A simulation-based backward planning approach for order-release
Proceedings of the 29th conference on Winter simulation
Experiences with backward simulation based approach for lot release planning
Proceedings of the 29th conference on Winter simulation
An application of a planning and scheduling multi-model approach in the chemical industry
Computers in Industry - Special issue on computer integrated manufacturing in the process industries (I-CIMPRO)
Proceedings of the 30th conference on Winter simulation
Modeling furnace operations using simulation and heuristics
Proceedings of the 30th conference on Winter simulation
Business Process Engineering: Reference Models for Industrial Enterprises
Business Process Engineering: Reference Models for Industrial Enterprises
A survey of approaches to the job shop scheduling problem
SSST '96 Proceedings of the 28th Southeastern Symposium on System Theory (SSST '96)
Supply chain planning: the role of simulation in advanced planning and scheduling
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Supply chain planning: promise and problems of simulation technology in SCM domain
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
| Hi-index | 0.00 |
Simulation-based production scheduling approaches are emerging as alternatives to optimization and simpler approaches such as priority rules. This paper presents an application of a simulation-based finite scheduling at Albany International, the largest manufacturer of paper machine clothing in the world. Simulation is used as a decision support tool for manual schedule creation. User experiences have been encouraging. We argue that an optimization-based approach is not necessarily the most economical and identify a number of tentative key enablers of a simulation-based solution. The case indicates that a simulation-based solution is a viable option when the production process does not include combination of materials and local sequencing is adequate. A simulation-based solution capitalizes on this existing source of tacit knowledge by giving expert human schedulers tools for testing and improving schedules.