GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

TL;DR

This paper introduces a GPU-accelerated parallel genetic algorithm for dynamic, energy-efficient flexible flow shop scheduling, effectively handling real-time job arrivals and reducing computational time.

Contribution

It presents a novel hybrid parallel genetic algorithm using CUDA for dynamic energy-efficient scheduling with reactive rescheduling capabilities.

Findings

Outperforms traditional static scheduling methods

Reduces computational time significantly

Achieves better energy efficiency in dynamic environments

Abstract

Due to new government legislation, customers' environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes a dynamic energy efficient flexible flow shop scheduling model using peak power value with the consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallel Genetic Algorithm with a predictive reactive complete rescheduling approach is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only achieve better performance than the traditional static approach, but also gain competitive results by reducing the time requirements dramatically.