---
title: "Expected Solution Quality"
authors:
  - "John L. Bresina"
  - "Mark Drummond"
  - "Keith Swanson"
doi: ""
url: "https://ijcai.org/proceedings/1995/204"
pdf: "https://ijcai.org/proceedings/1995/0204.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "1583-1590"
track: "Planning"
keywords:
---

# Expected Solution Quality

John L. Bresina, Mark Drummond, Keith Swanson

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, pages 1583-1590.

## Abstract

This paper presents th e Expected Solution Quality (ESQ) method for statistically characterizing scheduling problems and the performance of schedulers. The ESQ method is demonstrated by applying it to a practical telescope scheduling problem . The method addresses the important and difficult issue of how to meaningfully evaluate the performance of a scheduler on a constrained optimization problem for which an optimal solution is not known. At the heart of ESQ is a Monte Carlo algorithm that estimates a problem's probability density function with respect to solution quality. This "quality density function" provides a useful characterization of a scheduling problem, and it also provides a background against which scheduler performance can be meaningfully evaluated . ESQ provides a unitless measure that combines both schedule quality and the amount of time to generate a schedule.