Efficient Adaptive Implementation of the Serial Schedule Generation Scheme using Preprocessing and Bloom Filters

TL;DR

This paper introduces an efficient implementation of the serial schedule generation scheme using preprocessing and Bloom filters, significantly improving speed in scheduling metaheuristics through dynamic algorithm selection.

Contribution

It presents a novel approach combining Bloom filters and automated parameter control to accelerate schedule generation, with dynamic selection for optimal performance.

Findings

Significant speedup over traditional implementations

Effective use of Bloom filters for schedule generation

Dynamic algorithm selection improves overall performance

Abstract

The majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. Thus, a crucial part of such metaheuristics is a "schedule generation scheme" -- procedure translating the indirect solution representation into a schedule. Schedule generation scheme is used every time a new candidate solution needs to be evaluated. Being relatively slow, it eats up most of the running time of the metaheuristic and, thus, its speed plays significant role in performance of the metaheuristic. Despite its importance, little attention has been paid in the literature to efficient implementation of schedule generation schemes. We give detailed description of serial schedule generation scheme, including new improvements, and propose a new approach for speeding it up, by using Bloom filters. The results are further strengthened by automated control of parameters. Finally, we employ online algorithm selection to dynamically choose which of the two implementations to use. This hybrid approach significantly outperforms conventional implementation on a wide range of instances.