Proposing a model for a resilient supply chain: A meta-heuristic algorithm

Document Type : Original Article

Authors

1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

3 School of Stratagy and Leadership, Faculty of Business and law, Coventru University, Coventry, UK

Abstract

The resilient supply chain considers many capabilities for companies to overcome financial crises and to supply and distribute products. In this study, we address the allocation of inventory distribution for a distribution network, including a factory, a number of potential locations for distribution centers and a number of retailers. Customers demand is assumed to be certain and deterministic for all periods but time varying in the limited planning horizon. The proposed model in this research is a linear complex integer programming model with two-objective functions. The first objective function minimizes the total costs of the entire distribution system in the planning horizon, and the second objective function seeks to minimize the difference between the maximum and minimum distances traveled by vehicles over the planning horizon. Therefore, the model tries to satisfy the demand and at the same time reduce costs using the best route transportation option configuration and transportation option. The routing problem is developed, and as the problem is a NP-hard problem, a meta-heuristic method is used to solve it. In this model, the demand volume for each customer in a period of the network, vehicle capacity, factory capacity, constant transportation cost, variable transportation cost, etc., are considered as factors affecting the model. The results show that the model proposed in the network can be used as a lever to improve the performance of the financial economic supply network through saving in routes.

Keywords

References

  1. Mills J., Schmitz J., Frizelle G., “A strategic review of supply networks”, International Journal of Operations and Production Management, Vol. 24, (2004), 1012-1036.
  2. Pfohl H. C., Köhler. H., Thomas D., “State of the art in supply chain risk management research: empirical and conceptual findings and a roadmap for the implementation in practice”, Logistics Research, Vol. 2, (2010), 33-44, doi: 10.1590/0104-530X3800-20.
  3. Mensah P., Merkuryev Y., “Developing a resilient supply Chain”, Procedia-Social and Behavioral Sciences, Vol. 110, (2014), 309-319, doi: 10.1108/09600030410545436.
  4. Christopher M., “Creating resilient supply chains”, Logistics Europe, Vol.12, (2014), 14-21.
  5. Rajesh R., “Supplier selection in resilient supply chains: a grey relational analysis approach”, Journal of Cleaner Production, Vol. 86, (2015), 343-359, doi:10.1016/j.jclepro.2014.08.054.
  6. Sawik T., “Selection of resilient supply portfolio under disruption risk“, Omega, Vol. 41, (2013), 259-269, doi: 10.1016.
  7. Melnyk. S, Closs. D. J, Griffis.S. E, Zobel. C. W, Macdonald. J. R, “Understanding supply chain resilience. Supply Chain”, Management Review, Vol. 18, (2014), 34-41.
  8. Delfani F., Kazemi A., Seyedhosseini S. M., Niaki S. T. A.,” A Green Hazardous Waste Location-routing Problem Considering the Risks Associated with Transportation and Population”, International Journal of Engineering, Transctions B: Applications, Vol. 33, No. 11, (2020), 2272-2284, doi: 10.5829/IJE.2020.33.11B.18.
  9. Akbarzadeh Z., Safaei Ghadikolaei. A.H, Madhoushi. M, Aghajani. H, ”A Hybrid Fuzzy Multi-criteria Decision Making Model Based on Fuzzy DEMATEL with Fuzzy Analytical Network Process and Interpretative Structural Model for Prioritizing LARG Supply Chain Practices”, International Journal of Engineering, Transactions C: Aspects, Vol. 32, No. 3, (2019), 413-423. doi: 10.5829/ije.2019.32.03c.09
  10. Hillmann J., Guenther E., “Organizational resilience: a valuable construct for management research?”, International Journal of Management Reviews, Vol. 23, (2020), 7-44, doi:10.1111/ijmr.12239.
  11. Fasey K. J., Sarkar M., Wagstaff C. R., Johnston J., “Defining and Characterizing organizational resilience in elite sport”, Psychology of Sport and Exercise, Vol. 52, (2021), 101834, doi: 10.1016.
  12. Douglas S., “Building organizational resilience through human capital management strategy”, Development and Learning in Organizations: An International Journal, (2021), doi: 10.1108.
  13. Ungar M., “Multisystemic resilience: Adaptation and transformation in contexts of change”, Oxford University Press: USA, 2021.
  14. Masten A. S., “Resilience in developing systems: The promise of integrated approaches”, European Journal of Developmental Psychology, Vol. 13, (2016), 297-312, doi: 10.1080/17405629.
  15. Orth D., Schuldis. P. M., “Organizational learning. and unlearning capabilities for resilience during COVID-19”, The Learning Organization, (2021), doi: 10.1108.
  16. Ngoc Su. D., Luc Tra. D., Thi Huynh H. M., Nguyen H. H. T., O’Mahony. B, “Enhancing resilience in theCovid-19 crisis: lessons from human resource manage ment pratices in Vietnam”, Current Issue in Tourism, (2021), 1-17, doi: 10.1080/13683500.
  17. Khan I. U., Hameed Z., Hamayun M., “Investigating the acceptance of electronic banking in the rural areas of Pakistan: An application of the unified model”, Business and Economic Review, Vol. 11, (2019),  57-87, doi: 0.22547/BER/11.3.3.
  18. Tallaki M., Bracci E., “Risk Perception, Accounting, and Resilience in Public Sector Organizations: A Case Study Analysis”, Journal of Risk and Financial Management, Vol. 14, (2021), doi: 10.3390/jrfm14010004.
  19. Soni U., Jain V., Kumar S.,“ Measuring supply chain resilience using a deterministic modeling approach”, Computers & Industrial Engineering, Vol. 74, (2014), 11-25, doi: 10.1016/.
  20. Jalali G., Tavakoli-Moghaddam R., Ghomi-Avill. M, Jabbarzadeh. A,” A Network Design Model for a Resilient Closed- Loop Supply Chain with Lateral Transshipment”, International Journal of Engineering, Vol. 30, (2017), 374-383.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

21.   Wei R., Liu C., ”Research on carbon emission reduction in road freight transportation sector based on regulation-compliant route optimization model and case study”, Sustainable Computing: Informatics and Systems, Vol. 28, (2022), doi: 10.1016.100408.

22.   Kuang Z., Lian Z., Lien J. W., Zheng J., “Serial and parallel duopoly competition in multi-segment transportation routes”, Transportation Research Part E: Logistics and Transportation Review, Vol. 133, (2020), doi: 10.1016.101821.

  1. Schenekemberg C. M., Scarpin C.T., Pécora Jr. J.E., Guimarães T. A., Coelho L. C., “The two-echelon inventory-routing problem with fleet management”, Computers and Operations Research, Vol. 121, (2020), doi: 10.1016.104944.
  2. Alvarez A., Cordeau J. F., Jans R., Munari P., Morabito R., “Inventory routing under stochastic supply and Demand”, Omega, Vol. 102, (2020), doi:10.1016.102304.
  3. Yavari M., Enjavi H., Geraeli M., “Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products”, Research in Transportation Business & Management, Vol. 37, (2020), doi: 10.1016.100552.
  4. Ji Y., Du J., Han X., Wu X., Huang R., Wang S., Liu Z., “A mixed integer robust programming model for two-echelon inventory routing problem of perishable products”, Physica A: Statistical Mechanics and its Applications, Vol. 548, (2020), doi: 10.1016.124481.
  5. Eide L., Årdal G. C. H., Evsikova N., Hvattum L. M., Urrutia S., “Load-dependent speed optimization in maritime inventoryRouting”, Computers and Operations Research, Vol. 123, (2020), doi:10.1016.105051.
  6. Ortega E. J. A., Schilde M., Doerner K. F., “Matheuristic search techniques for the consistent inventory routing problem with time windows and split deliveries”, Operations Research Perspectives, Vol. 7, (2020), doi:10.1016.100152.
  7. Golsefidi A. H., Jokar M. R. A., “A robust optimization approach for the production-inventory-routing problem with simultaneous pickup and delivery”, Computers & Industrial Engineering, Vol. 143, (2020), doi: 10.1016.106388.
  8. Pitakaso R., Sethanan K., Theeraviriya C., “Variable Neighborhoodstrategy adaptive search for solving green 2-echelon location routing problem”, Computers and Electronics in Agriculture, Vol. 173, (2020), doi:10.1016.105406.
  9. Trachanatzi D., Rigakis M., Marinaki M., Marinakis Y.,“A firefly algorithm for the environmental prize-collecting vehicle routing problem”, Swarm and Evolutionary Computation, Vol. 57, (2020), doi:10.1016.100712.
  10. Wei X., Qiu H., Wang D., Duan J., Wang Y., Cheng. T. C. E., “An integrated location-routing problem with post-disaster relief Distribution”, Computers and Industrial Engineering, (2020), 147, doil:10.1016.106632.
International Journal of Engineering
Volume 34, Issue 12
TRANSACTIONS C: Aspects
December 2021
Pages 2566-2577

Files

Cited by

Share

How to cite

Statistics