A HYBRID APPROACH OF GENETIC ALGORITHMS AND SIMULATION FOR OPTIMIZING SUPPLY CHAIN DECISIONS

Fatoni; Sonianto; Fahlul Rizki

Fatoni Universitas Muhammadiyah Pringsewu
Sonianto Universitas Muhammadiyah Pringsewu
Fahlul Rizki Universitas Aisyah Pringsewu

Keywords: genetic algorithms, simulation, supply chain decision-making, cost reduction, hybrid approach

Abstract

This study is dedicated to developing a hybrid approach that integrates genetic algorithms with simulation to optimize decision-making processes within supply chains. In the increasingly complex landscape of modern supply chains, there is a pressing need for efficient methods to tackle the challenges associated with decision-making. Genetic algorithms have demonstrated their efficacy in identifying optimal solutions, while simulation offers valuable insights into the dynamics of existing systems. The methodology employed in this research involves the application of genetic algorithms to seek out the best solutions based on predefined input parameters. These solutions are then validated through simulation, aiming to evaluate the system's performance under a variety of potential scenarios. The findings of the study reveal that the synergy between genetic algorithms and simulation significantly enhances the efficiency of supply chain decision-making, achieving cost reductions and improvements in customer service. Consequently, the conclusion drawn from this research is that the hybrid approach is effective in optimizing supply chain decisions, providing solutions that are more adaptive and responsive to market fluctuations. Therefore, it is recommended that companies adopt this approach to boost competitiveness and responsiveness in their supply chain operations

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