Simulation Study for Multi-Echelon Multi-Depot Supply Chain System Using Live Data
DOI:
https://doi.org/10.37266/ISER.2019v7i2.pp116-124Keywords:
Vehicle Routing Problem (VRP), Industry 4.0, SimulationAbstract
The manufacturing industry is eager to implement the advancements of the fourth industrial revolution (Industry 4.0) due to the magnitude of the benefits it can provide. Hence, Industry 4.0 opens a wide avenue for researchers to explore possibilities in the field of the supply chain. This project focuses on building a decision framework for a supply chain system with disruptions. The impact of strategic decisions under the condition of unprecedented events for a vehicle routing problem (VRP) using simulation models is studied here. Those results help the supply chain managers in making sound decisions regarding different scenarios of disruption in VRP. To achieve this, multiple cases under different scenarios of facility disruption are considered. For all cases, the dependent parameter, namely, retailer service level and lost revenue, form the basis of the decision framework. The concept of live data is implemented by making retailer demand, current inventory at the depot, the position of the vehicle in the network and the current number of units in transit as the input data.References
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Worldwide Logistics Chains. LOGI-Scientific Journal on Transport and Logistics. https://doi.org/10.2478/logi-2018-0003
Hadjiconstantinou, E., & Baldacci, R. (1998). A multi-depot period vehicle routing problem arising in the utilities sector. Journal of the Operational Research Society. https://doi.org/10.1057/palgrave.jors.2600641
Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research. https://doi.org/10.1080/00207543.2014.999958
Kagermann, H., Wahlster, W. (German R. C. for A. I., & Helbig, J. (Deutsche P. A. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final Report of the Industrie 4.0 WG. https://doi.org/10.13140/RG.2.1.1205.8966
Kleindorfer, P. R., & Saad, G. H. (2005). Managing Disruption Risks in Global Supply Chains. Production and Operations Management. https://doi.org/10.1017/CBO9781107415324.004
Ratick, S., Meacham, B., & Aoyama, Y. (2008). Locating backup facilities to enhance supply chain disaster resilience. Growth and Change. https://doi.org/10.1111/j.1468-2257.2008.00450.x
Yu, Z., Ouyang, J., Li, S., & Peng, X. (2017). Formal modeling and control of cyber-physical manufacturing systems. Advances in Mechanical Engineering. https://doi.org/10.1177/1687814017725472
Published
2019-12-30
How to Cite
Rajkumar, R., Harikrishnakumar, R., Madhu, A., & Krishnan, K. (2019). Simulation Study for Multi-Echelon Multi-Depot Supply Chain System Using Live Data. Industrial and Systems Engineering Review, 7(2), 116-124. https://doi.org/10.37266/ISER.2019v7i2.pp116-124
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