An enhanced genetic-based multi-objective mathematical model for industrial supply chain network

Abstract

The multi-objective supply chain needs a full look at enterprise costs, coordinated delivery of different products, and more fluidity and efficiency within the network of the supply chain. However, existing methodologies rarely delve into the intricacies of the industrial supply chain. Therefore, in the emerging industrial supply chain network, a model for the multi-objective problem was made using a meta-heuristic approach, specifically the improved genetic algorithm, which is a type of soft computing. To create the initial population, a hybrid approach that combines topology theory and the random search method was adopted, which resulted in a modification of the conventional single roulette wheel selection procedure. Additionally, the crossover and mutation operations were enhanced, with determining their respective probabilities determined through a fusion of the elite selection approach and the roulette method. The simulation results indicate that the improved genetic algorithm reduced the supply load from 0.678 to 0.535, labor costs from 1832 yuan to 1790 yuan, and operational time by approximately 39.5%, from 48 seconds to 29.5 seconds. Additionally, the variation in node utilization rates significantly decreased from 30.1% to 12.25%, markedly enhancing resource scheduling efficiency and overall balance within the supply chain.