Journal Information
Computers & Industrial Engineering
https://www.sciencedirect.com/journal/computers-and-industrial-engineering
Impact Factor:
3.518
Publisher:
Elsevier
ISSN:
0360-8352
Viewed:
497
Tracked:
0

Call For Papers
Industrial engineering is one of the earliest fields to utilize computers in research, education, and practice. Over the years, computers and electronic communication have become an integral part of industrial engineering. Computers & Industrial Engineering (CAIE) is aimed at an audience of researchers, educators and practitioners of industrial engineering and associated fields.

It publishes original contributions on the development of new computerized methodologies for solving industrial engineering problems, as well as the applications of those methodologies to problems of interest in the broad industrial engineering and associated communities. The journal encourages submissions that expand the frontiers of the fundamental theories and concepts underlying industrial engineering techniques.

CAIE also serves as a venue for articles evaluating the state-of-the-art of computer applications in various industrial engineering and related topics, and research in the utilization of computers in industrial engineering education. Papers reporting on applications of industrial engineering techniques to real life problems are welcome, as long as they satisfy the criteria of originality in the choice of the problem and the tools utilized to solve it, generality of the approach for applicability to other problems, and significance of the results produced.

A major aim of the journal is to foster international exchange of ideas and experiences among scholars and practitioners with shared interests all over the world.
Last updated by Dou Sun in 2020-03-13
Special Issues
Special Issue on Intelligent Optimization with Learning for Scheduling and Logistics Systems
Submission Date: 2020-10-31

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. 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): 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
Last updated by Dou Sun in 2020-05-21
Special Issue on The Coronavirus Pandemic’s Impact on the Design and Management of Production Systems and Supply Chains
Submission Date: 2020-10-31

The recent outbreak of COVID-19 disease caused by the new coronavirus first detected in Wuhan China, and its rapid spread around the globe, rekindled the attention of the world towards the effects of such epidemics on people’s everyday life. This happened in the past when the “Severe Acute Respiratory Syndrome” (SARS) in 2003 in mainland China, the “Middle East Respiratory Syndrome” (MERS) in 2012 in Saudi Arabia, and the MERS in 2015 in South Korea (de Wit et al., 2016) took the scene. As observed, this kind of epidemic can rapidly spread by a group of infectious agents through several methods of interactions and threaten the health of many people in a short time (Medina, 2018). These kinds of viruses, and their induced related epidemic crises, are having a great impact on every aspect of the economy, finance and society, raising new challenges in the field of epidemic disease prevention and mitigation. Specifically, the impact of the Covid-19 pandemic has brought to light a new dimension in the interpretation of “sustainability” and “resilience” of production systems and supply chains. As to industrial production and distribution, suddenly the need arose for: Safer production systems which apply new (or advanced) standards for Personal Protective Equipment (PPE), new logics of functioning and organization, and new shift planning methods in order to prevent occupational exposure to health-threatening factors or diseases such as COVID-19. This has also affected those industrial sectors where eye, face and respiratory protections were not required before the coronavirus emergence. More flexible and more responsive production systems and supply chains, able to easily adapt to the profound and rapid changes that have occurred since the new virus emerged (controlled and reduced personal contacts in order to limit human-to-human transmission, rapid variation in volumes and prices, new marketing, financial and logistic policies, and so on). This Special Issue of Computers & Industrial Engineering aims to attract world-leading research which may help to understand the impact of the coronavirus on production systems and supply chains. Further aims are to identify new potential work-related exposure and health risks, to develop new solutions for increasing safety of production systems, and flexibility and resilience of domestic and cross-border supply chains. Contributions are expected, but not limited, to the following topics: evidence-based analysis of the pandemic’s impact on modern supply chains and production systems. novel safety management practices and strategies to prevent rapid epidemic diffusion in production environments (both goods and services production). optimized workforce management models and organizational methods to limit inter-personal contacts and disease transmission. innovative models for highly responsive PPE supply chains and PPE production facilities. novel design and management strategies for resilient supply chains and production systems. new supply chain sustainability models to encompass epidemic disease related issues. reconfiguration models for supply chains during emergency management.
Last updated by Dou Sun in 2020-05-21
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