Prod. Econ. 143:1 (2013), 86–94, 10.1016/j.ijpe.2010.10.023.
39. Prins, C., A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31:12 (2004), 1985–2002, 10.1016/S0305-0548(03)00158-8.
40. Qin, L., Miao, L., Ruan, Q., et al. A local search method for periodic inventory routing problem. Expert Syst. Appl. 41:2 (2014), 765–778, 10.1016/j.eswa.2013.07.100.
41. Ramkumar, N., Subramanian, P., Narendran, T.T., et al. Mixed integer linear programming model for multi-commodity multi-depot inventory routing problem. OPSEARCH 49:4 (2012), 413–429, 10.1007/s12597-012-0087-0.
42. Rosenkrantz, D.H., Stearns, R.E., Lewis, P.M. II, An analysis of several heuristics for the traveling salesman problem. SIAM J. Comput. 6:3 (1977), 563–581, 10.1137/0206041.
43. Roundy, R., 98%-effective integer-ratio lot-sizing for one-warehouse multi-retailer systems. Manag. Sci. 31:11 (1985), 1416–1430, 10.1287/mnsc.31.11.1416.
44. Seuring, S., A review of modeling approaches for sustainable supply chain management. Decis. Support Syst. 54:4 (2013), 1513–1520 10.1016/j.dss.2012.05.053.
45. Treitl, S., Nolz, P.C., Jammernegg, W., Incorporating environmental aspects in an inventory routing problem. A case study from the petrochemical industry. Flex. Serv. Manuf. J. 26:1–2 (2014), 143–169, 10.1007/s10696-012-9158-z.
46. Ubeda, S., Arcelus, F.J., Faulin, J., 2011. Green logistics at Eroski: A case study. International Journal of Production Economics 131 (1), 44–51, http://dx.doi.org/10.1016/j.ijpe.2010.04.041.
47. US Government Accountability Office, 2008. Carbon offsets: the U.S. Voluntary Market Is Growing, but Quality Assurance Poses Challenges for MarketParticipants.〈 http://www.gao.gov/new.items/d081048.pdf 〉, (accessed on 08.08).
48. Vidović, M., Popović, D., Ratković, B., Mixed integer and heuristics model for the inventory routing problem in fuel delivery. Int. J. Prod. Econ. 147:C (2014), 593–604, 10.1016/j.ijpe.2013.04.034.
49. Viswanathan, S., Mathur, K., Integrating routing and inventory decisions in one-warehouse multiretailer multiproduct distribution systems. Manag. Sci. 43:3 (1997), 294–312, 10.1287/mnsc.43.3.294.
50. Xiao, Y., Zhao, Q., Kaku, I., Xu, Y., Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput. Oper. Res. 39:7 (2012), 1419–1431, 10.1016/j.cor.2011.08.013.
51. Zhang, S., Lee, C.K.M., Choy, K.L., Ho, W., Ip, W.H., Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transp. Res. Part D: Transp. Environ. 31 (2014), 85–99, 10.1016/j.trd.2014.05.015.
52. Zhao, Q., Chen, S., Zang, C., Model and algorithm for inventory/routing decision in a three-echelon logistics system. Eur. J. Oper. Res. 191:3 (2008), 623–635, 10.1016/j.ejor.2006.12.056.