Algorithms Can Help You with Scheduling Problems


Does your company need to schedule work shifts, holidays, deliveries, or production line operations? Do you have some other scheduling problem that takes up a lot of time? Perhaps you have decided to just copy the previous schedule and change it as little as required to save time? By using algorithms to optimize scheduling problems you can save time and improve the results.

Optimization algorithms can find the most efficient solutions to your problem in seconds. If your tasks include the scheduling of work shifts of hundreds of employees for the next quarter, take-offs and landings of flights, or the use and maintenance plans of machines, for instance, it is worthwhile to utilize algorithms. Almost all scheduling problems can be optimized. If you can recognise the minimization or maximization objectives or the constraints that the final schedule must fulfill, you have already come far.

Using algorithms to optimize scheduling problems

Let’s discuss how optimization models are formed through simple examples. Optimization models are formed of the objective, decision variables and constraints. The objective is a meter that must be minimized or maximized. For example, if you must plan drivers’ work shifts, you may seek to minimize the use of part-time drivers or the variation between the length of shifts. On the other hand, you may seek to fulfil as many holiday wishes as possible or maximize the profitability of operations. The decisions that the algorithm is used to find solutions for are called decision variables.

In the driver work shift example, driver assignments to shifts are decision variables. The requirements set for the decisions are expressed as constraints that can be used to take different matters into account, such as the Working Hours Act or the drivers’ qualifications for performing different tasks.

Below are some examples of typical scheduling problems:

  • Shift planning
  • Holiday planning
  • Scheduling of surgeries performed in operating theatres
  • Planning of machine maintenance schedules
  • Scheduling of the use of a harbour, airport, or similar premises
  • Scheduling the manufacturing of different products on the production line

Typical objectives include:

  • Cost minimization / profit maximization
  • Maximization of the equal treatment of persons
  • Maximization of delivery speed
  • Minimization of the required resources
  • Other objectives or a combination of the ones listed above

Typical constraints can be:

  • Certain task requires a person or equipment with a specific quality
  • Tasks have a specific order
  • Each resource can only perform a restricted number of tasks at once
  • Work cannot be carried out for too long periods at once
  • Some tasks are alternative with one another

If you would like to read more about the benefits of optimization algorithms, check out this article: Why prescriptive analytics and decision optimization are crucial.

Scheduling optimization demo video

We have also made a demo video about using algorithms to optimize scheduling problems. The video illustrates the optimization of construction work and construction machinery maintenance. Each task requires a specific type of machine. However, each working hour brings the planned maintenance of the chosen machine one step closer. The video illustrates how an algorithm creates a scheduling plan and a cost estimation. It also shows how the results can be used to conclude whether there is a lack of resources and how the scenarios are used to investigate the benefits of making new investments.

If you would like to learn more about our advanced analytics and optimization solutions, check out the solution page.

Visa Linkiö
Data Scientist