no. 4
Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform
RAIRO. Operations Research, Tome 56 (2022) no. 4, pp. 2115-2137

This paper examines the environmental impact produced by multi-vehicle transportation on a sustainable supply chain (SC) network. The relevance of green principles is gaining momentum day by day, which has forced the governments to introduce carbon emission schemes for the transportation associated with the firms. Various countries around the globe are introducing carbon-pricing schemes, in which a carbon tax is imposed based on the amount of anthropogenic emissions. A firm, which sets environmental standards for the emission associated with its operational activities, should design a transportation network based on the trade-off between its economic efficiency and the carbon emission. In this paper, the main focus is to design a sustainable supply chain network. A mixed-integer-non-linear-programming (MINLP) model is formulated to minimize the overall cost incurred in a multi-vehicle, multi-product sustainable transportation network. The meta-heuristic approach i.e., Hybrid Chemical Reaction Optimization Algorithm with Tabu search (CRO-TS) and LINGO solver have been used to solve the proposed model. This analysis can guide the government to encourage the logistics service providers to capitalize on anthropogenic gas emission systems and simultaneously design the tax policy on carbon emission.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2022072
Classification : 90C30, 90C59, 90C31
Keywords: Sustainable transportation network design, multi-vehicle transportation, carbon emission, chemical reaction optimization algorithm, tabu search, LINGO 
@article{RO_2022__56_4_2115_0,
     author = {Prajapati, Dhirendra and Chelladurai, H. and Zhou, Fuli and Ip, Andrew W. H. and Pratap, Saurabh},
     title = {Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in {B2B} e-commerce platform},
     journal = {RAIRO. Operations Research},
     pages = {2115--2137},
     year = {2022},
     publisher = {EDP-Sciences},
     volume = {56},
     number = {4},
     doi = {10.1051/ro/2022072},
     mrnumber = {4450247},
     zbl = {1492.90177},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ro/2022072/}
}
TY  - JOUR
AU  - Prajapati, Dhirendra
AU  - Chelladurai, H.
AU  - Zhou, Fuli
AU  - Ip, Andrew W. H.
AU  - Pratap, Saurabh
TI  - Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform
JO  - RAIRO. Operations Research
PY  - 2022
SP  - 2115
EP  - 2137
VL  - 56
IS  - 4
PB  - EDP-Sciences
UR  - https://www.numdam.org/articles/10.1051/ro/2022072/
DO  - 10.1051/ro/2022072
LA  - en
ID  - RO_2022__56_4_2115_0
ER  - 
%0 Journal Article
%A Prajapati, Dhirendra
%A Chelladurai, H.
%A Zhou, Fuli
%A Ip, Andrew W. H.
%A Pratap, Saurabh
%T Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform
%J RAIRO. Operations Research
%D 2022
%P 2115-2137
%V 56
%N 4
%I EDP-Sciences
%U https://www.numdam.org/articles/10.1051/ro/2022072/
%R 10.1051/ro/2022072
%G en
%F RO_2022__56_4_2115_0
Prajapati, Dhirendra; Chelladurai, H.; Zhou, Fuli; Ip, Andrew W. H.; Pratap, Saurabh. Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform. RAIRO. Operations Research, Tome 56 (2022) no. 4, pp. 2115-2137. doi: 10.1051/ro/2022072

[1] R. Abyazi-Sani and R. Ghanbari, An efficient tabu search for solving the uncapacitated single allocation hub location problem. Comput. Ind. Eng. 93 (2016) 99–109. | DOI

[2] A. Anderluh, P. C. Nolz, V. C. Hemmelmayr and T. G. Crainic, Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and “grey zone” customers arising in urban logistics. Eur. J. Oper. Res. 289 (2021) 940–958. | MR | Zbl | DOI

[3] M. S. Atabaki, A. A. Khamseh and M. Mohammadi, A priority-based firefly algorithm for network design of a closed-loop supply chain with price-sensitive demand. Comput. Ind. Eng. 135 (2019) 814–837. | DOI

[4] C. Claycomb, K. Iyer and R. Germain, Predicting the level of B2B e-commerce in industrial organizations. Ind. Mark. Manag. 34 (2005) 221–234. | DOI

[5] J. F. Cordeau, M. Gendreau and G. Laporte, A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30 (1997) 105–119. | Zbl | DOI

[6] T. L. Dam, K. Li and P. Fournier-Viger, Chemical reaction optimization with unified tabu search for the vehicle routing problem. Soft Comput. 21 (2017) 6421–6433. | DOI

[7] A. Dwivedi, A. Jha, D. Prajapati, N. Sreenu and S. Pratap, Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem. Mod. Supply Chain Res. Appl. (2020). DOI: . | DOI

[8] S. Elhedhli and R. Merrick, Green supply chain network design to reduce carbon emissions. Transp. Res. Part D Transp. Environ. 17 (2012) 370–379. | DOI

[9] B. Fahimnia, J. Sarkis, F. Dehghanian, N. Banihashemi and S. Rahman, The impact of carbon pricing on a closed-loop supply chain: an Australian case study. J. Clean. Prod. 59 (2013) 210–225. | DOI

[10] J. Hong, A. Diabat, V. V. Panicker and S. Rajagopalan, A two-stage supply chain problem with fixed costs: an ant colony optimization approach. Int. J. Prod. Econ. 204 (2018) 214–226. | DOI

[11] A. Y. S. Lam and V. O. K. Li, Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14 (2009) 381–399. | DOI

[12] A. Y. S. Lam, V. O. K. Li and J. J. Q. Yu, Real-coded chemical reaction optimization. IEEE Trans. Evol. Comput. 16 (2012) 339–353. | DOI

[13] K. H. Leung, C. K. M. Lee and K. L. Choy, An integrated online pick-to-sort order batching approach for managing frequent arrivals of B2B e-commerce orders under both fixed and variable time-window batching. Adv. Eng. Inf. 45 (2020) 101125. | DOI

[14] J. Li and Q. Pan, Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. Int. J. Prod. Econ. 145 (2013) 4–17. | DOI

[15] W. C. Ling, A. B. Verasingham, V. Andiappan, Y. K. Wan, I. M. L. Chew and D. K. S. Ng, An integrated mathematical optimisation approach to synthesise and analyse a bioelectricity supply chain network. Energy 178 (2019) 554–571. | DOI

[16] I. Mallidis, R. Dekker and D. Vlachos, The impact of greening on supply chain design and cost: a case for a developing region. J. Transp. Geogr. 22 (2012) 118–128. | DOI

[17] D. G. Mogale, S. K. Kumar, F. P. G. Marquez and M. K. Tiwari, Bulk wheat transportation and storage problem of public distribution system. Comput. Ind. Eng. 104 (2017) 80–97. | DOI

[18] F. Mohebalizadehgashti, H. Zolfagharinia and S. H. Amin, Designing a green meat supply chain network: a multi-objective approach. Int. J. Prod. Econ. 219 (2020) 312–327. | DOI

[19] R. Ngambusabongsopa, Z. Li and E. Eldesouky, A hybrid mutation chemical reaction optimization algorithm for global numerical optimization. Math. Probl. Eng. 2015 (2015). DOI: . | DOI

[20] T. T. Nguyen, Z. Li, S. Zhang and T. K. Truong, A hybrid algorithm based on particle swarm and chemical reaction optimization. Expert Syst. Appl. 41 (2014) 2134–2143. | DOI

[21] F. R. Nilsson, A complexity perspective on logistics management: rethinking assumptions for the sustainability era. Int. J. Logist. Manag. 30 (2019) 681–698. | DOI

[22] J. Noh and J. S. Kim, Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. J. Clean. Prod. 208 (2019) 1421–1435. | DOI

[23] I. Nouira, R. Hammami, Y. Frein and C. Temponi, Design of forward supply chains: impact of a carbon emissions-sensitive demand. Int. J. Prod. Econ. 173 (2016) 80–98. | DOI

[24] T. Paksoy, T. Bektaş and E. Özceylan, Operational and environmental performance measures in a multi-product closed-loop supply chain. Transp. Res. Part E Logist. Transp. Rev. 47 (2011) 532–546. | DOI

[25] D. Prajapati, F. Zhou, N. Cheikhrouhou and S. Pratap, Minimizes the time window for delivery of orders in B2B e-commerce. In: Proceedings of the 5th International Conference on Industrial Engineering (ICIE). Sochi, Russia (2019) 18–19.

[26] D. Prajapati, A. R. Harish, Y. Daultani, H. Singhand S. Pratap, A clustering based routing heuristic for last-mile logistics in fresh food e-Commerce. Glob. Bus. Rev. (2020). DOI: . | DOI

[27] D. Prajapati, F. Zhou, M. Zhang, H. Chelladurai and S. Pratap, Sustainable logistics network design for multi-products delivery operations in B2B e-commerce platform. Sādhanā 46 (2021) 1–13. | MR | DOI

[28] A. Ramudhin, A. Chaabane, M. Kharoune and M. Paquet, Carbon market sensitive green supply chain network design. 2008 IEEE Int. Conf. Ind. Eng. Eng. Manag. 5 (2008) 1093–1097. | DOI

[29] H. S. Rao, Reducing carbon emissions from transport projects. Eval. Knowl. Br. 16 (2010) 1–96.

[30] Scenario Building Team, Long term mitigation scenarios: strategic options for South Africa. Long Term Mitig. Scenar. Scenar. Doc. Deaprtment Environ. Tour. (2007) 34. http://hdl.handle.net/11427/16804.

[31] A. Shamsuzzoha, E. Ndzibah and K. Kettunen, Data-driven sustainable supply chain through centralized logistics network: case study in a Finnish pharmaceutical distributor company. Curr. Res. Environ. Sustain. 2 (2020) 100013. | DOI

[32] K. Shaw, M. Irfan, R. Shankar and S. S. Yadav, Low carbon chance constrained supply chain network design problem: a benders decomposition based approach. Comput. Ind. Eng. 98 (2016) 483–497. | DOI

[33] M. Sreenivas and T. Srinivas,, The Role of Transportation in Logistics Chain. Alluri Institute of Management Science, Warangal. AP India (1814) 1–10.

[34] S. K. Srivastava, Green supply-chain management: a state-of-the-art literature review. Int. J. Manag. Rev. 9 (2007) 53–80. | DOI

[35] A. Stefaniec, K. Hosseini, J. Xie and Y. Li, Sustainability assessment of inland transportation in China: a triple bottom line-based network DEA approach. Transp. Res. Part D Transp. Environ. 80 (2020) 102258. | DOI

[36] Y. Tan, Y. Shi, F. Buarque, A. Gelbukh, S. Das and A. Engelbrecht, Advances in swarm and computational intelligence. In: 6th International Conference, ICSI 2015 held in conjunction with the Second BRICS Congress, CCI 2015 Beijing, China, June 25–28, 2015 Proceedings, Part II. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinf.). Vol. 9141 (2015) 229–237.

[37] J. Tang, H. Heinimann, K. Han, H. Luo and B. Zhong, Evaluating resilience in urban transportation systems for sustainability: a systems-based Bayesian network model. Transp. Res. Part C Emerg. Technol. 121 (2020) 102840. | DOI

[38] P. Taylor, V. V. Panicker, R. Vanga and R. Sridharan, Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge. Int. J. Prod. Res. 51 (2013) 698–717. | DOI

[39] C. J. Vidal and M. Goetschalckx, Strategic production-distribution models: a critical review with emphasis on global supply chain models. Eur. J. Oper. Res. 98 (1997) 1–18. | Zbl | DOI

[40] Y. Wang, Y. Sun, X. Guan and Y. Guo, Two-echelon location-routing problem with time windows and transportation resource sharing. J. Adv. Transp. 2021 (2021). DOI: . | DOI

[41] S. X. Xu, M. Cheng and G. Q. Huang, Efficient intermodal transportation auctions for B2B e-commerce logistics with transaction costs. Transp. Res. Part B 80 (2020) 322–337. | DOI

[42] P. Yang and L. Zeng, Models and methods for two-echelon location routing problem with time constraints in city logistics. Math. Probl. Eng. 2018 (2018). DOI: . | DOI

[43] M. Zhang, S. Pratap, G. Q. Huang and Z. Zhao, Optimal collaborative transportation service trading in B2B e-commerce logistics. Int. J. Prod. Res. 7543 (2017) 1–17.

[44] M. Zhang, L. Chen and X. Chen, An advanced chemical reaction optimization algorithm based on balanced local and global search. Math. Prob. Eng. 2018 (2018). DOI: . | DOI | MR | Zbl

[45] M. Zhang, S. Pratap, Z. Zhao, D. Prajapati and G. Q. Huang, Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics. Int. J. Prod. Res. 59 (2021) 6291–6310. | DOI

Cité par Sources :