Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Retrospective approximation algorithms for the multidimensional stochastic root-finding problem
WSC '04 Proceedings of the 36th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Reengineering, simulation and data analysis of an RFID system
Journal of Theoretical and Applied Electronic Commerce Research
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
| Hi-index | 0.01 |
We present a method for setting release times for jobs with due dates in a stochastic production flow line for which the sequence of jobs has been determined. Unlike other approaches to this problem, ours considers a transient situation. Thus, the flow line will typically contain work in process (WIP), that is, jobs that have been previously released to the system. Our goal is to develop a job release schedule that not only minimizes tardiness but also maximizes flexibility. The philosophy can be characterized as one that seeks to "release as late as possible, but no later!" Our methodology is based on Monte Carlo simulation and consequent optimization by a method that became known as "stochastic counterpart" or "sample path" simulation-based optimization techniques. We use this method to minimize an expected value objective function that contains terms for tardiness and flow time "costs." We include a discussion of how the cost parameters of this objective function can be obtained by considering a "characteristic curve" for the system. We also discuss means for obtaining sensitivity analysis with respect to due dates and service times distributions parameters. We conclude with a numerical example.