Explicit Learning: an Effort towards Human Scheduling Algorithms

TL;DR

This paper explores the development of a human-inspired scheduling algorithm that addresses the limitations of traditional genetic algorithms, aiming for a more general and constraint-aware approach to complex scheduling problems.

Contribution

It introduces a novel explicit learning method that enhances genetic algorithms for better handling constraints and solution modifications in scheduling tasks.

Findings

Improved constraint handling in genetic algorithms.

Enhanced solution adaptability to small changes.

Potential for more general scheduling algorithms.

Abstract

Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem.