Impact Score 4.62
Special Issue in Computers & Industrial Engineering:
Intelligent Optimization with Learning for Scheduling and Logistics Systems
Aim of the Special Issue:
Knowledge engineering is a branch of artificial intelligence that emphasizes the development and use of information learned from data. Many real-world applications for complex industrial engineering or design problems could be modeled as optimization problems. Intelligent Optimization with Learning methods is an emerging approach, utilizing advanced computation power with meta-heuristics algorithms and massive-data processing techniques. These approaches have been actively investigated and applied particularly to scheduling and logistics operations.
Intelligent Optimization Algorithms (IOAs), which are learned from biological or social phenomena, are a collection of search and optimization techniques. Intelligent optimization algorithms include evolutionary computation methods, swarm intelligence, etc. With IOAs, the optimization problems, which can be represented in any form, do not need to be mathematically represented as continuous and differentiable functions. The only requirement for representing optimization problems is that each individual is evaluated as the termed fitness value. Therefore, intelligent optimization algorithms could be utilized to solve more general optimization problems, especially for problems that are very difficult to solve with traditional hill-climbing algorithms.
Scheduling: 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 that could solve all troubles in real-world cases. Intelligent Optimization with Learning methods is a practical approach to handle such complexity by utilizing evolutionary computation, swarm intelligence, and other meta-heuristics methods from domain expert knowledge and experience.
Scope of the Special Issue:
Recently the interaction between the Intelligent Optimization Algorithms and Knowledge Learning has received considerable attention from both the research community and industry world. The intelligent optimization techniques can be incorporated into several knowledge learning strategies in various ways to optimize the evolutionary process of the IOAs. The learning ability also affects Metaheuristics on various aspects of Computers & Industrial Engineering. The increasing power of computing makes the Metaheuristics acceptable practically, to handle the complex scheduling and logistics problems efficiency. In order to review recent advances in Intelligent Optimization with Learning for Scheduling and Logistics, this special issue will focus on publishing original research papers dealing with theoretical/technical knowledge expansion on Intelligent Optimization with Learning ability for real-world applications in advancing Scheduling and Logistics. Submissions involving real-world case studies are encouraged in the following topics (but not limited to):
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 the EES website. Please make sure you select “Special Issue” as Article Type and “Intelligent Optimization with Learning” 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 a double-blind peer review by at least two qualified reviewers.
Prof. Mitsuo Gen, Fuzzy Logic Systems Institute, Iizuka & Tokyo University of Science, Tokyo, Japan; [email protected]
Prof. Ling Wang, Dept. of Automation, Tsinghua University, Beijing, China; [email protected]
Prof. Gursel Suer, Dept. of Industrial & Systems Eng., Ohio University, Athens, USA; [email protected]
Prof. Imed Kacem, Automation and Computer Science Laboratory of Lille, University of Lorraine - LCOMS, France; [email protected]
Managing Guest Editor:
Dr. Shi Cheng, School of Computer Sci., Shaanxi Normal Univ., Xi’an, China; [email protected]