Quantified Programs applied to Runway Scheduling

The following QIP problem definition and, as a consequence the content of this page, is a result of cooperative work between University Siegen (S. Gnad, M. Hartisch, U. Lorenz) and FAU Erlangen (L. Hupp, F. Liers, A. Peter). We used QIPs to model and solve a matching problem that can be interpreted as an airplane scheduling problem in which each airplane must be assigned to a time slot and at most b airplanes can be assigned to one time slot. This b-matching is enhanced by uncertain time intervals in which an airplane must land. For reasons of simplicity we will use the airplane scheduling interpretation to explain our intentions.

Broadly speaking, we are interested in an initial plan that can be fixed cheaply if the mandatory time windows (the sliders in the figure) for some planes do not contain the initially scheduled time slot. Reasons for such variations (in the arrival time) might be adjusted airspeed (due to weather) or operational problems.

The incurred costs are composed of the costs for the initial plan and the fixing costs. The costs for the initial plan only depend on the the initial assignment of planes to time slots regarding predetermined costs: In the example above assigning airplane 2 to time slot 4 would result in costs of 2 monetary units.

There are some ideas for the composition of the fixing costs, for example:

• rescheduling one airplane results in a fixed fee
• rescheduling one airplane results in costs depending on the newly selected time slot
• rescheduling one airplane results in costs depending on the initial and the newly selected time slot

For simplicity and a more general presentation, the costs of replacing airplane i depend on a function f(x_i*,y_i*) representing the relation between initial plan, fixed plan and fixing costs. Depending on the selected cost type, this function can be modeled using linear constraints.

Basic quantified program for the airplane runway scheduling problem:

Brief explanation of the model:

1. three stage objective function
   • first stage: select initial plan resulting in initial costs
   • second stage: uncertain events → new conditions regarding allowed time slots (the start (s_i∈S) and the length (l_i ∈L) of the time window is selected for each airplane i)
   • third stage: if necessary fix the initial plan causing additional costs
2. First stage existential variables: Initial scheduling variables
3. Second stage universal variables: Specification of the starting point (s) and the length (l) of the mandatory time window for each airplane
4. Third stage existential variables: Final scheduling variables respecting the time windows defined in stage two
5. Ensures that each airplane is assigned to exactly one time slot in the initial plan
6. Ensures that each time slot can only hold b airplanes in the initial plan
7. Ensures that each airplane is assigned to exactly one time slot in the fixed plan
8. Ensures that each time slot can only hold b airplanes in the fixed plan
9. Binds the assigned time slots of the fixed plan to the given time window
10. Fixing costs depend on difference between initial plan (X) and fixed plan (Y); various cost models imaginable.

Example costs: fixed fee

If, for example, the first mentioned fixing costs were used, i.e. fixed fee for replanning, further existential variables Z∈{0,1}^|A|×|W| are installed in the third stage and the following constraints would be added:

In this case variable z_ij must identify if airplane i was not scheduled in time slot j in the initial plan but in the fixed plan resulting in costs f for this plane.

Restricting the universal variables

By choosing the domains of the universal variables S and L carefully the user already can limit the influence of the universal variables. Nevertheless, some scenarios should not be considered: For example one might want to allow the time windows for some airplane to consist of only one time slot. However, this should not be the case for all airplanes, since this would constitute a rather implausible event. One conceivable demand for the time windows could be that on average the time windows have a length of 2 (i.e. consist of two time slots). Thus, the universal variables L should not only be forced to lie within some bounds, but also within a specific polytope. The polytope for this example would require the following additional constraint:

However, simply adding this constraint to the constraint system would not have the desired effect. In fact, it would increase the influence of the universal variables since this constraint could easily be violated and thus the entire instance would become infeasible. However, adding this as a universal constraint would do the trick. or the enforcement of rules regarding the universal variables could be performed implicitly: In a final existential block the fulfillment of such a constraint is checked and if a violation is detected the remaining constraint system is relaxed and the objective value is reduced dramatically. This has the effect that a violation provoked by the allocation of universal variables results in a very good objective value (regarding the existential objective of minimization) and is thus unfavorable with respect to the universal maximization objective.

Instances

For this runway scheduling problem we created several instances with several variations:

• Instances with more than 3 stages: After the initial plan the time windows for some airplanes are selected by the universal variables. For these airplanes a fixed plan must be prepared. After that the time windows for the remaining airplanes are specified by the universal variables and once again the plan must be fixed.
• Instances with restricted universal variables: As explained above, the universal variables determining the interval lengths must obey some rules.
• Instances with different objective function: the first (fixed fee) and third (distance from initial and final time slot) presented fixing costs are considered.
• Instances with diverse time windows: The interval length can vary from 0 up to 4 time slots and the interval itself can start at up to 4 time slots.
• Instances with different numbers of airplanes and time slots: the number of airplanes varies between 5 and up to more than 100. The number of time slots varies between 7 and up to 200 time slots.

Download Instances

For each of these 29 instances our solver Yasol (utilizing the Cplex LP solver) had one hour to solve the instance. The results where compared to Cplex trying to solve the corresponding deterministic equivalent program, also within one hour. The results are displayed in the following table. Yasol solves 25 out of the 29 instances while CPLEX only can solve 6 of the converted DEPs. Even when only considering the instances solved by CPLEX, Yasol only needs 2.66 seconds on average compared to 6.83 seconds. If instances get large (easy indicator is the number right after the 'A' in the instance name) Yasol is able to detect infeasible instances rather fast but does not manage to grasp optimal feasible solutions. CPLEX, on the other hand, often exceeds the available memory on such instances and does not cope very well if many universal variables are present. But even on instances with few universal variables CPLEX quickly reaches its limit.

[1] Hartisch M., Ederer T., Lorenz U., Wolf J. (2016) Quantified Integer Programs with Polyhedral Uncertainty Set. In: Plaat A., Kosters W., van den Herik J. (eds) Computers and Games. CG 2016. Lecture Notes in Computer Science, vol 10068. Springer, Cham