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Tactical Plan Optimisation for Large Multi-Skilled Workforces Using a Bi-Level Model
- Source :
- CEC
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
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.
- Subjects :
- Matching (statistics)
Linear programming
Operations research
Computer science
business.industry
Process (engineering)
media_common.quotation_subject
05 social sciences
050301 education
Tactical planning
02 engineering and technology
Automation
Capacity planning
Service (economics)
0202 electrical engineering, electronic engineering, information engineering
Revenue
020201 artificial intelligence & image processing
business
0503 education
Tertiary sector of the economy
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE Congress on Evolutionary Computation (CEC)
- Accession number :
- edsair.doi...........46e454511120acb7db67174caf9bee11
- Full Text :
- https://doi.org/10.1109/cec.2018.8477701