Name
Abstract | ||
|---|---|---|
As we have argued in previous papers, multi-level decision problems can often be modeled as multi-stage stochastic programs, and hierarchical planning systems designed for their solution, when viewed as stochastic programming heuristics, can be subjected to analytical performance evaluation. The present paper gives a general formulation of such stochastic programs and provides a framework for the design and analysis of heuristics for their solution. The various ways to measure the performance of such heuristics are reviewed, and some relations between these measures are derived. Our concepts are illustrated on a simple two-level planning problem of a general nature and on a more complicated two-level scheduling problem. |
Year | DOI | Venue |
|---|---|---|
1984 | 10.1007/BF01874450 | Annals OR |
Keywords | Field | DocType |
probabilistic analysis,and phrases hierarchical planning problem,stochastic programming,performance measure,heuristic,machine scheduling.,asymptotic optimality,system design,decision problem,scheduling problem | Mathematical optimization,Decision problem,Heuristic,Business system planning,Machine scheduling,Job shop scheduling,Computer science,Probabilistic analysis of algorithms,Heuristics,Stochastic programming | Journal |
Volume | Issue | ISSN |
1 | 1 | 1572-9338 |
Citations | PageRank | References |
5 | 2.41 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. K. Lenstra | 1 | 1689 | 329.39 |
| A. H. G. Rinnooy Kan | 2 | 2109 | 497.45 |
| L. Stougie | 3 | 128 | 23.07 |