# Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites

arXiv: https://arxiv.org/abs/1805.11555 · HTML: https://mlsanity.com/paper/1805.11555/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 2 of 2)

- [Quality Diversity Evolutionary Learning of Decision Trees](https://mlsanity.com/paper/2208.12758/) (2022)
- [Optimisation of Aircraft Maintenance Schedules](https://mlsanity.com/paper/2512.17412/) (2025)

## Abstract (arXiv)

Workforce Scheduling and Routing Problems (WSRP) are very common in many practical domains, and usually, have a number of objectives. Illumination algorithms such as Map-Elites (ME) have recently gained traction in application to em design problems, in providing multiple diverse solutions as well as illuminating the solution space in terms of user-defined characteristics, but typically require significant computational effort to produce the solution archive. We investigate whether ME can provide an effective approach to solving WSRP, a em repetitive problem in which solutions have to be produced quickly and often. The goals of the paper are two-fold. The first is to evaluate whether ME can provide solutions of competitive quality to an Evolutionary Algorithm (EA) in terms of a single objective function, and the second to examine its ability to provide a repertoire of solutions that maximise user choice. We find that very small computational budgets favour the EA in terms of quality, but ME outperforms the EA at larger budgets, provides a more diverse array of solutions, and lends insight to the end-user.