Simulation intelligence and modeling for manufacturing uncertainties

Authors:
Sk Ahad Ali;Hamid Seifoddini
Affiliations:
University of Puerto Rico - Mayaguez, Mayaguez, PR;University of Wisconsin-Milwaukee, Milwaukee, WI
Venue:
Proceedings of the 38th conference on Winter simulation
Year:
2006

Citing 4
Cited 3

Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment

Management Science
Introduction to Arena

WSC '93 Proceedings of the 25th conference on Winter simulation
Manufacturing 3: a simulation study of an automotive foundry plant manufacturing engine blocks

Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation of manufacturing operations: optimization of buffer sizes in assembly systems using intelligent techniques

Proceedings of the 34th conference on Winter simulation: exploring new frontiers

Embedding human scheduling in a steel plant simulation

Proceedings of the 40th Conference on Winter Simulation
Simulation of fuel tank assembly and process analysis for performance improvement

Winter Simulation Conference
Simulation of fuel tank assembly and process analysis for performance improvement

Proceedings of the Winter Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

The realistic simulation modeling becomes very essential and effective for designing and managing of manufacturing systems, which needs to be addressed manufacturing dynamics. This research includes manufacturing uncertainties in the form of simulation intelligence to improve the system's performance in the high-mix low-volume manufacturing systems. It shows how simulation modeling can be used to evaluate alternative designs in a dynamic uncertain manufacturing environment. Fuzzy rule based machine, labor and logistics uncertainties are addressed in this study. A combination of product mix and production volume is analyzed using intelligent simulation model for an optimal designing of the production system to meet future customer demands. Intelligent knowledge system shows significant close to real-life scenario. The proposed intelligent simulation modeling is validated with real life application.