AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

Abstract

Numerous artificial intelligence (AI) techniques are currently utilized to identify planning solutions for supply chains, which comprise suppliers, manufacturers, wholesalers, and customers. Continuous optimization of these chains is necessary to enhance their performance. Manufacturing is a critical stage within the supply chain that requires continuous optimization. Mass Customization Manufacturing is one such manufacturing type that involves high-volume production with a wide variety of materials. However, genetic algorithms have not been used to minimize both time and cost in the context of mass customization manufacturing. Therefore, we propose this study to present an artificial intelligence solution using genetic algorithm to build a model that minimizes the time and cost which associated with mass customized orders. Our problem formulation is based on a real-world case, and it adheres to expert descriptions. Our proposed optimization model incorporates two strategies to solve the optimization problem. The first strategy employs a single objective function focused on either time or cost, while the second strategy applies the multi-objective function NSGAII to optimize both time and cost simultaneously. The effectiveness of the proposed model was evaluated using a real case study, and the results demonstrated that leveraging genetic algorithms for mass customization optimization outperformed expert estimations in finding efficient solutions. On average, the evaluation revealed a 20.4% improvement for time optimization, a 29.8% improvement for cost optimization, and a 25.5% improvement for combined time and cost optimization compared to traditional expert optimization.

A Patrol Platform Based on Unmanned Aerial Vehicle for Urban Safety and Intelligent Social Governance

“…When solving optimization problems, swarm intelligence algorithms can not only consider multiple objectives but also find the optimal solution in large-scale complex problems. Representative algorithms include genetic algorithm (GA) [33] and grey wolf optimizer (GWO) [34]. Therefore, at present, more and more swarm intelligence algorithms are being applied to UAV trajectory planning problems.…”

“…The origin of swarm intelligence technology originated from Reynolds' research on the Bodies project, and after continuous evolution and development, swarm intelligence algorithms including GWO, GA, and CSA have emerged [33]- [34]. The swarm intelligence algorithm is a cluster of www.ijacsa.thesai.org algorithms based on group behavior and intelligence, which simulates the interaction and cooperation between individuals in a group to achieve the ability to solve problems collaboratively.…”