171 lines
7.6 KiB
MiniZinc
171 lines
7.6 KiB
MiniZinc
%
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% Main authors:
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% Guido Tack <tack@gecode.org>
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%
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% Copyright:
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% Guido Tack, 2007
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%
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% Last modified:
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% $Date: 2015-01-05 17:33:06 +1100 (Mon, 05 Jan 2015) $ by $Author: tack $
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% $Revision: 14337 $
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%
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% This file is part of Gecode, the generic constraint
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% development environment:
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% http://www.gecode.org
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%
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% Permission is hereby granted, free of charge, to any person obtaining
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% a copy of this software and associated documentation files (the
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% "Software"), to deal in the Software without restriction, including
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% without limitation the rights to use, copy, modify, merge, publish,
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% distribute, sublicense, and/or sell copies of the Software, and to
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% permit persons to whom the Software is furnished to do so, subject to
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% the following conditions:
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%
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% The above copyright notice and this permission notice shall be
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% included in all copies or substantial portions of the Software.
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%
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% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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% EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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% MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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% NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
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% LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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% OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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% WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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%
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%
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/***
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@groupdef gecode Additional declarations for Gecode
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These annotations and predicates are available for the Gecode
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solver. In order to use them in a model, include the file "gecode.mzn".
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*/
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/***
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@groupdef gecode.annotations Additional Gecode search annotations
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*/
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/** @group gecode.annotations Select variable with smallest accumulated failure count */
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annotation afc_min;
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/** @group gecode.annotations Select variable with smallest accumulated failure count divided by domain size */
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annotation afc_size_min;
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/** @group gecode.annotations Select variable with largest accumulated failure count */
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annotation afc_max;
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/** @group gecode.annotations Select variable with largest accumulated failure count divided by domain size */
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annotation afc_size_max;
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/** @group gecode.annotations Select variable with smallest activity count */
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annotation activity_min;
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/** @group gecode.annotations Select variable with smallest activity count divided by domain size */
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annotation activity_size_min;
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/** @group gecode.annotations Select variable with largest activity count */
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annotation activity_max;
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/** @group gecode.annotations Select variable with largest activity count divided by domain size */
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annotation activity_size_max;
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/** @group gecode.annotations Select random variable */
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annotation random;
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/** @group gecode.annotations Specify default search strategy for integer variables to use variable selection
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strategy \a varsel, and value choice strategy \a valsel. */
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annotation int_default_search(ann: varsel, ann: valsel);
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/** @group gecode.annotations Specify default search strategy for Boolean variables to use variable selection
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strategy \a varsel, and value choice strategy \a valsel. */
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annotation bool_default_search(ann: varsel, ann: valsel);
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/** @group gecode.annotations Specify default search strategy for set variables to use variable selection
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strategy \a varsel, and value choice strategy \a valsel. */
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annotation set_default_search(ann: varsel, ann: valsel);
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/** @group gecode.annotations Specify default search strategy for float variables to use variable selection
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strategy \a varsel, and value choice strategy \a valsel. */
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annotation float_default_search(ann: varsel, ann: valsel);
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/** @group gecode.annotations
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Simple large neighbourhood search strategy: upon restart, for each variable in \a x,
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the probability of it being fixed to the previous solution is \a percentage (out of 100).
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*/
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annotation relax_and_reconstruct(array[int] of var int: x, int: percentage);
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/** @group gecode.annotations
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Simple large neighbourhood search strategy: upon restart, for each variable in \a x,
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the probability of it being fixed to the previous solution is \a percentage (out of 100).
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Start from an initial solution \a y.
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*/
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annotation relax_and_reconstruct(array[int] of var int: x, int: percentage, array[int] of int: y);
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/***
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@groupdef gecode.constraints Additional Gecode constraints
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*/
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/** @group gecode.constraints
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Constrain \a z to be the intersection of all sets
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in \a y that are selected by \a x: \(i \in \a z \leftrightarrow \forall j \in \a x: (i \in \a y[j]) \)
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*/
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predicate gecode_array_set_element_intersect(var set of int: x,
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array[int] of var set of int: y, var set of int: z);
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/** @group gecode.constraints
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Constrain \a z to be the disjoint union of all sets
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in \a y that are selected by \a x: \(i \in \a z \leftrightarrow \exists j \in \a x: (i \in \a y[j]) \)
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and \( i \in \a x \land j \in \a x \land i\neq j \rightarrow \a y[i] \cap \a y[j]=\emptyset \)
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*/
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predicate gecode_array_set_element_partition(var set of int: x,
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array[int] of var set of int: y, var set of int: z);
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/** @group gecode.constraints
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Constrain \a z to be a subset of \a u, and \a z to be the intersection of all sets
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in \a y that are selected by \a x: \(i \in \a z \leftrightarrow \forall j \in \a x: (i \in \a y[j]) \)
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*/
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predicate gecode_array_set_element_intersect_in(var set of int: x,
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array[int] of var set of int: y,
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var set of int: z, set of int: u);
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predicate gecode_among_seq_int(array[int] of var int: x, set of int: S,
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int: l, int: m, int: n);
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predicate gecode_among_seq_bool(array[int] of var bool: x, bool: b,
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int: l, int: m, int: n);
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/** @group gecode.constraints
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Every subsequence of \a x of length \a l has at least \a m and at most \a n occurrences
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of the values in \a S
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*/
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predicate among_seq(array[int] of var int: x, set of int: S,
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int: l, int: m, int: n) = gecode_among_seq_int(x,S,l,m,n);
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/** @group gecode.constraints
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Every subsequence of \a x of length \a l has at least \a m and at most \a n occurrences
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of the values in \a S
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*/
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predicate among_seq(array[int] of var bool: x, bool: b,
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int: l, int: m, int: n) = gecode_among_seq_bool(x,b,l,m,n);
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predicate gecode_circuit_cost_array(array[int] of int: c,
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array[int] of var int: x, array[int] of var int: y, var int: z);
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predicate gecode_circuit_cost(array[int] of int: c, array[int] of var int: x,
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var int: z);
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/** @group gecode.constraints
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Constrains the elements of \a x to define a circuit where \a x[\p i] = \p j means
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that \p j is the successor of \p i. Additionally, constrain \a z to
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be the cost of the circuit. Each edge cost is defined by array \a c. The variables
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\a y[i] are constrained to be the edge cost of the node \a x[i].
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*/
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predicate circuit_cost_array(array[int] of int: c,
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array[int] of var int: x, array[int] of var int: y, var int: z) =
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gecode_circuit_cost_array(c,[x[i]-min(index_set(x)) | i in index_set(x)],
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y,z);
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/** @group gecode.constraints
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Constrains the elements of \a x to define a circuit where \a x[\p i] = \p j means
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that \p j is the successor of \p i. Additionally, constrain \a z to
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be the cost of the circuit. Each edge cost is defined by array \a c.
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*/
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predicate circuit_cost(array[int] of int: c, array[int] of var int: x,
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var int: z) =
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gecode_circuit_cost(c, [x[i]-min(index_set(x)) | i in index_set(x)], z);
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predicate gecode_schedule_unary(array[int] of var int: x, array[int] of int: p);
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predicate gecode_schedule_unary_optional(array[int] of var int: x,
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array[int] of int: p, array[int] of var bool: m);
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predicate gecode_schedule_cumulative_optional(array[int] of var int: start,
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array[int] of int: duration, array[int] of int: usage,
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array[int] of var bool: m, int: capacity);
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