Two-stage knowledge-driven evolutionary algorithm for distributed green flexible job shop scheduling with type-2 fuzzy processing time
This study is investigated on multi-objective distributed green flexible job shop scheduling
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …
problem with type-2 fuzzy processing time. Minimizing makespan and total energy …
A knowledge-guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem
F Yu, C Lu, J Zhou, L Yin, K Wang - Engineering Applications of Artificial …, 2024 - Elsevier
With the guidance of the advanced manufacturing philosophy, green scheduling and energy
efficiency have received considerable attention from enterprises and countries. Meanwhile …
efficiency have received considerable attention from enterprises and countries. Meanwhile …
Surprisingly popular-based adaptive memetic algorithm for energy-efficient distributed flexible job shop scheduling
With the development of the economy, distributed manufacturing has gradually become the
mainstream production mode. This work aims to solve the energy-efficient distributed flexible …
mainstream production mode. This work aims to solve the energy-efficient distributed flexible …
Co-evolution with deep reinforcement learning for energy-aware distributed heterogeneous flexible job shop scheduling
Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is
an extension of the traditional FJS, which is harder to solve. This work aims to minimize total …
an extension of the traditional FJS, which is harder to solve. This work aims to minimize total …
Knowledge-driven two-stage memetic algorithm for energy-efficient flexible job shop scheduling with machine breakdowns
This paper focuses on the multi-objective energy-efficient flexible job shop scheduling
problem with machine breakdowns. To mitigate the impact of machine breakdowns, a …
problem with machine breakdowns. To mitigate the impact of machine breakdowns, a …
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …
machines while meeting manufacturing rules as well as job specifications. Optimizing the …
Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
This study addresses a distributed assembly permutation flowshop scheduling problem,
which is of great significance in practical manufacturing systems. We aim to sequence …
which is of great significance in practical manufacturing systems. We aim to sequence …
A Q-learning-based hyper-heuristic evolutionary algorithm for the distributed flexible job-shop scheduling problem with crane transportation
ZQ Zhang, FC Wu, B Qian, R Hu, L Wang… - Expert Systems with …, 2023 - Elsevier
With the globalization and sustainable development of the modern manufacturing industry,
distributed manufacturing and scheduling systems that consider environmental effects have …
distributed manufacturing and scheduling systems that consider environmental effects have …
Bi-population balancing multi-objective algorithm for fuzzy flexible job shop with energy and transportation
Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial
systems. In this study, we propose a bi-population balancing multi-objective evolutionary …
systems. In this study, we propose a bi-population balancing multi-objective evolutionary …
Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories
With the development of the global economy and the enhancement of environmental
awareness, energy-efficient permutation flow shop scheduling gets more attention …
awareness, energy-efficient permutation flow shop scheduling gets more attention …