Journal Information
Computers & Industrial Engineering
Impact Factor:
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 2022-01-29
Special Issues
Special Issue on Data-Driven Analytics for Complex System/Network Optimization
Submission Date: 2022-08-31

Summary of this Virtual issue Simon (1991) defined a complex system as “one made up of a large number of parts that interact in a non-simple way.” In such systems, given the properties of the entities and the rules of their interaction, it is not simple to infer the preference of the whole (Ethiraj and Levinthal 2004). A well-known example is a supply chain which is a complex network with a large number of interactions and inter-dependencies among different parties, processes and resources. The network shows complex multi-scale behavior, and evolves and self-organizes through a complex interplay of its structure and function (Surana et al. 2005). This strict complexity of supply chain system, with inevitable lack of prediction, leads it difficult to manage and control them. For example, the COVID-19 pandemic has uncovered the vulnerability in many supply chain networks, as complex networks failed from disruptions at local nodes, their operation missing connectivity. Such disruption may occur caused by many reasons (Huang et al 2012 and Pi et al 2019). Although many firms have realized the negative impacts of the pandemic, most of them lacked guidance on how to handle disruptions in a complex system, which resulting in delays of the system’s recovery. In other words, the changing organizational and market trends mean that the supply chains should be highly dynamic, reconfigurable and adaptive: the network should sense and respond effectively to satisfy changes in reality. In addition, the age of advanced digital technologies allows storing huge volumes of data of each party in supply chain network. New information technologies, such as Artificial Intelligence (AI) and blockchain, set up dynamic information exchange networks, have been the key factors in solving threat of disruptions. Artificial Intelligence (AI) automation and Blockchain are two key implications of the fourth industrial revolution, i.e., Industry 4.0 (Olsen and Tomlin 2020). The past decades have witnessed rapid development, wide applications and outstanding achievements of AI and Blockchain in supply chain. For example, automated inventory replenishment systems often recommend order quantities to the retail store managers (Van Donselaar et al. 2010). Walmart has piloted the use Blockchain to track pork in China (Nash 2016). The intelligent data driven applications of AI and Blockchain in supply chain systems improve relationship in terms of transparency, efficacy and productivity. Most recently, AI algorithms are used to develop vaccine for novel coronavirus COVID, which belongs to production side of supply chain network. Furthermore, new information technologies are emerging to affect many aspects of decision marking of parties in a complex supply chain network, such as product manufacturing, inventory and distributing as well as information collecting, analyzing and exchanging. Business institutions are deploying and integrating AI and Blockchain into their business practice. In this context, exploring the opportunity of data analytics to effectively manage the complex system decision making has become inevitable since these emerging technologies are useful in optimizing each party’s problems. AI and Blockchain are founded on and combines statistics, database, machine learning and expert systems. It can be adopted for achieving various goals of complex system such as prediction, classification, clustering and exploration. Therefore, the problem of designing, managing, coordinating, and optimizing the numerous activities of each part, such as economic institutions, products or organizations, in supply chain network has been attracted more attentions due to the increased awareness and technological developments. A complex supply chain network transfers information, products and finances between various suppliers, manufacturers, distributors, retailers and customers. This system is characterized by a forward flow of goods and a backward flow of information. New information technology of data analysis, which can provide wide range of connectivity, enterprise integration, data collecting and analyzing, has been the key tools for the management of complex system. It is significant for eliminating collaboration and coordination costs, and allows the rapid establishment of dynamic information exchange networks to eliminate information asymmetry. Objectives As learnt from the COVID-19 pandemic, it can be found that the optimization of the complex system should be further studied. Unexpected disruptions are fatal blow to complex systems. In the era of Industry 4.0, decision optimization for complex supply chain is only feasible when data analytics are taken into consideration. Big data analytics is proven to help forecast the decision marking, and hence can be powerful in improving performance of supply chain network. Blockchain has the ability to enhance the transparency between different parties in system. This special issue aims to discuss the opportunities and challenges of these new information technologies and the performance of complex supply chain network should be improved by AI, Blockchain techniques and other data analysis method. This special issue also highlights data-driven complex system’s optimization/evaluation by considering unexpected disruptions. Manuscripts with MS/OR/modelling methodologies are suited for the special issue. In particular, the following research directions, but not limited to, are in the scope of this Special Issue: Topics of interest of this Special Issue Authors are encouraged to submit high-quality original empirical, quantitative, or conceptual research papers. Suggested topics of interest include, but are not limited to: Complex supply chain network design from perspectives of viability, adaptability and re-configurable supply networks Optimization of complex system in coping with unexpected disruptions Efficiency evaluation of complex system AI/ Blockchain based supply chain network decision making Data analytics for entities’ pricing strategy in supply chain network Information collecting and sharing between supply chain nodes Data analytics for supply chain network coordination/optimization Risk modeling and management Group making under Social Network Manuscript Submission Before submission, all authors must check the standard editorial guidelines provided in the Guide for Authors at: This special edition follows the standard submission procedures of the Editorial Manager. Authors should submit their papers through the Editorial Manager at: Authors should choose the article type as: VSI: Data-Driven Analytics All submissions will be verified for similarity. Submissions with high levels of similarity will be automatically rejected. We invite the authors to ensure that the papers submitted are written in good English standard (American or British usage is accepted, but not a mixture of these). Authors who feel their manuscript might need editing and proofreading may to use the English Language Editing service available from Elsevier's Author Services or other Professional English Scientific Editor before the submission as well as before the final review. Important dates Manuscript Submission Deadline: 31st August 2022 Notification of First Decision: 31st November 2022 Revised Version Submission: 31st January 2023 Final Decision: 31st March 2023 Expected Publication: As soon as accepted (VSI) Guest Editorial Team Jie Wu University of Science and Technology, China. Ron Fisher Cardiff Metropolitan University, UK Jian Wu Shanghai Maritime University, China. Hamido Fujita Iwate Prefectural University, Iwate, Japan
Last updated by Dou Sun in 2022-01-29
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