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A genetic algorithm for fuzzy random and low-carbon integrated forward/reverse logistics network design

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
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Abstract

Considering the influence of carbon emissions trading, the fuzzy stochastic programming model was established to cut back the total cost of carbon trading balance. Modeling this chain is carried out by accounting for carbon cap-and-trade considerations and total cost optimization. In this paper, we analyze the low-carbon integrated forward/reverse logistics network and made relevant simulation tests. The results show that the changes of the confidence level and carbon emission limits have obvious influences on logistics costs. If the emission limit is large, carbon trading mechanism has little effect on the total logistics cost in the same scenario. Therefore, the government needs to use the appropriate emission limits to guide enterprises to reduce carbon emissions, and enterprises can make coping strategies according to the different limit at the same time. Therefore, the fuzzy random programming model proposed in this paper is practical. Its decision making applying the proposed algorithm is reasonable and applicable and could provide decision basis for enterprise managers.

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References

  1. IPCC (2007) The physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  2. Carbon, Trust (2006) Carbon Footprint in the supply chain: the next step for business. Report No. CTC616, Carbon Trust London

  3. Daskin MS, Benjaafar S (2010) National science foundation symposium on low carbon supply chain final. NSF Symposium report, USA

  4. Guo J, Wang X, Fan S et al (2017) Forward and reverse logistics network and route planning under the environment of low-carbon emissions: a case study of shanghai fresh food E-commerce enterprises. Comput Ind Eng 106(C):351–360

    Article  Google Scholar 

  5. Daryanto Y, Wee HM, Astanti RD (2019) Three-echelon supply chain model considering carbon emission and item deterioration. Transp Res Part E Logist Transp Rev 122:368–383

    Article  Google Scholar 

  6. Wang J, Lim MK, Tseng M, Yang Y (2019) Promoting low carbon agenda in the urban logistics network distribution system. J Clean Prod 211:146–160

    Article  Google Scholar 

  7. Jabbarzadeh A, Haughton M, Khosrojerdi A (2018) Closed-loop supply chain network design under disruption risks: a robust approach with real world application. Comput Ind Eng 116:178–191

    Article  Google Scholar 

  8. Liao TY (2018) Reverse logistics network design for product recovery and remanufacturing. Appl Math Model 60:145–163

    Article  MathSciNet  MATH  Google Scholar 

  9. Schiffer M, Walther G (2018) Strategic planning of electric logistics fleet networks: a robust location-routing approach. Omega 80:31–42

    Article  Google Scholar 

  10. Amin S, Ramezanian R (2018) An efficient hybrid genetic algorithm for multi-product competitive supply chain network design with price-dependent demand. Appl Soft Comput 71:872–893

    Article  Google Scholar 

  11. Mahdi PM, Marjan O (2018) Designing and solving a reverse logistics network for polyethylene terephthalate bottles. J Clean Prod 195:605–617

    Article  Google Scholar 

  12. Jing L, Zhong Y, Futao Z et al (2018) A novel method to solve supplier selection problem: hybrid algorithm of genetic algorithm and ant colony optimization. Math Comput Simul 156:294–309

    MathSciNet  Google Scholar 

  13. Penkuhn T, Spengler T, Puchert H, Rentz O (1997) Environmental integrated production planning for ammonia synthesis. Eur J Oper Res 97(2):327–336

    Article  MATH  Google Scholar 

  14. Cherrafi A, Garza-Reyes JA, Kumar V et al (2018) Lean, green practices and process innovation: a model for green supply chain performance. Int J Prod Econ 206:79–92

    Article  Google Scholar 

  15. Mtalaa W, Aggoune R, Schaefers J (2009) CO2 emission calculation models for green supply chain management . http://coba.georgiasouthern.edu/hanna/FullPapers/Fullpaper.htm. Accessed 05 April 2010

  16. Wong EYC, Tai AH, Emma Z (2018) Optimizing truckload operations in third-party logistics: a carbon footprint perspective in volatile supply chain. Transp Res Part D Transp Environ 63:649–661

    Article  Google Scholar 

  17. Chen JX, Chen J (2017) Supply chain carbon foot printing and responsibility allocation under emission regulations. J Environ Manag 188:255–267

    Article  Google Scholar 

  18. Zhang D, Zhan Q, Chen Y et al (2018) Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO2 emission reduction targets. Transp Res Part D Transp Environ 60:174–190

    Article  Google Scholar 

  19. Ding H, Liu Q, Zheng L (2016) Assessing the economic performance of an environmental sustainable supply chain in reducing environmental externalities. Eur J Oper Res 255(2):463–480

    Article  MathSciNet  MATH  Google Scholar 

  20. Ding H, Zhao Q, An Z et al (2016) Collaborative mechanism of a sustainable supply chain with environmental constraints and carbon caps. Int J Prod Econ 181:191–207

    Article  Google Scholar 

  21. Li F, Haasis HD (2017) Imposing emission trading scheme on supply chain: separate- and joint implementation. J Clean Prod 142:2288–2295

    Article  Google Scholar 

  22. Bai Q, Gong Y, Jin M, Xu X (2019) Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory. Int J Prod Econ 208:83–99

    Article  Google Scholar 

  23. Mohan MN, Peter K (2019) Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand. Eur J Oper Res 272(1):147–161

    Article  MathSciNet  MATH  Google Scholar 

  24. Xiao Z, Sun J, Shu W, Wang T (2019) Location-allocation problem of reverse logistics for end-of-life vehicles based on the measurement of carbon emissions. Comput Ind Eng 127:169–181

    Article  Google Scholar 

  25. Zaid AA, Jaaron AAM, Abdul TB (2018) The impact of green human resource management and green supply chain management practices on sustainable performance: an empirical study. J Clean Prod 204:965–979

    Article  Google Scholar 

  26. Ruiz-Benitez R, López C, Juan C, Real JC (2017) Environmental benefits of lean, green and resilient supply chain management: the case of the aerospace sector. J Clean Prod 167:850–862

    Article  Google Scholar 

  27. Fahimnia B, Jabbarzadeh A, Sarkis J (2018) Greening versus resilience: a supply chain design perspective. Transp Res Part E Logist Transp Rev 119:129–148

    Article  Google Scholar 

  28. Chang X, Xia H, Zhu H et al (2015) Production decisions in a hybrid manufacturing–remanufacturing system with carbon cap and trade mechanism. Int J Prod Econ 162:160–173

    Article  Google Scholar 

  29. Lang X, Chuanxu W, Junjie Z (2018) Decision and coordination in the dual-channel supply chain considering cap-and-trade regulation. J Clean Prod 197:551–561

    Article  Google Scholar 

  30. Comasmarti J, Tancrez JS, Seifert RW (2015) Carbon footprint and responsiveness trade-offs in supply chain network design. Int J Prod Econ 166:129–142

    Article  Google Scholar 

  31. Abbassi A, Elhilali Alaoui A, Boukachour J (2019) Robust optimization of the intermodal freight transport problem: modeling and solving with an efficient hybrid approach. J Comput Sci 30:127–142

    Article  Google Scholar 

  32. Asim Z, Jalil SA, Javaid S (2019) An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sustain Prod Consum 17:298–310

    Article  Google Scholar 

  33. Andres G, Laurence D, Nacima L et al (2018) A multi-population algorithm to solve the VRP with stochastic service and travel times. Comput Ind Eng 125:144–156

    Article  Google Scholar 

  34. Langroodi RRP, Amiri M (2016) A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty. Expert Syst Appl 51:231–244

    Article  Google Scholar 

  35. Noh J, Kim JS (2016) Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. J Clean Prod 208:1421–1435

    Article  Google Scholar 

  36. He F, Yang J, Li M (2018) Vehicle scheduling under stochastic trip times: an approximate dynamic programming approach. Transp Res Part C Emerg Technol 96:144–159

    Article  Google Scholar 

  37. Wei M, Chen X, Sun B, Zhu YY (2015) Model and algorithm for resolving regional bus scheduling problems with fuzzy travel times. J Intell Fuzzy Syst 29(6):2689–2696

    Article  Google Scholar 

  38. Wang C, Matthies HG, Xu M et al (2018) Hybrid reliability analysis and optimization for spacecraft structural system with random and fuzzy parameters. Aerosp Sci Technol 77:353–361

    Article  Google Scholar 

  39. John H (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Google Scholar 

  40. Khanduzi R, Sangaiah AK (2019) A fast genetic algorithm for a critical protection problem in biomedical supply chain networks. Appl Soft Comput 75:162–179

    Article  Google Scholar 

  41. Senoussi A, Dauzère-Pérès S, Brahimi N et al (2018) Heuristics based on genetic algorithms for the capacitated multi vehicle production distribution problem. Comput Op Res 96:108–199

    Article  MathSciNet  MATH  Google Scholar 

  42. Cui YY, Guan Z, Saif U et al (2017) Close loop supply chain network problem with uncertainty in demand and returned products: genetic artificial bee colony algorithm approach. J Clean Prod 162:717–742

    Article  Google Scholar 

  43. Woo YB, Kim BS (2018) A genetic algorithm-based metaheuristic for hydrogen supply chain network problem with two transportation modes and replenishment cycles. Comput Ind Eng (In press)

  44. Mitsuo G, Lin L, Youngsu Y et al (2018) Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design. Comput Ind Eng 125:394–412

    Article  Google Scholar 

  45. Dai Z, Aqlan F, Zheng X et al (2018) A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Comput Ind Eng 119:338–352

    Article  Google Scholar 

  46. Afrouzy ZA, Nasseri SH, Mahdavi I (2016) A genetic algorithm for supply chain configuration with new product development. Comput Ind Eng 101:440–454

    Article  Google Scholar 

  47. Chakraborty D, Jana DK, Roy TK (2015) Multi-item integrated supply chain model for deteriorating items with stock dependent demand under fuzzy random and bifuzzy environments. Comput Ind Eng 88:166–180

    Article  Google Scholar 

  48. Liu B (2001) Fuzzy random chance-constrained programming. IEEE Trans Fuzzy Syst 9(5):713–720

    Article  Google Scholar 

  49. Ma Y, Yan F, Kang K et al (2016) A novel integrated production-distribution planning model with conflict and coordination in a supply chain network. Knowl-Based Syst 105:119–133

    Article  Google Scholar 

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Acknowledgements

This research is supported by the National Nature Science Foundation of China (Project No: 71373157).

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Authors and Affiliations

  1. School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China

    Yangjun Ren, Chuanxu Wang, Chao Yu & Suyong Zhang

  2. College of Port and Shipping Management, Guangzhou Maritime University, Guangzhou, 510725, China

    Botang Li

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  1. Yangjun Ren
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  2. Chuanxu Wang
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  3. Botang Li
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  4. Chao Yu
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  5. Suyong Zhang
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Correspondence to Botang Li.

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Ren, Y., Wang, C., Li, B. et al. A genetic algorithm for fuzzy random and low-carbon integrated forward/reverse logistics network design. Neural Comput & Applic 32, 2005–2025 (2020). https://doi.org/10.1007/s00521-019-04340-4

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