Timetable Optimization Leads to Time Savings and Higher Employee Satisfaction
With more than 450 employees, RAVU is one of the largest ambulance care providers in the Netherlands. To realize this care, people have to be ready day and night. The employees of RAVU have many different employment contracts and schedule preferences, which leads to a considerable schedule puzzle. To easily respond to these schedule preferences and save planners time, MKB Datalab has developed a schedule optimization tool.
The challenge
For a company such as RAVU, which has a relatively large number of employees (~30 people) and has to divide day, evening, night and weekend shifts, planning is a complex task. A standard number of employees has been determined for each service type, who must be scheduled for a minimum number of hours. In addition, there are also service-related restrictions that must be taken into account, such as a maximum of 4 night shifts in a row. The number of hours to be worked per week per employee (based on the type of employment) must also be included. This gives the planner a complex and time-consuming task.
The result
For this project, MKB Datalab has developed a tool that performs most of the roster optimization, with more eye for the employee. The tool is used every (half) year by RAVU and takes a lot of work away from the planner.
The input of the tool is an Excel file with the grids created by the employee. This contains their ideal 8-week grid, which meets the restrictions. At the front, team managers ensure that not everyone is structurally free on the same day.
The tool is based on a Python script that first reads preferences and then optimizes them based on the required capacity. Because the roster is made for half a year, the roster preferences of the employees can be moved: who’s roster starts when? In the optimization, a preference can also be given for optimizing the night shifts, since these are normally more difficult to fill.
The optimized schedule is exported to an Excel format. It also makes clear which days are still lacking capacity or are overstaffed. Based on this, the planner only needs to make a few final adjustments in consultation with the employees!
Testimonial
Testimonial
“Thanks to the JADS MKB Datalab, we can now obtain an optimal distribution for, among other things, night shifts, at the push of a button. The collaboration was pleasant and flexible. Because the student asked critical questions, we also had to think about our processes.”
Astrid Poppelaars – RAVU
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