Tactical plan optimisation for large multi-skilled workforces using a bi-level model.

Ainslie, Russell; McCall, John; Shakya, Sid; Owusu, Gilbert

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.

Citation

AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2018. Tactical plan optimisation for large multi-skilled workforces using a bi-level model. In Proceedings of Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article ID 8477701. Available from: https://doi.org/10.1109/CEC.2018.8477701

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2018)
• Start Date: Jul 8, 2018
• End Date: Jul 13, 2018
• Acceptance Date: Mar 15, 2018
• Online Publication Date: Oct 4, 2018
• Publication Date: Oct 4, 2018
• Deposit Date: Jan 4, 2019
• Publicly Available Date: Jan 4, 2019
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 8477701
• DOI: https://doi.org/10.1109/CEC.2018.8477701
• Keywords: Bi-level; Tactical planning; Optimisation; Genetic algorithm; GA; Linear programming
• Public URL: http://hdl.handle.net/10059/3241
• Contract Date: Jan 4, 2019