Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project's makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, e-indicator and empirical attaintment functions.

Automated summary

In this paper, the authors study a job shop scheduling problem with the dual objectives of minimizing energy consumption during machine idle time and minimizing the project's makespan. They consider uncertainty in processing times using fuzzy numbers and propose a multi-objective optimization model along with an enhanced memetic algorithm. Experimental results validate the effectiveness of the proposed method.