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:
- Bio-inspired algorithms, Nature-inspired Computing
- Computational Intelligence, Evolutionary Algorithms
- Meta-heuristic Algorithms, Swarm Intelligence
- Machine Learning, Deep Learning
- Reinforcement Learning, Deep Reinforcement Learning
- Agent-based Simulation, Multi-Agent Systems
- Intelligent Scheduling Systems, Decision Support Systems
- Intelligent Logistics Systems, Reverse Logistics Systems
- E-Commerce, Automation in Scheduling & Logistics
- Supply Chain (SC) Network SC Models with Sustainable Development Goals
- Underground Logistics Systems, Vehicle Routing Problem
Submission Guidelines:
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.
- Deadline for manuscript submission: October 31st, 2020
- Review report: December 31st, 2020
- Revised paper submission deadline: January 31st, 2021
- Notification of final acceptance: Febrary 28th, 2021
Guest Editors:
Prof. Mitsuo Gen, Fuzzy Logic Systems Institute, Iizuka & Tokyo University of Science, Tokyo, Japan; gen@flsi.or.jp
Prof. Ling Wang, Dept. of Automation, Tsinghua University, Beijing, China; wangling@mail.tsinghua.edu.cn
Prof. Gursel Suer, Dept. of Industrial & Systems Eng., Ohio University, Athens, USA; suer@ohio.edu
Prof. Imed Kacem, Automation and Computer Science Laboratory of Lille, University of Lorraine - LCOMS, France; imed.kacem@univ-lorraine.fr
Managing Guest Editor:
Dr. Shi Cheng, School of Computer Sci., Shaanxi Normal Univ., Xi’an, China; cheng@snnu.edu.cn
计算机与工业工程特刊：
基于学习的调度与物流系统智能优化
特刊目标：
知识工程是人工智能的一个分支，它强调开发和利用从数据中获得的信息。许多复杂工业工程或设计问题的实际应用都可以建模为优化问题。智能优化学习方法是一种新兴的智能方法，使用智能启发式算法和先进的数据处理技术。这些方法已经被积极地研究和应用，特别是在调度和物流运作中。
智能优化算法（IOAs）是从生物或社会现象中学习的一种搜索和优化技术的集合。智能优化算法包括进化计算方法、群体智能等，在IOAs中，优化问题可以用任何形式表示，不需要用数学的方法表示为连续可微函数。表示优化问题的唯一要求是每个个体都被评估为所谓的适应值。因此，智能优化算法可以用来解决更一般的优化问题，特别是对于传统爬山算法很难解决的问题。
调度：收集大量数据，用于优化路线选择、出租车调度、动态公交调度和其他机动服务，以提高运营效率。
物流：供应链实体（包括仓库、工厂、配送中心和零售店）内部和之间的物料流动通过先进的面向数据的技术进行改进和优化。
由于实际应用程序的复杂性，没有一种万能之计可以解决现实世界中的所有问题。智能优化学习方法是一种利用进化计算、群体智能和其他领域专家知识和经验的元启发式方法来处理这种复杂性的实用方法。
特刊范围：
近年来，智能优化算法与知识学习的相互作用受到了研究界和工业界的广泛关注。智能优化技术可以通过多种方式集成到多种知识学习策略中，以优化IOAs的进化过程。学习能力也影响到元启发式在计算机和工业工程的各个方面。随着计算能力的不断增强，元启发式算法在实际中得到了广泛的应用，可以有效地处理复杂的调度和物流问题。为了回顾具有学习能力的智能优化在调度和物流方面的最新进展，本期特刊将集中发表有关智能优化学习方法的理论/技术知识扩展的原创研究论文，以供在推进调度和物流方面的实际应用。鼓励在以下主题（但不限于）中提交涉及实际案例的研究：
- 生物启发算法，自然启发计算
- 计算智能、进化算法
- 元启发式算法，群体智能
- 机器学习、深度学习
- 强化学习、深度强化学习
- 基于Agent的仿真，多Agent系统
- 智能调度系统、决策支持系统
- 智能物流系统、逆向物流系统
- 电子商务、调度和物流自动化
- 具有可持续发展目标的供应链网络供应链模型
- 地下物流系统，车辆路径问题。
提交指南：
稿件应通过出版商的在线系统爱思唯尔编辑系统（EES）提交http://ees.elsevier.com/caie/。请按照EES网站主页上的“作者指南”中的说明进行操作。请确保选择“特刊”作为文章类型，选择“智能优化与学习”作为部分/类别。在准备手稿时，要求作者严格遵守“作者须知”。提交材料将根据C&IE的严格标准和程序，通过至少两名合格审稿员的双盲同行评审进行评审。
出版时间表：
- 稿件提交截止日期：2020年10月31日
- 评审报告：2020年12月31日
- 修改论文提交截止日期：2021年1月31日
- 最终接收通知：2021年2月28日
特邀编辑：
Prof. Mitsuo Gen, Fuzzy Logic Systems Institute, Iizuka & Tokyo University of Science, Tokyo, Japan; gen@flsi.or.jp
Prof. Ling Wang, Dept. of Automation, Tsinghua University, Beijing, China; wangling@mail.tsinghua.edu.cn
Prof. Gursel Suer, Dept. of Industrial & Systems Eng., Ohio University, Athens, USA; suer@ohio.edu
Prof. Imed Kacem, Automation and Computer Science Laboratory of Lille, University of Lorraine - LCOMS, France; imed.kacem@univ-lorraine.fr
执行特邀编辑：
Dr. Shi Cheng, School of Computer Sci., Shaanxi Normal Univ., Xi’an, China; cheng@snnu.edu.cn