Optimising Personnel Rostering Through Mixed Integer Linear Programming
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- Master of Science 
Personnel planning is an important component of business management. Improved personnel planning can lead to more satisfied employees and decreased personnel costs. This thesis explores how mixed integer linear programming (MILP) can assist the police´s operations centre with personnel planning, by allocating employees to predefined shifts following legally required work regulations. Further, the effects of recommended health-promoting shift patterns and employee satisfaction are explored. The research finds that the optimisation model considering only the legally required regulations, can provide complete schedules within a satisfactory time limit. However, when implementing recommended health-promoting shift patterns and personnel preferences, the complexity of the model increases significantly. Key findings reflect that the running time and complexity of the rostering problem increases significantly when considering all regulations, preferences, and health-promoting shift patterns. Hence, for the purpose of generating high quality solutions that considers all kinds of preferences and regulations, also the less important ones, heuristics approaches should be considered to obtain improved solutions more efficiently.
Masteroppgave(MSc) in Master of Science in Business Analytics, Handelshøyskolen BI, 2023