Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

TL;DR: In this article , the authors introduce the sustainable flexible scheduling problem (SFJSSP) as a human and energy-efficiency-centered scheduling problem, which considers the human in the loop, and shows that the well-being and skills of workers significantly affect scheduling performance.

TL;DR: This paper addresses the flexible job shop scheduling problem with limited fixture-pallet resources in multi-product mixed manufacturing workshops, proposing a mixed-integer programming model and a novel genetic algorithm with feasibility correction and self-learning VNS for optimisation.

TL;DR: Li et al. as mentioned in this paper proposed a two-stage knowledge-driven evolutionary algorithm (TS-KEA) which divided evolutionary process into two stage. And then, a full-active scheduling strategy was designed to reduce total energy consumption.

TL;DR: Experimental results indicate that DQCE outperforms the six state-of-the-art algorithms for DHFJS.

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.

TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.

TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.

TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.