Inventory Routing Problem for Perishable Products in a Closed-Loop Supply Chain
Decisions about transportation routing, location of logistics centers and reference points in material and product inventory control are important decisions in the supply chain that with coordination and planning together, they can be effective in reducing cost. One of the effective factors in these decisions is the restrictions caused by product corruption. The purpose of this research is to provide a mathematical model to solve the inventory routing problem in a closed-loop supply chain by considering the perishability of goods.
In this research, after presenting the assumptions in the environment of the problem, a linear programming model has been presented to deal with the routing-inventory problem of perishable products. The problem is solved based on a number of sample data in small dimensions with precise algorithms. Due to the computational complexity of the mentioned problem, two meta-heuristic methods of genetic algorithm and mass optimization of particles have been used to solve it on a large scale.
The findings of the research showed that although the solving time is less in the genetic algorithm, the mass particle optimization algorithm has a better efficiency in solving the problem of this research and produces better quality answers compared to the genetic algorithm.
The mathematical model developed in this research and the proposed algorithms can be used in real situations and play a role in solving the inventory routing problem in the design of a part of the green supply chain for perishable products.
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