Formulation of a multi-period multi-echelon location-inventory-routing problem comparing different nature-inspired algorithms
Article Type
Research Article
Publication Title
Sadhana - Academy Proceedings in Engineering Sciences
Abstract
The efficacy of an integrated approach to supply chain decision-making has demonstrated cost-effectiveness in contrast to the traditional sequential decision-making strategy. This has inspired researchers to challenge conventional practices and emphasize the importance of integrated decision-making in the realm of supply chains. This article addresses a multi-period multi-echelon location-inventory-routing problem consisting of a single factory, multiple distribution centres, and multiple retailers where important managerial decisions such as the location of distribution centres, vehicle routing schedule, delivery quantity to the various retailers, and replenishment schedule of the distribution centres are determined in different time periods so as to minimize the total cost of the supply chain which is one of the significant contributions of this research work. To solve the mathematical model, a novel chromosome representation is designed, specifically tailored for genetic algorithm, adding an innovative dimension to this research. To ascertain the results, different selection mechanisms of the genetic algorithm have been employed. The determined results are also compared with particle swarm optimization. The study reveals that genetic algorithm with tournament selection criteria gives the best optimal solution compared to the other algorithms for the proposed mathematical model in all the numerical instances. Further, a sensitivity analysis is also carried out to highlight the impact of various input parameters and provide relevant managerial insights.
DOI
https://10.1007/s12046-023-02288-9
Publication Date
12-1-2023
Recommended Citation
Kumari, Mamta; De, Pijus Kanti; and Chakraborty, Ashis Kumar, "Formulation of a multi-period multi-echelon location-inventory-routing problem comparing different nature-inspired algorithms" (2023). Journal Articles. 3461.
https://digitalcommons.isical.ac.in/journal-articles/3461