Add constraints to a problem to ensure that each feature is represented in a contiguous unit of dispersible habitat. These constraints are a more advanced version of those implemented in the add_contiguity_constraints function, because they ensure that each feature is represented in a contiguous unit and not that the entire solution should form a contiguous unit. Additionally, this function can use data showing the distribution of dispersible habitat for each feature to ensure that all features can disperse through out the areas designated for their conservation.

# S4 method for ConservationProblem,ANY,Matrix
add_feature_contiguity_constraints(x, zones, data)

# S4 method for ConservationProblem,ANY,data.frame
add_feature_contiguity_constraints(x, zones, data)

# S4 method for ConservationProblem,ANY,matrix
add_feature_contiguity_constraints(x, zones, data)

# S4 method for ConservationProblem,ANY,ANY
add_feature_contiguity_constraints(x, zones, data)

Arguments

x

ConservationProblem-class object.

zones

matrix, Matrix or list object describing the connection scheme for different zones. For matrix or and Matrix arguments, each row and column corresponds to a different zone in the argument to x, and cell values must contain binary numeric values (i.e. one or zero) that indicate if connected planning units (as specified in the argument to data) should be still considered connected if they are allocated to different zones. The cell values along the diagonal of the matrix indicate if planning units should be subject to contiguity constraints when they are allocated to a given zone. Note arguments to zones must be symmetric, and that a row or column has a value of one then the diagonal element for that row or column must also have a value of one. If the connection scheme between different zones should differ among the features, then the argument to zones should be a list of matrix or Matrix objects that shows the specific scheme for each feature using the conventions described above. The default argument to zones is an identity matrix (i.e. a matrix with ones along the matrix diagonal and zeros elsewhere), so that planning units are only considered connected if they are both allocated to the same zone.

data

NULL, matrix, Matrix, data.frame or list of matrix, Matrix, or data.frame objects. The argument to data shows which planning units should be treated as being connected when implementing constraints to ensure that features are represented in contiguous units. If different features have different dispersal capabilities, then it may be desirable to specify which sets of planning units should be treated as being connected for which features using a list of objects. The default argument is NULL which means that the connection data is calculated automatically using the connected_matrix function and so all adjacent planning units are treated as being connected for all features. See the Details section for more information.

Details

This function uses connection data to identify solutions that represent features in contiguous units of dispersible habitat. In earlier versions of the prioritizr package, it was known as the add_corridor_constraints function but has since been renamed for clarity. It was inspired by the mathematical formulations detailed in \"Onal and Briers (2006) and Cardeira et al. 2010. For an example that has used these constraints, see Hanson, Fuller, and Rhodes (2018). Please note that these constraints require the expanded formulation and therefore cannot be used with feature data that have negative vales. Please note that adding these constraints to a problem will drastically increase the amount of time required to solve it.

The argument to data can be specified in several ways:

NULL

connection data should be calculated automatically using the connected_matrix function. This is the default argument and means that all adjacent planning units are treated as potentially dispersible for all features. Note that the connection data must be manually defined using one of the other formats below when the planning unit data in the argument to x is not spatially referenced (e.g. in data.frame or numeric format).

matrix, Matrix

where rows and columns represent different planning units and the value of each cell indicates if the two planning units are connected or not. Cell values should be binary numeric values (i.e. one or zero). Cells that occur along the matrix diagonal have no effect on the solution at all because each planning unit cannot be a connected with itself. Note that pairs of connected planning units are treated as being potentially dispersible for all features.

data.frame

containing the fields (columns) "id1", "id2", and "boundary". Here, each row denotes the connectivity between two planning units following the Marxan format. The field boundary should contain binary numeric values that indicate if the two planning units specified in the fields "id1" and "id2" are connected or not. This data can be used to describe symmetric or asymmetric relationships between planning units. By default, input data is assumed to be symmetric unless asymmetric data is also included (e.g. if data is present for planning units 2 and 3, then the same amount of connectivity is expected for planning units 3 and 2, unless connectivity data is also provided for planning units 3 and 2). Note that pairs of connected planning units are treated as being potentially dispersible for all features.

list

containing matrix, Matrix, or data.frame objects showing which planning units should be treated as connected for each feature. Each element in the list should correspond to a different feature (specifically, a different target in the problem), and should contain a matrix, Matrix, or data.frame object that follows the conventions detailed above.

References

\"Onal H and Briers RA (2006) Optimal selection of a connected reserve network. Operations Research, 54: 379--388.

Cardeira JO, Pinto LS, Cabeza M and Gaston KJ (2010) Species specific connectivity in reserve-network design using graphs. Biological Conservation, 2: 408--415.

Hanson JO, Fuller RA, & Rhodes JR (2018) Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow. Journal of Applied Ecology, In press: https://doi.org/10.1111/1365-2664.13315.

Examples

# load data data(sim_pu_raster, sim_pu_zones_stack, sim_features, sim_features_zones) # create minimal problem p1 <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.3) # create problem with contiguity constraints p2 <- p1 %>% add_contiguity_constraints() # create problem with constraints to represent features in contiguous # units p3 <- p1 %>% add_feature_contiguity_constraints() # create problem with constraints to represent features in contiguous # units that contain highly suitable habitat values # (specifically in the top 1.5th percentile) cm4 <- lapply(seq_len(nlayers(sim_features)), function(i) { # create connectivity matrix using the i'th feature's habitat data m <- connectivity_matrix(sim_pu_raster, sim_features[[i]]) # convert matrix to TRUE/FALSE values in top 20th percentile m <- m > quantile(as.vector(m), 1 - 0.015, names = FALSE) # convert matrix from TRUE/FALSE to sparse matrix with 0/1s m <- as(m, "dgCMatrix") # remove 0s from the sparse matrix m <- Matrix::drop0(m) # return matrix m }) p4 <- p1 %>% add_feature_contiguity_constraints(data = cm4)
# solve problems s1 <- stack(solve(p1), solve(p2), solve(p3), solve(p4))
#> Optimize a model with 5 rows, 90 columns and 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> Coefficient statistics: #> Matrix range [2e-01, 9e-01] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [9e+00, 2e+01] #> Found heuristic solution: objective 6571.2631720 #> Presolve time: 0.00s #> Presolved: 5 rows, 90 columns, 450 nonzeros #> Variable types: 0 continuous, 90 integer (90 binary) #> Presolved: 5 rows, 90 columns, 450 nonzeros #> #> #> Root relaxation: objective 5.894507e+03, 16 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5894.50724 0 4 6571.26317 5894.50724 10.3% - 0s #> H 0 0 6035.8622908 5894.50724 2.34% - 0s #> #> Explored 1 nodes (16 simplex iterations) in 0.00 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 2: 6035.86 6571.26 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.035862290759e+03, best bound 5.894507235784e+03, gap 2.3419% #> Optimize a model with 236 rows, 234 columns and 1202 nonzeros #> Variable types: 0 continuous, 234 integer (234 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 2e+01] #> Presolve removed 24 rows and 25 columns #> Presolve time: 0.01s #> Presolved: 212 rows, 209 columns, 945 nonzeros #> Variable types: 0 continuous, 209 integer (209 binary) #> Presolved: 212 rows, 209 columns, 945 nonzeros #> #> #> Root relaxation: objective 5.974619e+03, 113 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5974.61922 0 87 - 5974.61922 - - 0s #> 0 0 5980.28056 0 92 - 5980.28056 - - 0s #> 0 0 5981.05378 0 93 - 5981.05378 - - 0s #> 0 0 5987.71284 0 93 - 5987.71284 - - 0s #> 0 0 5988.40661 0 81 - 5988.40661 - - 0s #> 0 0 5988.66628 0 101 - 5988.66628 - - 0s #> 0 0 5988.69676 0 98 - 5988.69676 - - 0s #> 0 0 5989.42897 0 100 - 5989.42897 - - 0s #> 0 0 5998.40310 0 101 - 5998.40310 - - 0s #> 0 0 5998.77347 0 99 - 5998.77347 - - 0s #> 0 0 5998.95186 0 86 - 5998.95186 - - 0s #> 0 0 6001.39549 0 100 - 6001.39549 - - 0s #> 0 0 6001.47103 0 100 - 6001.47103 - - 0s #> 0 0 6001.47103 0 100 - 6001.47103 - - 0s #> H 0 0 6549.4237024 6001.47103 8.37% - 0s #> #> Cutting planes: #> Gomory: 1 #> Clique: 1 #> MIR: 1 #> Zero half: 10 #> Mod-K: 3 #> #> Explored 1 nodes (308 simplex iterations) in 0.09 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 1: 6549.42 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.549423702358e+03, best bound 6.001471028808e+03, gap 8.3664% #> Optimize a model with 1610 rows, 1260 columns and 5110 nonzeros #> Variable types: 0 continuous, 1260 integer (1260 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 2e+01] #> Presolve removed 194 rows and 195 columns #> Presolve time: 0.07s #> Presolved: 1416 rows, 1065 columns, 4023 nonzeros #> Variable types: 0 continuous, 1065 integer (1065 binary) #> Presolved: 1416 rows, 1065 columns, 4023 nonzeros #> #> #> Root relaxation: objective 5.974619e+03, 1232 iterations, 0.04 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5974.61922 0 489 - 5974.61922 - - 0s #> 0 0 5978.30324 0 407 - 5978.30324 - - 0s #> 0 0 5978.45612 0 508 - 5978.45612 - - 0s #> 0 0 5979.72695 0 503 - 5979.72695 - - 0s #> 0 0 5980.31974 0 547 - 5980.31974 - - 0s #> 0 0 5980.34413 0 553 - 5980.34413 - - 0s #> 0 0 5980.34687 0 554 - 5980.34687 - - 0s #> 0 0 5980.34687 0 551 - 5980.34687 - - 0s #> H 0 0 7804.3290306 5980.34687 23.4% - 0s #> 0 2 5980.36472 0 551 7804.32903 5980.36472 23.4% - 0s #> H 27 27 7007.7359682 5980.45025 14.7% 80.3 0s #> H 28 28 6379.5724215 5980.45025 6.26% 78.0 0s #> #> Cutting planes: #> Gomory: 1 #> Zero half: 11 #> #> Explored 28 nodes (4612 simplex iterations) in 0.66 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 3: 6379.57 7007.74 7804.33 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.379572421546e+03, best bound 5.980450249872e+03, gap 6.2563% #> Optimize a model with 1076 rows, 915 columns and 3541 nonzeros #> Variable types: 0 continuous, 915 integer (915 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 2e+01] #> Presolve removed 608 rows and 529 columns #> Presolve time: 0.02s #> Presolved: 468 rows, 386 columns, 1294 nonzeros #> Variable types: 0 continuous, 386 integer (386 binary) #> Presolved: 468 rows, 386 columns, 1294 nonzeros #> #> #> Root relaxation: objective 1.168422e+04, 369 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 11684.2161 0 81 - 11684.2161 - - 0s #> H 0 0 12310.758400 11684.2161 5.09% - 0s #> #> Explored 1 nodes (447 simplex iterations) in 0.03 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 1: 12310.8 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 1.231075839987e+04, best bound 1.168421614208e+04, gap 5.0894%
# plot solutions plot(s1, axes = FALSE, box = FALSE, main = c("basic solution", "contiguity constraints", "feature contiguity constraints", "feature contiguity constraints with data"))
# create minimal problem with multiple zones, and limit the solver to # 30 seconds to obtain solutions in a feasible period of time p5 <- problem(sim_pu_zones_stack, sim_features_zones) %>% add_min_set_objective() %>% add_relative_targets(matrix(0.1, ncol = 3, nrow = 5)) %>% add_default_solver(time_limit = 30) %>% add_binary_decisions() # create problem with contiguity constraints that specify that the # planning units used to conserve each feature in different management # zones must form separate contiguous units p6 <- p5 %>% add_feature_contiguity_constraints(diag(3)) # create problem with contiguity constraints that specify that the # planning units used to conserve each feature must form a single # contiguous unit if the planning units are allocated to zones 1 and 2 # and do not need to form a single contiguous unit if they are allocated # to zone 3 zm7 <- matrix(0, ncol = 3, nrow = 3) zm7[seq_len(2), seq_len(2)] <- 1 print(zm7)
#> [,1] [,2] [,3] #> [1,] 1 1 0 #> [2,] 1 1 0 #> [3,] 0 0 0
p7 <- p5 %>% add_feature_contiguity_constraints(zm7) # create problem with contiguity constraints that specify that all of # the planning units in all three of the zones must conserve first feature # in a single contiguous unit but the planning units used to conserve the # remaining features do not need to be contiguous in any way zm8 <- lapply(seq_len(number_of_features(sim_features_zones)), function(i) matrix(ifelse(i == 1, 1, 0), ncol = 3, nrow = 3)) print(zm8)
#> [[1]] #> [,1] [,2] [,3] #> [1,] 1 1 1 #> [2,] 1 1 1 #> [3,] 1 1 1 #> #> [[2]] #> [,1] [,2] [,3] #> [1,] 0 0 0 #> [2,] 0 0 0 #> [3,] 0 0 0 #> #> [[3]] #> [,1] [,2] [,3] #> [1,] 0 0 0 #> [2,] 0 0 0 #> [3,] 0 0 0 #> #> [[4]] #> [,1] [,2] [,3] #> [1,] 0 0 0 #> [2,] 0 0 0 #> [3,] 0 0 0 #> #> [[5]] #> [,1] [,2] [,3] #> [1,] 0 0 0 #> [2,] 0 0 0 #> [3,] 0 0 0 #>
p8 <- p5 %>% add_feature_contiguity_constraints(zm8)
# solve problems s2 <- lapply(list(p5, p6, p7, p8), solve)
#> Optimize a model with 105 rows, 270 columns and 1620 nonzeros #> Variable types: 0 continuous, 270 integer (270 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 8e+00] #> Found heuristic solution: objective 7019.1222763 #> Presolve time: 0.00s #> Presolved: 105 rows, 270 columns, 1620 nonzeros #> Variable types: 0 continuous, 270 integer (270 binary) #> Presolved: 105 rows, 270 columns, 1620 nonzeros #> #> #> Root relaxation: objective 5.935429e+03, 100 iterations, 0.00 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5935.42867 0 13 7019.12228 5935.42867 15.4% - 0s #> H 0 0 6082.2792264 5935.42867 2.41% - 0s #> #> Explored 1 nodes (100 simplex iterations) in 0.01 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 2: 6082.28 7019.12 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.082279226435e+03, best bound 5.935428674960e+03, gap 2.4144% #> Optimize a model with 4935 rows, 3795 columns and 15660 nonzeros #> Variable types: 0 continuous, 3795 integer (3795 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 8e+00] #> Presolve removed 369 rows and 369 columns #> Presolve time: 0.47s #> Presolved: 4566 rows, 3426 columns, 14223 nonzeros #> Variable types: 0 continuous, 3426 integer (3426 binary) #> Presolved: 4566 rows, 3426 columns, 14223 nonzeros #> #> #> Root relaxation: objective 5.964223e+03, 5545 iterations, 0.26 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5964.22270 0 817 - 5964.22270 - - 0s #> 0 0 5973.19018 0 890 - 5973.19018 - - 1s #> 0 0 5974.24060 0 951 - 5974.24060 - - 1s #> 0 0 5974.59812 0 979 - 5974.59812 - - 1s #> 0 0 5981.69061 0 939 - 5981.69061 - - 1s #> 0 0 5981.81929 0 906 - 5981.81929 - - 1s #> 0 0 5982.14288 0 956 - 5982.14288 - - 1s #> 0 0 5985.60715 0 1016 - 5985.60715 - - 1s #> 0 0 5986.15050 0 1083 - 5986.15050 - - 1s #> 0 0 5986.18663 0 1090 - 5986.18663 - - 1s #> 0 0 5986.18663 0 1090 - 5986.18663 - - 1s #> 0 0 5989.16270 0 1089 - 5989.16270 - - 1s #> 0 0 5989.26277 0 1054 - 5989.26277 - - 1s #> 0 0 5989.26277 0 1055 - 5989.26277 - - 1s #> 0 0 5989.85690 0 1080 - 5989.85690 - - 1s #> 0 0 5990.72442 0 1067 - 5990.72442 - - 1s #> 0 0 5990.89259 0 1113 - 5990.89259 - - 2s #> 0 0 5990.90595 0 1137 - 5990.90595 - - 2s #> 0 0 5991.12807 0 1053 - 5991.12807 - - 2s #> 0 0 5991.18138 0 1088 - 5991.18138 - - 2s #> 0 0 5991.18284 0 1112 - 5991.18284 - - 2s #> 0 0 5991.61520 0 1190 - 5991.61520 - - 2s #> 0 0 5991.96959 0 1121 - 5991.96959 - - 2s #> 0 0 5992.11345 0 1075 - 5992.11345 - - 2s #> 0 0 5993.37984 0 1050 - 5993.37984 - - 2s #> 0 0 5993.42949 0 1101 - 5993.42949 - - 2s #> 0 0 5993.42949 0 1101 - 5993.42949 - - 2s #> 0 0 5993.53010 0 1098 - 5993.53010 - - 2s #> 0 0 5993.53010 0 1099 - 5993.53010 - - 2s #> 0 0 5993.53010 0 1095 - 5993.53010 - - 2s #> 0 0 5993.53010 0 1094 - 5993.53010 - - 2s #> 0 2 5993.59840 0 1094 - 5993.59840 - - 3s #> 37 39 6296.70562 25 544 - 5993.68294 - 330 5s #> H 147 118 9095.6069285 5993.68294 34.1% 156 5s #> H 176 139 8874.0196096 5995.87353 32.4% 178 7s #> H 203 157 8629.5143835 5995.87353 30.5% 185 7s #> H 230 99 6774.1900965 5995.87353 11.5% 178 7s #> H 257 120 6769.2701548 5996.71360 11.4% 189 8s #> H 285 138 6765.4352048 5996.71360 11.4% 185 9s #> 307 162 6131.15519 15 948 6765.43520 5996.96548 11.4% 191 10s #> H 312 165 6754.1573992 5996.96548 11.2% 190 10s #> H 394 220 6750.1005609 5997.40740 11.2% 203 12s #> 476 296 6149.28256 17 1137 6750.10056 5997.58112 11.1% 224 15s #> 522 329 6411.22879 29 1209 6750.10056 5997.58112 11.1% 218 20s #> 532 336 6009.76943 8 1217 6750.10056 5997.58112 11.1% 214 25s #> H 536 321 6575.4232077 5998.76106 8.77% 213 28s #> #> Cutting planes: #> Gomory: 5 #> Cover: 1 #> MIR: 15 #> Flow cover: 1 #> Zero half: 25 #> Mod-K: 1 #> #> Explored 536 nodes (152608 simplex iterations) in 28.01 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 9: 6575.42 6750.1 6754.16 ... 9095.61 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.575423207660e+03, best bound 5.998761061507e+03, gap 8.7700% #> Optimize a model with 4495 rows, 3795 columns and 14780 nonzeros #> Variable types: 0 continuous, 3795 integer (3795 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 8e+00] #> Presolve removed 652 rows and 656 columns #> Presolve time: 0.41s #> Presolved: 3843 rows, 3139 columns, 12612 nonzeros #> Variable types: 0 continuous, 3139 integer (3139 binary) #> Presolve removed 749 rows and 81 columns #> Presolved: 3094 rows, 3058 columns, 10608 nonzeros #> #> #> Root relaxation: objective 5.956540e+03, 3679 iterations, 0.18 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5956.54040 0 505 - 5956.54040 - - 0s #> 0 0 5965.39042 0 652 - 5965.39042 - - 0s #> 0 0 5969.77495 0 643 - 5969.77495 - - 1s #> 0 0 5969.97402 0 691 - 5969.97402 - - 1s #> 0 0 5970.20934 0 704 - 5970.20934 - - 1s #> 0 0 5970.24138 0 705 - 5970.24138 - - 1s #> 0 0 5975.77093 0 828 - 5975.77093 - - 1s #> 0 0 5976.29135 0 856 - 5976.29135 - - 1s #> 0 0 5976.94935 0 857 - 5976.94935 - - 1s #> 0 0 5976.98442 0 827 - 5976.98442 - - 1s #> 0 0 5977.02879 0 827 - 5977.02879 - - 1s #> 0 0 5981.60828 0 767 - 5981.60828 - - 1s #> 0 0 5982.78317 0 829 - 5982.78317 - - 1s #> 0 0 5982.83353 0 825 - 5982.83353 - - 1s #> 0 0 5982.84389 0 826 - 5982.84389 - - 1s #> 0 0 5984.85168 0 883 - 5984.85168 - - 1s #> 0 0 5985.04510 0 868 - 5985.04510 - - 1s #> 0 0 5985.05956 0 887 - 5985.05956 - - 1s #> 0 0 5985.06333 0 888 - 5985.06333 - - 1s #> 0 0 5986.28241 0 910 - 5986.28241 - - 1s #> 0 0 5986.29123 0 920 - 5986.29123 - - 1s #> 0 0 5986.29381 0 921 - 5986.29381 - - 1s #> 0 0 5986.85448 0 890 - 5986.85448 - - 1s #> 0 0 5986.92443 0 873 - 5986.92443 - - 1s #> 0 0 5986.95660 0 938 - 5986.95660 - - 1s #> 0 0 5986.96350 0 942 - 5986.96350 - - 1s #> 0 0 5987.03203 0 912 - 5987.03203 - - 1s #> 0 0 5987.11519 0 931 - 5987.11519 - - 1s #> 0 0 5987.11554 0 932 - 5987.11554 - - 1s #> 0 0 5987.14844 0 904 - 5987.14844 - - 2s #> 0 0 5987.18015 0 903 - 5987.18015 - - 2s #> 0 0 5987.18534 0 878 - 5987.18534 - - 2s #> 0 0 5987.25907 0 899 - 5987.25907 - - 2s #> 0 0 5987.26054 0 900 - 5987.26054 - - 2s #> 0 0 5987.31702 0 881 - 5987.31702 - - 2s #> 0 0 5987.32632 0 888 - 5987.32632 - - 2s #> 0 0 5987.50129 0 902 - 5987.50129 - - 2s #> 0 0 5987.54099 0 908 - 5987.54099 - - 2s #> 0 0 5987.56418 0 916 - 5987.56418 - - 2s #> 0 0 5987.57285 0 913 - 5987.57285 - - 2s #> 0 0 5987.69546 0 940 - 5987.69546 - - 2s #> 0 0 5987.75945 0 936 - 5987.75945 - - 2s #> 0 0 5987.82662 0 904 - 5987.82662 - - 2s #> 0 0 5987.85507 0 941 - 5987.85507 - - 2s #> 0 0 5987.88073 0 925 - 5987.88073 - - 2s #> 0 0 5987.89359 0 946 - 5987.89359 - - 2s #> 0 0 5987.90436 0 949 - 5987.90436 - - 2s #> 0 0 5987.90565 0 952 - 5987.90565 - - 2s #> 0 0 5988.48112 0 845 - 5988.48112 - - 2s #> 0 0 5988.48760 0 924 - 5988.48760 - - 2s #> 0 0 5988.51496 0 935 - 5988.51496 - - 2s #> 0 0 5988.52976 0 915 - 5988.52976 - - 2s #> 0 0 5988.53378 0 949 - 5988.53378 - - 2s #> 0 0 5988.53549 0 945 - 5988.53549 - - 2s #> 0 0 5988.53549 0 942 - 5988.53549 - - 2s #> 0 2 5988.54919 0 941 - 5988.54919 - - 3s #> 157 156 infeasible 123 - 5988.57597 - 81.9 5s #> H 233 149 6305.5335829 5989.25928 5.02% 108 6s #> #> Cutting planes: #> MIR: 31 #> StrongCG: 1 #> Zero half: 9 #> #> Explored 233 nodes (37624 simplex iterations) in 6.18 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 1: 6305.53 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.305533582904e+03, best bound 5.989259279935e+03, gap 5.0158% #> Optimize a model with 2584 rows, 2490 columns and 8328 nonzeros #> Variable types: 0 continuous, 2490 integer (2490 binary) #> Coefficient statistics: #> Matrix range [2e-01, 1e+00] #> Objective range [2e+02, 2e+02] #> Bounds range [1e+00, 1e+00] #> RHS range [1e+00, 8e+00] #> Presolve removed 1080 rows and 1080 columns #> Presolve time: 0.05s #> Presolved: 1504 rows, 1410 columns, 6879 nonzeros #> Variable types: 0 continuous, 1410 integer (1410 binary) #> Presolve removed 290 rows and 0 columns #> Presolved: 1214 rows, 1410 columns, 6009 nonzeros #> #> #> Root relaxation: objective 5.956523e+03, 1702 iterations, 0.04 seconds #> #> Nodes | Current Node | Objective Bounds | Work #> Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time #> #> 0 0 5956.52331 0 176 - 5956.52331 - - 0s #> 0 0 5965.87304 0 244 - 5965.87304 - - 0s #> 0 0 5972.15690 0 298 - 5972.15690 - - 0s #> 0 0 5972.16676 0 309 - 5972.16676 - - 0s #> 0 0 5973.04652 0 324 - 5973.04652 - - 0s #> 0 0 5977.80373 0 244 - 5977.80373 - - 0s #> 0 0 5981.53750 0 318 - 5981.53750 - - 0s #> 0 0 5981.73164 0 318 - 5981.73164 - - 0s #> 0 0 5982.15775 0 318 - 5982.15775 - - 0s #> 0 0 5982.22132 0 315 - 5982.22132 - - 0s #> 0 0 5982.22895 0 316 - 5982.22895 - - 0s #> 0 0 5986.25642 0 325 - 5986.25642 - - 0s #> 0 0 5986.60848 0 345 - 5986.60848 - - 0s #> 0 0 5987.53320 0 333 - 5987.53320 - - 0s #> 0 0 5987.67979 0 335 - 5987.67979 - - 0s #> 0 0 5987.68774 0 335 - 5987.68774 - - 0s #> 0 0 5989.10670 0 348 - 5989.10670 - - 0s #> 0 0 5989.48539 0 329 - 5989.48539 - - 0s #> 0 0 5989.48768 0 331 - 5989.48768 - - 0s #> 0 0 5990.18557 0 347 - 5990.18557 - - 0s #> 0 0 5990.19842 0 337 - 5990.19842 - - 0s #> 0 0 5990.47544 0 354 - 5990.47544 - - 0s #> 0 0 5990.62134 0 357 - 5990.62134 - - 0s #> 0 0 5990.65379 0 354 - 5990.65379 - - 0s #> 0 0 5990.66234 0 355 - 5990.66234 - - 0s #> 0 0 5991.16770 0 360 - 5991.16770 - - 0s #> 0 0 5991.26469 0 363 - 5991.26469 - - 0s #> 0 0 5991.26898 0 361 - 5991.26898 - - 0s #> 0 0 5991.74123 0 379 - 5991.74123 - - 0s #> 0 0 5991.82562 0 365 - 5991.82562 - - 0s #> 0 0 5991.90103 0 373 - 5991.90103 - - 0s #> 0 0 5991.90546 0 381 - 5991.90546 - - 0s #> 0 0 5991.90907 0 380 - 5991.90907 - - 0s #> 0 0 5991.90907 0 374 - 5991.90907 - - 0s #> 0 2 5991.94307 0 374 - 5991.94307 - - 1s #> H 144 103 6578.0672409 5992.33563 8.90% 78.0 2s #> #> Cutting planes: #> Gomory: 1 #> Cover: 1 #> MIR: 19 #> StrongCG: 3 #> Zero half: 19 #> #> Explored 144 nodes (15818 simplex iterations) in 2.10 seconds #> Thread count was 1 (of 4 available processors) #> #> Solution count 1: 6578.07 #> #> Optimal solution found (tolerance 1.00e-01) #> Best objective 6.578067240863e+03, best bound 5.992335625826e+03, gap 8.9043%
s2 <- stack(lapply(s2, category_layer)) # plot solutions plot(s2, main = c("p5", "p6", "p7", "p8"), axes = FALSE, box = FALSE)