scholarly journals Pharmaceutical drug two-warehouse inventory model under FIFO dispatching policy using ant colony optimization for travelling salesman problem

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Ajay Singh Yadav ◽  
Veenita Sharma ◽  
Priyanka Agarwal ◽  
Anupam Swami ◽  
Piyush Kumar Yadav

In this paper a deterministic Pharmaceutical drug inventory model for deteriorating items with two level of storage system and time dependent demand with partial backlogged shortages is developed. Stock is transferred RW to OW under bulk release pattern and the transportation cost is taken to be negligibleUnder FIFO dispatching policy Using Ant Colony Optimization for travelling salesman problem. The deterioration rates in both the warehouses are constant but different due to the different preservation proceduresUnder FIFO dispatching policy Using Ant Colony Optimization for travelling salesman problem. Holding cost is considered to be constant up to a definite time and is increases. Ant Colony Optimization for travelling salesman problemwith varying population size is used to solve the model. In this Ant Colony Optimization for travelling salesman problem a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set.The numerical example is presented to demonstrate the development of mode land to validate it. Sensitivity analysis is performed separately for each parameter and Ant Colony Optimization for travelling salesman problem.

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.


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