Dispatching rules for stochastic finite capacity scheduling
Proceedings of the 23rd international conference on on Computers and industrial engineering
Evaluation and comparison of production schedules
Computers in Industry - Special issue on advances in computer integrated production in honour of professor C.L. Moodie's retirement
An experimental benchmarking of two multi-agent architectures for production scheduling and control
Computers in Industry - Special issue on intelligent manufacturing systems
Definition of a Robustness Metric for Resource Allocation
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
Using online simulation in Holonic manufacturing systems
Engineering Applications of Artificial Intelligence
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Scheduling decisions constitute the last decision-making phase of the production planning and control process. From the industrial side, the adoption of highly reactive and efficient scheduling and control systems strongly affects the level of productivity and utilization of a manufacturing system, particularly under the pressure of shortened product cycles, reduced batch sizes and a broader variety of items to be produced. In the meanwhile, from the research side, there has been a considerable amount of works done in the area of manufacturing systems control, even if they still remain ''unheard voices'' in industry. Hence, in the scheduling world there is a risk of miscommunication between academics and industrial users. Aim of the paper is to provide a comprehensive view of the rationale, the conceptual model, the development efforts and first applicative experiences of the Benchmarking Service, a research initiative which has been carried out within the activities of the Special Interest Group on Benchmarking and Performance Measurement of the IMS Network of Excellence. In particular, the paper details the PMS-ESS conceptual framework developed for assessing the level of quality of a scheduling solution in terms of efficiency, robustness and flexibility.