Research Output

Evolving solution choice and decision support for a real-world optimisation problem

  Agencies who provide social care services typically have to optimise staff allocations and the travel whilst attempting to satisfy conflicting objectives. In such cases it is desirable to have a range of solutions to choose from, allowing the agency's planning staff to explore the various options available This paper examines the use of multi-objective evolutionary algorithms to produce solutions to the Workforce Scheduling and Routing Problem (WSRP) formulated with three objectives which should be minimised: financial cost, CO2 emissions and car use. We show that financial cost and CO2 increase with the size of the problem and the imposed constraints. In order to support the planning staff in their decision making, we present an Evolutionary Algorithm based support tool that will identify a group of solutions from the Pareto front which match criteria specified by the planner. We demonstrate that our approach is able to find a wide range of solutions, which enhance the flexibility of the agency’s choices, the decision support tool subsequently allows the planner to discover small groups of solutions that meet their specific requirements.

  • Date:

    01 July 2017

  • Publication Status:


  • Publisher

    Association for Computing Machinery

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

  • Funders:

    Edinburgh Napier Funded


Urquhart, N., & Fonzone, A. (2017). Evolving solution choice and decision support for a real-world optimisation problem. In Proceedings of the Genetic and Evolutionary Computation Conference 2017, (1264-1271).



Evolutionary Algorithms, Transportation, Multi-Objective Optimisation, Decision Support, Real-World Problems

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