Multi-objective Large-Scale Staff Allocation

Roberto Anzaldua, Christina Burt, Harry Edmonds (), Karsten Lehmann and Guangyan Song
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Roberto Anzaldua: Satalia
Christina Burt: Satalia
Harry Edmonds: Satalia
Karsten Lehmann: Satalia
Guangyan Song: Satalia

A chapter in Operations Research Proceedings 2017, 2018, pp 573-579 from Springer

Abstract: Abstract Satalia is working with a multinational company that needs to have a team of 10 people spend 4 months every year manually assigning 1,000 staff to perform 10,000 jobs on 2,000 projects. This is a massive undertaking, in part because of the scale of the problem and in part because the problem is multi-objective with 57 hard and soft business rules. The task can be formulated as a large-scale scheduling problem. We demonstrate that our optimisation methods can unlock substantial savings in company work-hours while also improving quality as measured across a range of objectives. In this paper, we will outline the heuristic and exact approaches utilised, describe some of the many challenges of such a real-world problem, and show how we overcame them.

Keywords: scheduling; staff allocation; Local Neighbourhood Search; mixed-integer programming; multi-objective optimisation (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-89920-6_76

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