Abstract
Abstract Algorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an archive, can allow the user an overview of the solution space which may be useful when formulating policy around the problem itself. The number of solutions that can potentially be returned by MAP-Elites is controlled by a parameter d that discretises the user-defined features into ‘bins’. For a fixed evaluation budget, increasing the number of bins increases user-choice, but at the same time, may lead to a reduction in overall quality of solutions. We undertake a study of the application of Map-Elites to a Workforce Scheduling and Routing problem, using a set of realistic instances based in London.
Highlights
Introduction and MotivationOptimisation has traditionally focused upon providing specific solutions to a given problem
As we consider more complex real-world scheduling problems, optimisation methods that present a single-solution to the user become less attractive
Space and the effects of constraining or relaxing characteristics. That this allows the user a greater degree of choice from a policy making perspective
Summary
Introduction and MotivationOptimisation has traditionally focused upon providing specific solutions to a given problem. As we consider more complex real-world scheduling problems, optimisation methods that present a single-solution to the user become less attractive Such real-world problems typically encompass multiple solution objectives and characteristics, it is no longer a case of searching for the one optimal solution and presenting that to the user. The adoption of manyobjective optimisation methods for such problems [4] allowed the generation of a non-dominated front By generating such a front, the user (typically a planner) is presented with a set of solutions that show the best possible trade-offs between objectives. In this paper we discuss the scenario of an an expert user being presented with a wider range of high-quality solutions using a quality-diversity (QD) algorithm [10] This allows the user a greater understanding of the solution space and the effects of constraining or relaxing characteristics. In [2], the authors examine the trade off between the cost of solutions and client inconvenience within another home-care scheduling problem
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