2018/05/29 by Neil Urquhart, Urquhart, Neil, Emma Hart +1 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Optimization and Search Problems #Reinforcement Learning in Robotics #Scheduling and Optimization Algorithms
paper · pdf · doi:10.48550/arxiv.1805.11555
openalex publication_date 2018/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.