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Instance dataset for a multiprocessor scheduling problem with multiple time windows and time lags: Similar instances

Emil Karlsson^1,2, Elina Rönnberg¹

¹Department of Mathematics, Linköping University, SE-581 83 Linköping, Sweden.

|November 25, 2022

Summary

This study introduces a new dataset of 384 instances for multiprocessor scheduling problems, revealing significant performance variations for constraint programming solvers. This resource aids in understanding solver behavior and benchmarking scheduling methods.

Keywords:

Avionics scheduling Constraint programming Exact time lags Instance dataset Instances Multiple time windows Multiprocessor scheduling Time lags

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Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

Multiprocessor scheduling problems are complex optimization challenges.
Existing datasets may not fully capture performance variations in constraint programming solvers.
Logic-based Benders decomposition is a technique used for solving complex scheduling problems.

Purpose of the Study:

To introduce a novel dataset of 384 instances for the feasibility version of a multiprocessor scheduling problem.
To highlight the significant variability in computational performance of solving similar instances using constraint programming solvers.
To provide a resource for investigating solver performance and benchmarking scheduling algorithms.

Main Methods:

Dataset construction based on subproblems from a logic-based Benders decomposition scheme.
Instances feature multiple time windows, positive time lags, and exact time lags.
Utilizing IBM ILOG CP Optimizer for computational performance evaluation.

Main Results:

Demonstration of vastly different solving times for highly similar instances.
Identification of instance pairs where one is solved in minutes and another takes over 24 hours.
Highlighting the dataset's utility for analyzing constraint programming solver performance.

Conclusions:

The new dataset is valuable for understanding computational performance differences in constraint programming solvers.
The dataset serves as a benchmark for multiprocessor scheduling methods.
The dataset is openly available under a Creative Commons license for research and adaptation.