Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Improved implicit optimal modeling of the labor shift scheduling problem
Management Science
Scheduling workforce and workflow in a high volume factory
Management Science
Personnel Tour Scheduling When Starting-Time Restrictions Are Present
Management Science
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Cooperative Multiagent Systems: A Personal View of the State of the Art
IEEE Transactions on Knowledge and Data Engineering
An Agent-Based Approach for Manufacturing Enterprise Integration and Supply Chain Management
PROLAMAT '98 Proceedings of the Tenth International IFIP WG5.2/WG5.3 Conference on Globalization of Manufacturing in the Digital Communications Era of the 21st Century: Innovation, Agility, and the Virtual Enterprise
Worker Cross-Training in Paced Assembly Lines
Manufacturing & Service Operations Management
The AARIA agent architecture: From manufacturing requirements to agent-based system design
Integrated Computer-Aided Engineering
Competency and preference based personnel scheduling in large assembly lines
International Journal of Computer Integrated Manufacturing - Industrial Engineering and Systems Management
A multi-agent-based approach for personnel scheduling in assembly centers
Engineering Applications of Artificial Intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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This article is about a multi-agent based algorithm for personnel scheduling and rescheduling in a dynamic environment of a paced multi-product assembly center. The purpose is first to elaborate daily employees' assignment to workstations so as to minimize the operational costs as well as personnel dissatisfactions; the second is to generate an alternative planning when the first solution has to be rescheduled due to disturbances related to absenteeism. The proposed approach takes into account individual competencies, mobility and preferences of each employee, along with the competency requirements associated with each assembly activity, with respect to both the current master assembly schedule and the line balancing for each product. We use solutions obtained through a simulated annealing algorithm in order to benchmark the performance of the multi-agent approach. Experimental results show that our multi-agent approach can produce high-quality and efficient solutions in a short computational time.