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Optimising Training for Service Delivery

Authors Ilankaikone Senthooran , Pierre Le Bodic , Peter J. Stuckey

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Ilankaikone Senthooran
  • Data Science & AI, Monash University, Clayton, Australia
Pierre Le Bodic
  • Data Science & AI, Monash University, Clayton, Australia
Peter J. Stuckey
  • Data Science & AI, Monash University, Clayton, Australia

Acknowledgements

We are grateful for our industry partner for this opportunity to work on a challenging real-life workforce planning problem and for the many discussions that have allowed us to conduct this work.

Cite As Get BibTex

Ilankaikone Senthooran, Pierre Le Bodic, and Peter J. Stuckey. Optimising Training for Service Delivery. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 48:1-48:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.CP.2021.48

Abstract

We study the problem of training a roster of engineers, who are scheduled to respond to service calls that require a set of skills, and where engineers and calls have different locations. Both training an engineer in a skill and sending an engineer to respond a non-local service call incur a cost. Alternatively, a local contractor can be hired. The problem consists in training engineers in skills so that the quality of service (i.e. response time) is maximised and costs are minimised. The problem is hard to solve in practice partly because (1) the value of training an engineer in one skill depends on other training decisions, (2) evaluating training decisions means evaluating the schedules that are now made possible by the new skills, and (3) these schedules must be computed over a long time horizon, otherwise training may not pay off. We show that a monolithic approach to this problem is not practical. Instead, we decompose it into three subproblems, modelled with MiniZinc. This allows us to pick the approach that works best for each subproblem (MIP or CP) and provide good solutions to the problem. Data is provided by a multinational company.

Subject Classification

ACM Subject Classification
  • Theory of computation → Integer programming
  • Theory of computation → Constraint and logic programming
Keywords
  • Scheduling
  • Task Allocation
  • Training Optimisation

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