A Bi-objective Cold Supply Chain for Perishable Products Considering Quality Aspects: A Case Study in Iran Dairy Sector

Document Type : Original Article


1 Department of Industrial Engineering, Shahed University, Tehran, Iran

2 Department of Industrial Engineering and management, Shahrood University of Technology, Shahrood, Iran


Improper handling in the food cold supply chain may compromise food safety and reduce quality, which can lead to economic losses and undesirable effects on food accessibility. Therefore, designing an efficient and reliable cold supply chain is extremely important for the company, suppliers, customers, and society. The majority of the traditional studies in the supply chain do not consider the cost of quality (prevention, appraisement, and failure) in supply chain network design. In this study, all dimensions of the cost of quality in a cold supply chain design such as the cost of quality related to suppliers and the cost of distribution service quality are investigated to close the problem to real-world conditions. Moreover, the quality of suppliers, manufacturers, and distributors is simultaneously considered throughout a supply chain with a new approach. To this end, the problem is formulated as a mathematical model for multi-item and multi-period cases considering two objective functions. The first objective function minimizes the total expected costs and the second objective function maximizes the total quality of the supply chain. The proposed bi-objective model has been transformed into a single-objective model by the solution of the parametric method (normalized weighted summation) and solved for a medium-sized instance considering data of a real-world case study. Computational results and analyzes indicate the efficiency of the proposed model as well as the exact solution method available for small and medium scales. The real case study which involves Kaleh Dairy Company is conducted to illustrate the potential of the proposed model and proper sensitivity analyses.


Main Subjects

  1. Huff, D.L., "Defining and estimating a trading area", Journal of Marketing, Vol. 28, No. 3, (1964), 34-38, doi.org/10.1177/002224296402800307.
  2. Geoffrion, A.M. and Graves, G.W., "Multicommodity distribution system design by benders decomposition", Management Science, Vol. 20, No. 5, (1974), 822-844, doi.org/10.1287/mnsc.20.5.822.
  3. Franca, R.B., Jones, E.C., Richards, C.N. and Carlson, J.P., "Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality", International Journal of Production Economics, Vol. 127, No. 2, (2010), 292-299, doi.org/10.1016/j.ijpe.2009.09.005.
  4. Chao, G.H., Iravani, S.M. and Savaskan, R.C., "Quality improvement incentives and product recall cost sharing contracts", Management Science, Vol. 55, No. 7, (2009), 1122-1138, doi.org/10.1287/mnsc.1090.1008.
  5. Zhu, K., Zhang, R.Q. and Tsung, F., "Pushing quality improvement along supply chains", Management Science, Vol. 53, No. 3, (2007), 421-436, doi.org/10.1287/mnsc.1060.0634.
  6. Mohammadian, M., Babaei, M., Amin Jarrahi, M. and Anjomrouz, E., "Scheduling nurse shifts using goal programming based on nurse preferences: A case study in an emergency department", International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 7, (2019), 954-963, doi.org/10.5829/IJE.2019.32.07A.08.
  7. Schiefer, G., "Computer support for tracking, tracing and quality assurance schemes in commodities", Journal für Verbraucherschutz und Lebensmittelsicherheit, Vol. 1, No. 2, (2006), 92-96, doi.org/10.1007/s00003-006-0016-3.
  8. Moradian, E., "Integrating web services and intelligent agents in supply chain for securing sensitive messages", in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Springer. (2008), 771-778, doi.org/10.1007/978-3-540-85567-5_96.
  9. Canbolat, Y.B., Gupta, G., Matera, S. and Chelst, K., "Analysing risk in sourcing design and manufacture of components and sub-systems to emerging markets", International Journal of Production Research, Vol. 46, No. 18, (2008), 5145-5164, doi.org/10.1080/00207540701266807.
  10. Wu, D.D. and Olson, D.L., "Enterprise risk management: Small business scorecard analysis", Production Planning and Control, Vol. 20, No. 4, (2009), 362-369, doi.org/10.1080/09537280902843706.
  11. Lin, C.-Y., "Determinants of the adoption of technological innovations by logistics service providers in china", International Journal of Technology Management & Sustainable Development, Vol. 7, No. 1, (2008), 19-38, doi.org/10.1386/ijtm.7.1.19_1.
  12. Kameli, A., Javadian, N. and Daghbandan, A., "Multi-period and multi-objective stock selection optimization model based on fuzzy interval approach", International Journal of Engineering, Transactions C: Aspects, Vol. 32, No. 9, (2019), 1306-1311, doi.org/10.5829/IJE.2019.32.09C.11.
  13. Ahmadi-Javid, A. and Hoseinpour, P., "A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints", Transportation Research Part E: Logistics and Transportation Review, Vol. 82, (2015), 238-255
  14. Hsu, C.C., Kannan, V.R., Tan, K.C. and Leong, G.K., "Information sharing, buyer‐supplier relationships, and firm performance: A multi‐region analysis", International Journal of Physical Distribution & Logistics Management, (2008), doi.org/10.1108/09600030810875391.
  15. Ren, Z.J. and Zhou, Y.-P., "Call center outsourcing: Coordinating staffing level and service quality", Management Science, Vol. 54, No. 2, (2008), 369-383, doi.org/10.1287/mnsc.1070.0820.
  16. Hsieh, C.-C. and Lu, Y.-T., "Manufacturer’s return policy in a two-stage supply chain with two risk-averse retailers and random demand", European Journal of Operational Research, Vol. 207, No. 1, (2010), 514-523, doi.org/10.1016/j.ejor.2010.04.026.
  17. Mendes, A.A. and Lorenzoni, M.W., "Analysis and optimization of periodic inspection intervals in cold standby systems using monte carlo simulation", Journal of Manufacturing Systems, Vol. 49, (2018), 121-130, doi.org/10.1016/j.jmsy.2018.09.006.
  18. Mejjaouli, S. and Babiceanu, R.F., "Cold supply chain logistics: System optimization for real-time rerouting transportation solutions", Computers in Industry, Vol. 95, (2018), 68-80, doi.org/10.1016/j.compind.2017.12.006.
  19. Zhang, S., Chen, N., Song, X. and Yang, J., "Optimizing decision-making of regional cold chain logistics system in view of low-carbon economy", Transportation Research Part A: Policy and Practice, Vol. 130, (2019), 844-857, doi.org/10.1016/j.tra.2019.10.004.
  20. Qin, G., Tao, F. and Li, L., "A vehicle routing optimization problem for cold chain logistics considering customer satisfaction and carbon emissions", International Journal of Environmental Research and Public Health, Vol. 16, No. 4, (2019), 576, doi.org/10.3390/ijerph16040576.
  21. Zhang, L.-Y., Tseng, M.-L., Wang, C.-H., Xiao, C. and Fei, T., "Low-carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm", Journal of Cleaner Production, Vol. 233, (2019), 169-180, doi.org/10.1016/j.jclepro.2019.05.306.
  22. Goodarzian, F., Hosseini-Nasab, H. and Fakhrzad, M., "A multi-objective sustainable medicine supply chain network design using a novel hybrid multi-objective metaheuristic algorithm", International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 10, (2020), 1986-1995, doi.org/10.5829/IJE.2020.33.10A.17.
  23. Al Theeb, N., Smadi, H.J., Al-Hawari, T.H. and Aljarrah, M.H., "Optimization of vehicle routing with inventory allocation problems in cold supply chain logistics", Computers & Industrial Engineering, Vol. 142, (2020), 106341, doi.org/10.1016/j.cie.2020.106341.
  24. Qi, C. and Hu, L., "Optimization of vehicle routing problem for emergency cold chain logistics based on minimum loss", Physical Communication, Vol. 40, (2020), 101085, doi.org/10.1016/j.cie.2020.106341.
  25. Shafiee, F., Kazemi, A., Chaghooshi, A.J., Sazvar, Z. and Mahdiraji, H.A., "A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry", Journal of Cleaner Production, Vol. 294, (2021), 126230, doi.org/10.1016/j.jclepro.2021.126230.
  26. Theophilus, O., Dulebenets, M.A., Pasha, J., Lau, Y.-y., Fathollahi-Fard, A.M. and Mazaheri, A., "Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations", Computers & Industrial Engineering, (2021), 107240, doi.org/10.1016/j.cie.2021.107240.
  27. Wang, M., Wang, Y., Liu, W., Ma, Y., Xiang, L., Yang, Y. and Li, X., "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?", Physica A: Statistical Mechanics and its Applications, Vol. 566, (2021), 125637, doi.org/10.1016/j.physa.2020.125637.
  28. Saif, A. and Elhedhli, S., "Cold supply chain design with environmental considerations: A simulation-optimization approach", European Journal of Operational Research, Vol. 251, No. 1, (2016), 274-287, doi.org/10.1016/j.ejor.2015.10.056.
  29. Hariga, M., As' ad, R. and Shamayleh, A., "Integrated economic and environmental models for a multi stage cold supply chain under carbon tax regulation", Journal of Cleaner Production, Vol. 166, (2017), 1357-1371, doi.org/10.1016/j.jclepro.2017.08.105.
  30. Babagolzadeh, M., Shrestha, A., Abbasi, B., Zhang, Y., Woodhead, A. and Zhang, A., "Sustainable cold supply chain management under demand uncertainty and carbon tax regulation", Transportation Research Part D: Transport and Environment, Vol. 80, (2020), 102245, doi.org/10.1016/j.trd.2020.102245.
  31. Li, X. and Zhou, K., "Multi-objective cold chain logistic distribution center location based on carbon emission", Environmental Science and Pollution Research, (2021), 1-9, doi.org/10.1007/s11356-021-12992-w.
  32. Fasihi, M., Tavakkoli-Moghaddam, R., Najafi, S.E. and Hajiaghaei-Keshteli, M., "Developing a bi-objective mathematical model to design the fish closed-loop supply chain", International Journal of Engineering, Transactions B: Applications, Vol. 34, No. 5, (2021), 1257-1268. doi: 10.5829/ije.2021.34.05b.19
  33. Zhao, B., Gui, H., Li, H. and Xue, J., "Cold chain logistics path optimization via improved multi-objective ant colony algorithm", IEEE Access, Vol. 8, (2020), 142977-142995, doi.org/10.1109/ACCESS.2020.3013951.
  34. Ji, Y., Du, J., Wu, X., Wu, Z., Qu, D. and Yang, D., "Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand", Environment, Development and Sustainability, (2021), 1-24, doi.org/10.1007/s10668-021-01236-z.
  35. Bai, X. and Liu, Y., "Robust optimization of supply chain network design in fuzzy decision system", Journal of Intelligent Manufacturing, Vol. 27, No. 6, (2016), 1131-1149, doi.org/10.1007/s10845-014-0939-y.