Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Improved implicit optimal modeling of the labor shift scheduling problem
Management Science
Personnel Tour Scheduling When Starting-Time Restrictions Are Present
Management Science
Dynamic Coalition Formation among Rational Agents
IEEE Intelligent Systems
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
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
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Distributed control of production systems
Engineering Applications of Artificial Intelligence
Applications of agent-based models for optimization problems: A literature review
Expert Systems with Applications: An International Journal
An agent-based algorithm for personnel shift-scheduling and rescheduling in flexible assembly lines
Journal of Intelligent Manufacturing
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This paper presents a multi-agent-based approach for personnel scheduling problems in the context of a paced multi-product assembly center. Our purpose is to elaborate daily assignment of employees to workstations in order to minimize simultaneously the operational costs and personnel dissatisfactions. The proposed approach considers the individual competencies, mobility and preferences of each employee, as well as the personnel and competency requirements associated with each assembly activity given both the current master assembly schedule and the line balancing for each product. To benchmark the performance of the multi-agent approach, we use optimal solutions obtained through a linear programming model resolution using a commercial solver. Experimental results show that our multi-agent approach can produce high-quality and efficient solutions in a short computational time.