%0 Conference Proceedings
%T Reactive Scheduling by Intelligent DSS
%+ Beihang University (BUAA)
%+ Avic Chengdu Civil Aircraft Co. Ltd
%+ Beijing Shuguang Aviation Electric Co. Ltd
%A He, Yumin
%A Lin, Yaohu
%A Liu, Hongbo
%A Guo, Mengpeng
%Z Part 4: Learning and Robust Decision Support Systems for Agile Manufacturing environments
%< avec comité de lecture
%@ 978-3-030-85873-5
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Nantes, France
%Y Alexandre Dolgui
%Y Alain Bernard
%Y David Lemoine
%Y Gregor von Cieminski
%Y David Romero
%I Springer International Publishing
%3 Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
%V AICT-630
%N Part I
%P 267-274
%8 2021-09-05
%D 2021
%R 10.1007/978-3-030-85874-2_28
%K Reactive scheduling
%K Multi-criteria decision making
%K Decision support system
%K Inductive learning
%K Knowledge-based System
%K Agile manufacturing
%Z Computer Science [cs]Conference papers
%X Agile manufacturing is in practice by many companies. In agile manufacturing environments, it is important for companies to make quick response to the changes in the environments. This paper proposes an intelligent decision support system (DSS) for reactive scheduling to handle disturbances in agile manufacturing environments. The intelligent decision support system integrates a knowledge-based system for intelligent and multiple criteria decision-making. The intelligent DSS includes three basic modules, the database module, the model base module, and the interface module. The framework of the intelligent DSS is presented. The objective-oriented data model, the knowledge-based rules, and rule induction are designed. The reactive scheduling algorithm is developed. Radio frequency identification and knowledge acquisition tools are applied by the intelligent DSS. The intelligent DSS can be implemented by applying contemporary information technology and can provide an approach to make reactive production scheduling decisions quickly to handle disturbances for manufacturing firms to obtain competitive advantage and agility.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-04030359/document
%2 https://inria.hal.science/hal-04030359/file/509923_1_En_28_Chapter.pdf
%L hal-04030359
%U https://inria.hal.science/hal-04030359
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-630