Name
Abstract | ||
|---|---|---|
In this paper, we present a modification of the stochastic ruler method for solving discrete stochastic optimization problems. Our method generates a stationary Markov chain sequence taking values in the feasible set of the underlying discrete optimization problem. The number of visits to every state by this Markov chain is used to estimate the optimal solution. Unlike the original stochastic ruler method, our method is guaranteed to converge almost surely to a global optimal solution. We present empirical results that illustrate the performance of our method, and we show that these results compare favorably with empirical results obtained using the original stochastic ruler method. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1145/256562.256698 | Winter Simulation Conference |
Keywords | Field | DocType |
feasible set,empirical result,original stochastic ruler method,optimal solution,discrete stochastic optimization problem,stochastic ruler method,underlying discrete optimization problem,markov chain,global optimal solution,stationary markov chain sequence,logistics,discrete optimization,industrial engineering,modeling,simulated annealing,stochastic resonance,stochastic optimization,random variables,global optimization,stochastic processes | Simulated annealing,Mathematical optimization,Stochastic optimization,Discrete-time stochastic process,Computer science,Markov chain,Stochastic process,Continuous-time stochastic process,Feasible region,Stochastic resonance | Conference |
ISBN | Citations | PageRank |
0-7803-3383-7 | 4 | 1.15 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmoud H. Alrefaei | 1 | 90 | 10.14 |
| Sigrún Andradóttir | 2 | 548 | 55.09 |