Manufacturing flow line systems: a review of models and analytical results
Queueing Systems: Theory and Applications - Special issue on queueing models of manufacturing systems
Sample-path optimization in simulation
WSC '94 Proceedings of the 26th conference on Winter simulation
Analysis of sample-path optimization
Mathematics of Operations Research
Mathematical programming models of discrete event system dynamics
Proceedings of the 32nd conference on Winter simulation
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 35th conference on Winter simulation: driving innovation
Mathematical Programming: Series A and B
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Proceedings of the 38th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Mathematical programming-based perturbation analysis for GI/G/1 queues
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A time-based decomposition algorithm for fast simulation with mathematical programming models
Proceedings of the Winter Simulation Conference
Simulation-optimization of flow lines: an lp-based bounding approach
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
Optimization-via-simulation consists in applying iteratively two detached models until an optimality condition is reached: a simulation model for predicting the system performance, and a model for generating potential optimal solutions. Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This paper proposes explicit mathematical programming representations for jointly simulating and optimizing discrete event systems. The main advantage of such models is the rapidity of searching for the optimal solution, given to the explicit knowledge of objective function and constraints. Three types of formulations are proposed for solving the buffer allocation problem in flow lines with finite buffer capacities: an exact mixed integer linear model, an approximate LP model and a stochastic programming model. Numerical analysis shows that the computational time required to solve resource allocation problems can be significantly reduced by using the proposed formulations.