Modeling a Green Inventory-Routing Problem with Hybrid Vehicles and Solving With Large Neighbourhood Search Algorithms

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 MSc. Grad., Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

The routing-inventory problem is one of the most commonly used issues in the distribution of goods. This is the result of merge and integration of two classic issues in fact. The first issue is related to the routing which determines the rout of the vehicles between several locations. The next issue is in the field of inventory, which plans and defines the inventory policies according to the maintenance costs and scarcity. Dynamics is one of the features of this issue which states that the above decisions are time-dependent and should be repeated several times during the horizon. On the other hand, today, the debate on the use of vehicles with electric and hybrid fuels has found a special place in green distribution systems. In this research, it has been attempted to develop the issue of green routing-inventory considering the hybrid fleet by combining two approaches. For this purpose, the mathematical model of the green inventory routing problem with consideration the hybrid fleet is presented at first, and then a big neighborhood search algorithm has been used in real dimensions in order to solve the model. The GAMS software has been used to check the validity of the proposed mathematical model and the results of the proposed algorithm have compared with the results of the exact method in order to validate the algorithm. Then the results of the implementation of the meta-algorithm was presented for a sample of generated issues and analyzed. The reported results confirm the proper functioning of the proposed algorithm. Finally, sensitivity analysis has been performed on the problem parameters

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References

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History

  • Receive Date: 27 April 2018
  • Revise Date: 01 August 2018
  • Accept Date: 04 August 2018

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