Aim of the Special Issue:
Machine Learning (ML) accelerated by GPU computing, particularly, Deep Learning (DL) and Reinforcement Learning (RL) are examples of the foundational technological drivers for the 4th Industrial Revolution. The increasing computation power and the availability of Big Data have redefined the value of the Artificial Intelligence (AI) based approach. The Machine Learning based Evolutionary Algorithm and Optimization are emerging approaches, utilizing advanced computation power with GPU and massive-data processing techniques. These approaches have been actively investigated and applied particularly to transportation and logistics operations.
Transportation: massive data is collected and used to optimize the route selection, taxi dispatching, dynamic transit bus scheduling, and other mobility services to improve the efficiency of the operations.
Logistics: material movements, within and between supply chain entities including warehouses, factories, distribution centers, and retail shops, are improved and optimized with advanced data oriented techniques.
Due to the complexity of real world applications, there is no one panacea which could solve all troubles in real world cases. Machine Learning based Evolutionary Algorithms and Optimization are practical approaches to handle such complexity by utilizing GPU computing power, ML learning ability, and heuristics from domain expert knowledge and experience.
Scope of the Special Issue:
Recently the interaction between the Machine Learning (ML) concept and Evolutionary Algorithm (EA) has received considerable attention from both the research community and industry world. The ML techniques can be incorporated into several Evolutionary Algorithms (EA) in various ways to optimize the evolutionary process of the EA methods. The learning ability of ML also affects Metaheuristics on various aspects in Computers & Industrial Engineering. GPU computing makes the ML based Metaheuristics acceptable practically, to handle the complex logics and big data with the learning abilities. In order to review recent advances on Machine Learning based Evolutionary Algorithm and Optimization, this special issue will focus on publishing original research papers dealing with theoretical/technical knowledge expansion on AI, ML, DL and RL based EA and Optimization for real-world applications in advancing Intelligent Transportation and Logistics. Submissions involving real world case studies are encouraged in the following topics (but not limited to): Topics of interest include, but are not limited to:
AI, Machine Learning, Deep Learning, Reinforcement Learning & CUDA
Acceleration by GPU computing
Computational Intelligence & Evolutionary Algorithms
Intelligent Transport Systems
Intelligent Port Logistics
Intelligent Logistics & SCM Networks
Container Systems & AGV Scheduling
Automation in Transportation & Logistics
Other Related Topics
Manuscripts should be submitted through the publisher’s online system, Elsevier Editorial System (EES) at http://ees.elsevier.com/caie/. Please follow the instructions described in the “Guide for Authors”, given on the main page of EES website. Please make sure you select “Special Issue” as Article Type and “Machine Learning based EA and Optimization” as Section/Category. In preparing their manuscript, the authors are asked to closely follow the “Instructions to Authors”. Submissions will be reviewed according to C&IE’s rigorous standards and procedures through double-blind peer review by at least two qualified reviewers.
Deadline for manuscript submission: Feb. 28th, 2018
Review report: May 1st, 2018
Revised paper submission deadline: June 15th, 2018
Notification of final acceptance: Aug. 15th, 2018
Expected Publication (Tentative): Nov. 2018
Dr. John Cheng: Senior Research Scientist, BGT, Houston, USA; email@example.com
Dr. Will Ramey, Director of Developer Marketing, NVIDIA Corp., USA WRamey@nvidia.com
Dr. Bin Yang: Prof., Logistics Research Center, Shanghai Maritime University, China; firstname.lastname@example.org
Dr. Mitsuo Gen: Senior Research Scientist, Fuzzy Logic Systems Institute and Visiting Prof., Tokyo University of Science, Japan; email@example.com
Dr. Yong Jae Jang: Associate Prof., Dept. of Industrial & Systems Engineering, KAIST, Korea; firstname.lastname@example.org
Managing Guest Editor:
Dr. Cheng-Ji Liang: Prof., Logistics Research Center, Shanghai Maritime University, China; email@example.com