The Library
Efficient solution selection for two-stage stochastic programs
Tools
Tools
Tools
Fei, Xin, Gulpinar, Nalan and Branke, Jürgen (2019) Efficient solution selection for two-stage stochastic programs. European Journal of Operational Research, 277 (3). pp. 918-929. doi:10.1016/j.ejor.2019.02.015
|
PDF
WRAP-efficient-solution-selection-two-stage-stochastic-Branke-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial-Share Alike 4.0. Download (1450Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ejor.2019.02.015
Abstract
Sampling-based stochastic programs are extensively applied in practice. However, the resulting models tend to be computationally challenging. A reasonable number of samples needs to be identified to represent the random data, and a group of approximate models can then be constructed using such a number of samples. These approximate models can produce a set of potential solutions for the original model. In this paper, we consider the problem of allocating a finite computational budget among numerous potential solutions of a two-stage linear stochastic program, which aims to identify the best solution among potential ones by conducting simulation under a given computational budget. We propose a two-stage heuristic approach to solve the computational resource allocation problem. First, we utilise a Wasserstein-based screening rule to remove potentially inferior solutions from the simulation. Next, we use a ranking and selection technique to efficiently collect performance information of the remaining solutions. The performance of our approach is demonstrated through well-known benchmark problems. Results show that our method provides good trade-offs between computational effort and solution performance.
| Item Type: | Journal Article | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
||||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||||
| Journal or Publication Title: | European Journal of Operational Research | ||||||||||
| Publisher: | Elsevier Science BV | ||||||||||
| ISSN: | 0377-2217 | ||||||||||
| Official Date: | 19 September 2019 | ||||||||||
| Dates: |
|
||||||||||
| Volume: | 277 | ||||||||||
| Number: | 3 | ||||||||||
| Page Range: | pp. 918-929 | ||||||||||
| DOI: | 10.1016/j.ejor.2019.02.015 | ||||||||||
| Status: | Peer Reviewed | ||||||||||
| Publication Status: | Published | ||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
