Manuscript Topics

Intelligent manufacturing/logistics is the key issue to integrate the new information technology & manufacturing technology and promote the intelligent manufacturing process. Production scheduling/routing is the core of intelligent manufacturing optimization, and it is the key to achieve high efficiency, high reliability, and high flexibility. Because of its characteristics of multiple resources, multiple constraints, strong coupling and NP-hard, it has become a research hotspot in the field of intelligent manufacturing. When solving the production scheduling problems, metaheuristics and/or machine learning algorithms with its global search ability and problem adaptation ability, has become an effective technology to solve complex scheduling problems.

Recently, hybrid metaheuristics algorithm (HMA) has gradually become a new development trend. It mixes different concepts, different components of a variety of MA, a variety of metaheuristic methods or other simple heuristic algorithms in a reasonable combination to further improve the efficiency of algorithm, overcome the disadvantages of single algorithm, and provide faster and better solutions for decision makers. Improving algorithm optimization quality, efficiency and robustness has become one of the challenging studies in the field of HMA design. Especially, the HMA with knowledge learning has attracted a lot of attention in both the academic research and practical industry. The metaheuristics could be properly incorporated within knowledge learning strategies in various ways to optimize their evolutionary process. The learning ability also enhances metaheuristics on various aspects by the problem knowledge. The combination of metaheuristics and knowledge learning could handle the complex intelligent scheduling and routing problems effectively and efficiently. The purpose of this Special Issue is to gather a collection of articles that cover the latest developments in different fields of intelligent manufacturing/logistics by hybrid metaheuristics and/or machine learning algorithms with different metaheuristics, swarm intelligence, deep learning, reinforcement learning, and others. This special issue will publish original research, review and application papers including but not limited to the following fields:

Intelligent Manufacturing, Logistics/SCM, Closed-Loop Sustainability, Big Data Analytics
Metaheuristics Algorithms for Multiobjective Optimization Problems
Metaheuristics for Flexible Jobshop/Flowshop Scheduling
Hybrid Metaheuristics with Learning for Green Scheduling/Routing
Hybrid Metaheuristics with Learning for Process Planning and Scheduling
Hybrid Metaheuristics with Learning for Vehicle Routing
Hybrid Metaheuristics with Learning for Resource-constrained Project Scheduling
Hybrid Metaheuristics with Learning for Transport/Train Scheduling
Reinforcements Learning in Intelligent Manufacturing/Logistics
Hybrid Metaheuristics with Learning with Data-Driven in Intelligent Manufacturing
Applications of Hybrid Metaheuristics with Learning in Smart Manufacturing/Logistics
Deep Learning Algorithms for Automation Systems
Applications of Deep Learning in Intelligent Manufacturing/Logistics
Other Related Topics on Hybrid Metaheuristics with Learning in Intelligent Scheduling/Routing

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/