Test instances
The here provided are instances are formatted in the
qlp file format.
Note that in some instances universally quantified
constraints are still indicated by a constraint name
starting with "U_", rather than using the keyword
UNCERTAINTY SUBJECT TO. When creating new instance
always use the keyword.

Artificial QIP instances
Quantified Integeger Programs (QIP) are QMIPs with only
integer variables. A special case of QIPs are Quantified
Boolean Programs (QBP) with only boolean variables. The
constraints are linear and an objective typically exists.
QIP test instances

Multistage Robust Selection Problem
The task is to select p out of n=2p items, such that the
costs are minimized^{1 2}. This happens in a
multistage manner: In a first (existential) decision stage
items can be selected for fixed costs. Then, in a universal
decision stage, one of N cost scenario is selected and in
the subsequent existential decision stage further items can
be selected. Those two stages (disclosure of a scenario and
selection of items) are repeated S times. We provide two
different quantified models: a standard QIP and a QIP with
universal constraints, i.e. a QIP with polyhedral
uncertainty set. Selection instances have the naming
scheme selectionnpNSRt.qlp. R is the seed for the
random number generator. t∈{s,u} represents the model
type: s for "standard QIP" and u for "QIP with universal
constraints". We provide the following testsets:

N=4, n∈{10,20,30,40,50} and S∈{1,..,8}.
50 instances per constellation:

n=10, N=2^k, k∈{1,..,8} and S=s∈{1,..,8}
with k+s≤11. 50 instances per constellation:

Multistage Robust Assignment Problem
For a weighted bipartite graph G=(V,E,c) with V=A∪B,
n=A=B one wants to determine a perfect matching
of minimum costs^{1 2}. Similar to the
selection problem,
this happens in a multistage manner: In a first
(existential) decision stage edges can be selected
for fixed costs. Then, in a universal decision stage,
one of N cost scenario is selected and in the subsequent
existential decision stage further edges can be selected.
Those two stages (disclosure of a scenario and selection
of edges) are repeated S times. Again, we provide two
different quantified models: a standard QIP and a QIP
with universal constraints. Assignment instances have
the naming scheme selectionnNSRt.qlp. R is the seed
for the random number generator. t∈{s,u} represents
the model type: s for "standard QIP" and u for "QIP
with universal constraints". We provide the following
testset:

n∈{4,5,6,7,8,9,10}, N∈{2,4,8} and S∈{1,2,3,4}.
50 instances per constellation:

Multistage Robust LotSizing Problem
We consider a single item lotsizing problem with
discrete ordering decisions and T periods^{2}.
At the beginning of each time period, the product demand
that needs to be satisfied is disclosed: Either
d_{t}
or d_{t}.
In order to serve the demand, in each period
t∈{0,...,T1} one of B basic orders can be places
resulting in the deliverance of corresponding quantity
at the beginning of the subsequent period. Additionally,
on of U urgent orders can be placed in each period
t∈{1,...,T} (with higher costs than the basic order),
that is delivered in the same period. If the available
quantity exceeds the demand, excess units are stored.
LotSizing instances have the naming scheme
LotSizingTBUT.qlp. R is the seed for the random
number generator. We provide the following testset:

T∈{5,6,7,8,9,10}, B∈{3,4} and U∈{2,3}.
50 instances per constellation:

Multistage Robust Knapsack Problem
The task is to find a valid knapsack solution for each of
T stages with n available items, where uncertainty in the
item weights has been added^{2}. The number of
items with increased weight is budgeted: in each time
step the weight of at most α items can be increased
and overall at most β such increases are allowed. The
objective ist to maximize the profit resulting from
the selected knapsack items but additionally a
transition bonus is used to aim for a stable sequence
of solutions. Knapsack instances have the naming
scheme Knapsack_TnR.qlp. R is the seed for the
random number generator. We provide the following testset:

n∈{2,...,7} and T∈{2,...,7}. 50 instances per
constellation:

Runway Scheduling Instances
For a set of airplanes A, a set of timeslots S, and b
runways we are interested in a bmatching such that each
airplane is assigned to exactly one time slot and each
time slot contains at most b airplanes^{1}.
After an initial plan is determined, the time windows
(a set of time slots) for a (sub)set of airplanes are
disclosed. These airplanes must be assigned to their
final time slot (within the given time window). This
happens in a multistage manner with s being the number
of disclosures, i.e. the number of universal variable
blocks, e.g. s=2 stands for the quantification
sequence ∃ ∀ ∃ ∀ ∃. The universal player is restricted
in the way she is allowed to select the time windows:
the overall time window lengths must exceed a given
value. This can be modeled either explicitly by using
universal constraints or implicitly by adding variables
and constraint that detect and penalize such a violation.
For a more thorough introduction we refer to this
Ph.D. thesis or this
site. Runway
scheduling instances have the naming scheme
RWSAbSsRt.qlp. Again, R is the seed for the
random number generator. t∈{s,u} represents the model
type: s for "standard QIP" and u for "QIP with
universal constraints". We provide the following
testsets:

A∈{2,...,8}, S∈{3,...,10}, b∈{2,3,4} with s=1.
20 instances per constellation:

A=7, S=12, b=3 and s∈{1,..,4}. 300 instances
per constellation:

Other instances
The Quantified Satisfiability problem (QSAT), which is
also know as the satisfiability of Quantified Boolean
Formulas (QBF), is related to the QMIPproblem. In QSAT
there is no objective function and all variables are
binary. Further, it deals with clauses instead of
arithmetic linear inequalities. However, with some
effort and good will, it is possible to extract very
special QMIPs from QSAT instances. Similar is the
situation with MIPs, which deal with linear constraints
but without quantification.
Here, we present some further examples from these two
subdisciplines, collected from
qbflib.org
and
miplib and converted to QLPformat.
Instance collection IP based
Instance collection MIP based
Instance collection QBF based
Please note that not all instances have been converted
1:1 due to some border cases in the conversion.

^{1} Further details can be found in this
[Ph.D. thesis]
^{2} Further details can be found in this
[paper].


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