Multi-Period Multi-Product Distribution Network Redesign under Uncertainty

Document Type : 15th IIEC conference selected papers

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22070/jqepo.2021.5479.1157

Abstract

– Reviewing related studies to supply chain networks shows that various factors influence the supply chain network's optimal design. Demand changes in market zones, changes in customers' locations, various competitive conditions in different regions, network costs fluctuations, and different disruption pose risks to the supply chain network in terms of optimal condition. In such conditions, to help the network return to its optimality, the network needs to be redesigned. In this respect, this paper has formulated a multi-period multi-product mathematical model for redesigning the warehouses considering backorder shortage. In this regard, decisions such as the construction of new facilities, closing non-optimal facilities, increasing the active facilities' capacity, the optimal amount of order/shortage in each active warehouse in each period have been investigated. Due to the inherent uncertainty in real-world business, parameters related to cost and demand are considered uncertain. Then, to deal with uncertain parameters in the proposed model, Jimenez's possibilistic programming has been used. The obtained results show that redesigning the supply chain network by setting up new facilities/closing non-optimal facilities can significantly reduce inventory and transportation costs and shortages.

Keywords

References

Analia, M., Vecchietti, A. R., Harjunkoski, I., & Grossmann, I. E. (2014). Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I : MINLP and MILP models. Computers and Chemical Engineering, 62, pp. 194–210.
Asl-Najafi, J., et all. (2017). A dynamic closed- loop location-inventory problem under disruption risk. Computers & Industrial Engineering, 90, pp. 414–428.
Correia, I., & Melo, T. (2017). A multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment. Computers & Industrial Engineering, 110, 307-321.
Cortinhal, M. J. , Lopes, M. J. , & Melo, M. T. (2015). Dynamic design and redesign of multi-echelon, multi-product logistics networks with outsourcing opportunities: A computational study. Computers & Industrial Engineering, 90, pp. 118–131.
Feitó-Cespón, M., Sarache, W., Piedra-Jiminez, F., & Cespón-Castro, R. 2017, Redesign of a sustainable reverse supply chain under uncertaintyA case study , Journal Of Cleaner production Production, 151, pp. 206–217.
Farahani, M., Shavandi, H., and Rahmani, D. (2017). A Location-Inventory Model Considering a Strategy to Reduce Disruption Risk in Supply Chain by substitutable products. Computers & Industrial Engineering, 108, pp. 213–224.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2018). A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Computers & Operations Research, 100, 314-332.
Gitinavard, H., & Akbarpour Shirazi, M. (2018). An extended intuitionistic fuzzy modified group complex proportional assessment approach. Journal of Industrial and Systems Engineering, 11(3), 229-246.
Gitinavard, H., & Zarandi, M. H. F. (2016). A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337-345.
Gitinavard, H., & Akbarpour Shirazi, M. (2018). An extended intuitionistic fuzzy modified group complex proportional assessment approach. Journal of Industrial and Systems Engineering, 11(3), 229-246.
Gitinavard, H., Akbarpour Shirazi, M., & Ghodsypour, S. H. (2019). A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty. scientiairanica, 26(5), 2952-2970.
Hammami, R. & Frein Y. (2014). Redesign of global supply chains with integration of transfer pricing : Mathematical modeling and managerial insights. International Journal of Production Economics, 158, pp. 267–277.
Jena, S. D., Cordeau, J., & Gendron, B. (2015). Dynamic Facility Location with Generalized Modular Capacities. Transportation Science, 49(3), pp. 484–49.9.
Jahani, H., Abbasi, B., Alavifard, F., & Talluri, S. (2018). Supply chain network redesign with demand and price uncertainty. International Journal of Production Economics, 205, 287-312.
Jena, S. D. , Cordeau, J. F. , & Gendron, B. (2016). Solving a dynamic facility location problem with partial closing and reopening. Computers & Operations Research, 67, pp. 143–154.
Khatami, M., et all. (2015). Benders' decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. TransportationResearchPartE,79, pp.1–21.
Melo, T., & Nickel, S .(2014). An efficient hdseuristic approach for a multi-period logistics network redesign problem.Operations Research Proceedings, 22(1), pp.80-108. 
Melo, M. T., et all. (2011). A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. Interntional Journal of Production Economics, 136(1), pp.218–230.
Mousazadeh, M., Torabi, S. A., Pishvaee, M. S., & Abolhassani, F. (2018). Accessible, stable, and equitable health service network redesign: A robust mixed possibilistic-flexible approach. Transportation Research Part E: Logistics and Transportation Review, 111, 113-129.
Paz, J., Orozco, J., Salinas, J., Buriticá, N., & Escobar, J. (2015). Redesign of a supply network by considering stochastic demand. International Journal of Industrial Engineering Computations, 6(4), 521-528.
Peeters, D. (2001). On solving complex multi-period location models using simulated annealing. European Journal of Operational Research, 130, pp. 190–201.
Razmi, J., Zahedi-Anaraki, A., & Zakerinia, M. (2013). A bi-objective stochastic optimization model for reliable warehouse network redesign. Mathematical and Computer Modelling , 58(11-12), pp. 1804–1813.
Razm, S., Nickel, S., & Sahebi, H. (2019). A multi-objective mathematical model to redesign of global sustainable bioenergy supply network. Computers & Chemical Engineering, 128, 1-20.
Razm, S., Nickel, S., & Sahebi, H. (2019). A multi-objective mathematical model to redesign of global sustainable bioenergy supply network. Computers & Chemical Engineering, 128, 1-20.
Santoso, T., Ahmed, S., Goetschalckx, M., and Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167, pp. 96–115.
Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 54(5), 1358-1387.
Vakili, R., Akbarpour Shirazi, M., & Gitinavard, H. (2020). Multi-echelon green open-location-routing problem: A robust-based stochastic optimization approach. Scientia Iranica.
Journal of Quality Engineering and Production Optimization
Volume 5, Issue 2
December 2020
Pages 145-162

Files

Share

How to cite

Statistics