Tactical Plan Optimisation for Large Multi-Skilled Workforces Using a Bi-Level Model

Russell Ainslie, John McCall, Sid Shakya, Gilbert Owusu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The service chain planning process is a critical component in the operations of companies in the service industry, such as logistics, telecoms or utilities. This process involves looking ahead over various timescales to ensure that available capacity matches the required demand whilst maximizing revenues and minimizing costs. This problem is particularly complex for companies with large, multi-skilled workforces as matching these resources to the required demand can be done in a vast number of combinations. The vastness of the problem space combined with the criticality to the business is leading to an increasing move towards automation of the process in recent years. In this paper we focus on the tactical plan where planning is occurring daily for the coming weeks, matching the available capacity to demand, using capacity levers to flex capacity to keep backlogs within target levels whilst maintaining target levels for provision of new revenues. First we describe the tactical planning problem before defining a bi-level model to search for optimal solutions to it. We show, by comparing the model results to actual planners on real world examples, that the bi-level model produces good results that replicate the planners' process whilst keeping the backlogs closer to target levels, thus providing a strong case for its use in the automation of the tactical planning process.

Original languageBritish English
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060177
DOIs
StatePublished - 28 Sep 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • Bi-level
  • GA
  • genetic algorithm
  • linear programming
  • optimisation
  • tactical planning

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