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Assignment Problems with Weighted andNonweighted Neighborhood Constraints in 36, 44
and 63 Tilings
A.A.D. Bosaing J.F. Rabajante M.L.D. De Lara
Institute of Mathematical Sciences and PhysicsUniversity of the Philippines Los Baños
International Conference in Mathematics and Applications, 2011
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 1 / 42
Introduction The Assignment Problem
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 2 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
ASSIGNMENT: elements of given finite sets should be assignedto the compartments of a finite tiling
regular tilings of regular polygons in Euclidean plane (36, 44 and 63)
CONSTRAINT: costs of having adjacent elements from differentsets are minimized
two compartments are adjacent if they share a common edgewe assign weights ωg and ωg to sets g and g, respectivelycost of adjacency=
∣∣ωg − ωg∣∣
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 3 / 42
Introduction The Assignment Problem
Assignment Problem
Figure: Assignment Problem as Weighted Bipartite Graph
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 4 / 42
Introduction Weighted Neighborhood Constraint
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 5 / 42
Introduction Weighted Neighborhood Constraint
Weighted Neighborhood Constraint
Figure: Weighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 6 / 42
Introduction Nonweighted Neighborhood Constraint
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 7 / 42
Introduction Nonweighted Neighborhood Constraint
Nonweighted Neighborhood Constraint
Figure: Weighted VS Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 8 / 42
Introduction Nonweighted Neighborhood Constraint
Nonweighted Neighborhood Constraint
Figure: Weighted VS Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 9 / 42
Introduction Parameters and Decision Variables
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 10 / 42
Introduction Parameters and Decision Variables
Weighted Neighborhood Constraint36 Tiling
Let the binary-valued decision variables be
xgij =
0, if an element from set g is not assigned to the
compartment at the i−th row and j−th column1, otherwise
for i = 1,2, . . . , r and j = 1,2, . . . , cr is the number of rowsc is the number of columns
Let Ng be the number of elements in set g for g = 1,2, . . . , k where kis the number of sets.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 11 / 42
Weighted Neighborhood Constraint 36 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 12 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling
Figure: Starting with adjacent (column) compartment and starting withnon-adjacent (column) compartment
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 13 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling
Figure: Graph representation
Figure: Adjacencies
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 14 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
The integer program is
Minimize
r∑i=1
c−1∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+
dr/2e−1∑i=1
dc/2e∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxg(2i)(2j) −k∑
g=1
ωgxg(2i+1)(2j)
∣∣∣∣∣∣ (O2)
+
br/2c∑i=1
bc/2c∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxg(2i−1)(2j−1) −k∑
g=1
ωgxg(2i)(2j−1)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 15 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
subject to
Constraint 1: For i = 1,2, . . . , r and j = 1,2, . . . , c,
k∑g=1
xgij
{= 0, if ij−th compartment is a dummy compartment≤ 1, otherwise
Constraint 2: For g = 1,2, . . . , k ,
r∑i=1
c∑j=1
xgij = Ng
Constraint 3: For i = 1,2, . . . , r , j = 1,2, . . . , c and g = 1,2, . . . , k ,
xgij ∈ {0,1}
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 16 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
The linearized objective function is
Minimize
r∑i=1
c−1∑j=1
αij +
dr/2e−1∑i=1
dc/2e∑j=1
β(2i)(2j) +
br/2c∑i=1
bc/2c∑j=1
γ(2i−1)(2j−1)
subject to
Constraint 1: For i = 1,2, . . . , r and j = 1,2, . . . , c − 1,
k∑g=1
ωgxgij −k∑
g=1
ωgxgi(j+1) − αij ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 17 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
continuation...
Constraint 2: For i = 1,2, . . . , r and j = 1,2, . . . , c − 1,
−k∑
g=1
ωgxgij +k∑
g=1
ωgxgi(j+1) − αij ≤ 0
Constraint 3: For i = 1,2, . . . , dr/2e − 1 and j = 1,2, . . . , dc/2e,k∑
g=1
ωgxg(2i)(2j) −k∑
g=1
ωgxg(2i+1)(2j) − β(2i)(2j) ≤ 0
Constraint 4: For i = 1,2, . . . , dr/2e − 1 and j = 1,2, . . . , dc/2e,
−k∑
g=1
ωgxg(2i)(2j) +k∑
g=1
ωgxg(2i+1)(2j) − β(2i)(2j) ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 18 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
continuation...
Constraint 5: For i = 1,2, . . . , br/2c and j = 1,2, . . . , bc/2c,
k∑g=1
ωgxg(2i−1)(2j−1) −k∑
g=1
ωgxg(2i)(2j−1) − γ(2i−1)(2j−1) ≤ 0
Constraint 6: For i = 1,2, . . . , br/2c and j = 1,2, . . . , bc/2c,
−k∑
g=1
ωgxg(2i−1)(2j−1) +k∑
g=1
ωgxg(2i)(2j−1) − γ(2i−1)(2j−1) ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 19 / 42
Weighted Neighborhood Constraint 36 Tiling
Weighted Neighborhood Constraint36 Tiling: Starting with non-adjacent (column) compartment
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+
dr/2e−1∑i=1
dc/2e∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxg(2i)(2j−1) −k∑
g=1
ωgxg(2i+1)(2j−1)
∣∣∣∣∣∣ (O2)
+
br/2c∑i=1
bc/2c∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxg(2i−1)(2j) −k∑
g=1
ωgxg(2i)(2j)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 20 / 42
Weighted Neighborhood Constraint 44 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 21 / 42
Weighted Neighborhood Constraint 44 Tiling
Weighted Neighborhood Constraint44 Tiling
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣+r−1∑i=1
c∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)j
∣∣∣∣∣∣
Figure: Adjacencies in square tiling
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 22 / 42
Weighted Neighborhood Constraint 63 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 23 / 42
Weighted Neighborhood Constraint 63 Tiling
Weighted Neighborhood Constraint63 Tiling
Figure: Adjacencies in hexagonal tiling
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 24 / 42
Weighted Neighborhood Constraint 63 Tiling
Weighted Neighborhood Constraint63 Tiling
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+r−1∑i=1
c∑j=1
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)j
∣∣∣∣∣∣ (O2)
+r−1∑i=1
c∑j=2
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)(j−1)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 25 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 26 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood ConstraintSuppose a1, a2, . . . , ak , b1, b2, . . . , bk be the dummy weights associated to thedecision variables y1, y2, . . . , yk , z1, z2, . . . , zk , respectively, then define the relationρ(O?) as
ρ(O?)(|(a1y1 + a2y2 + · · ·+ ak yk )− (b1z1 + b2z2 + · · ·+ bk zk )|)=
∣∣κ1(O?)
∣∣+ ∣∣κ2(O?)
∣∣+ · · ·+ ∣∣κk(O?)
∣∣where
κ1(O?) = (a1y1 + a2y2 + · · ·+ ak−1yk−1 + ak yk )−(b1z1 + b2z2 + · · ·+ bk−1zk−1 + bk zk )
κ2(O?) = (a2y1 + a3y2 + · · ·+ ak yk−1 + a1yk )−(b2z1 + b3z2 + · · ·+ bk zk−1 + b1zk )
κ3(O?) = (a3y1 + a4y2 + · · ·+ a1yk−1 + a2yk )−(b3z1 + b4z2 + · · ·+ b1zk−1 + b2zk )
...κ(k−1)(O?) = (ak−1y1 + ak y2 + · · ·+ ak−3yk−1 + ak−2yk )
−(bk−1z1 + bk z2 + · · ·+ bk−3zk−1 + bk−2zk )κk(O?) = (ak y1 + a1y2 + · · ·+ ak−2yk−1 + ak−1yk )
−(bk z1 + b1z2 + · · ·+ bk−2zk−1 + bk−1zk ).
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 27 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint
For simplicity, we let ωg = g.
Figure: Circular Shift Permutation of the dummy weights
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 28 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
ρ(O1)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+
dr/2e−1∑i=1
dc/2e∑j=1
ρ(O2)
∣∣∣∣∣∣k∑
g=1
ωgxg(2i)(2j) −k∑
g=1
ωgxg(2i+1)(2j)
∣∣∣∣∣∣ (O2)
+
br/2c∑i=1
bc/2c∑j=1
ρ(O3)
∣∣∣∣∣∣k∑
g=1
ωgxg(2i−1)(2j−1) −k∑
g=1
ωgxg(2i)(2j−1)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 29 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
The linearized objective function is
Minimize
r∑i=1
c−1∑j=1
k∑h=1
αhij +
dr/2e−1∑i=1
dc/2e∑j=1
k∑h=1
βh(2i)(2j) +
br/2c∑i=1
bc/2c∑j=1
k∑h=1
γh(2i−1)(2j−1)
subject to
Constraint 1: For h = 1,2, . . . , k , i = 1,2, . . . , r and j = 1,2, . . . , c − 1,
κh(O1) − αhij ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 30 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
continuation...
Constraint 2: For h = 1,2, . . . , k , i = 1,2, . . . , r and j = 1,2, . . . , c − 1,
−κh(O1) − αhij ≤ 0
Constraint 3: For h = 1,2, . . . , k , i = 1,2, . . . , dr/2e − 1 andj = 1,2, . . . , dc/2e,
κh(O2) − βh(2i)(2j) ≤ 0
Constraint 4: For h = 1,2, . . . , k , i = 1,2, . . . , dr/2e − 1 andj = 1,2, . . . , dc/2e,
−κh(O2) − βh(2i)(2j) ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 31 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint36 Tiling: Starting with adjacent (column) compartment
continuation...
Constraint 5: For h = 1,2, . . . , k , i = 1,2, . . . , br/2c andj = 1,2, . . . , bc/2c,
κh(O3) − γh(2i−1)(2j−1) ≤ 0
Constraint 6: For h = 1,2, . . . , k , i = 1,2, . . . , br/2c andj = 1,2, . . . , bc/2c,
−κh(O3) − γh(2i−1)(2j−1) ≤ 0
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 32 / 42
Nonweighted Neighborhood Constraint 36 Tiling
Nonweighted Neighborhood Constraint36 Tiling: Starting with non-adjacent (column) compartment
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
ρ(O1)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+
dr/2e−1∑i=1
dc/2e∑j=1
ρ(O2)
∣∣∣∣∣∣k∑
g=1
ωgxg(2i)(2j−1) −k∑
g=1
ωgxg(2i+1)(2j−1)
∣∣∣∣∣∣ (O2)
+
br/2c∑i=1
bc/2c∑j=1
ρ(O3)
∣∣∣∣∣∣k∑
g=1
ωgxg(2i−1)(2j) −k∑
g=1
ωgxg(2i)(2j)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 33 / 42
Nonweighted Neighborhood Constraint 44 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 34 / 42
Nonweighted Neighborhood Constraint 44 Tiling
Nonweighted Neighborhood Constraint44 Tiling
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
ρ(O1)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+r−1∑i=1
c∑j=1
ρ(O2)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)j
∣∣∣∣∣∣ (O2)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 35 / 42
Nonweighted Neighborhood Constraint 63 Tiling
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 36 / 42
Nonweighted Neighborhood Constraint 63 Tiling
Nonweighted Neighborhood Constraint63 Tiling
The objective function of the integer program is
Minimize
r∑i=1
c−1∑j=1
ρ(O1)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxgi(j+1)
∣∣∣∣∣∣ (O1)
+r−1∑i=1
c∑j=1
ρ(O2)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)j
∣∣∣∣∣∣ (O2)
+r−1∑i=1
c∑j=2
ρ(O3)
∣∣∣∣∣∣k∑
g=1
ωgxgij −k∑
g=1
ωgxg(i+1)(j−1)
∣∣∣∣∣∣ (O3)
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 37 / 42
Illustrative Example Weighted and Nonweighted Neighborhood Constraint
Outline1 Introduction
The Assignment ProblemWeighted Neighborhood ConstraintNonweighted Neighborhood ConstraintParameters and Decision Variables
2 Weighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
3 Nonweighted Neighborhood Constraint36 Tiling44 Tiling63 Tiling
4 Illustrative ExampleWeighted and Nonweighted Neighborhood Constraint
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 38 / 42
Illustrative Example Weighted and Nonweighted Neighborhood Constraint
Illustrative Example
Table: Distribution of elements per group.Group Number of elements
Group 1 3Group 2 4Group 3 5
Assume ωg = g
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 39 / 42
Illustrative Example Weighted and Nonweighted Neighborhood Constraint
Weighted Neighborhood Constraint
Figure: Optimal Solutions
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 40 / 42
Illustrative Example Weighted and Nonweighted Neighborhood Constraint
Nonweighted Neighborhood Constraint
Figure: Optimal Solutions
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 41 / 42
Appendix References
References
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Diaby, M.Linear programming formulation of the vertex colouring problem. Int. J. Mathematics inOperational Research, 2(3):259–289, 2010.
Esteves, R.J.P., Villadelrey, M.C. and Rabajante, J.F.Determining the Optimal Distribution of Bee Colony Locations To Avoid OverpopulationUsing Mixed Integer Programming. Journal of Nature Studies, 9(1):79–82, 2010.
Kaatz, F.H., Bultheel, A. and Egami, T.Order in mathematically ideal porous arrays: the regular tilings.http://nalag.cs.kuleuven.be/papers/ade/regulartiles/index.html
De Lara, M.L.D. and Rabajante, J.F.Population assignments in grids with neighborhood constraint. International IndustrialEngineering Conference: Research, Applications and Best Practices August 2010, Cebu,Philippines.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 42 / 42
Appendix References
References
Taha, H.A.Operations Research: An Introduction. Prentice Hall, 2006.
Diaby, M.Linear programming formulation of the vertex colouring problem. Int. J. Mathematics inOperational Research, 2(3):259–289, 2010.
Esteves, R.J.P., Villadelrey, M.C. and Rabajante, J.F.Determining the Optimal Distribution of Bee Colony Locations To Avoid OverpopulationUsing Mixed Integer Programming. Journal of Nature Studies, 9(1):79–82, 2010.
Kaatz, F.H., Bultheel, A. and Egami, T.Order in mathematically ideal porous arrays: the regular tilings.http://nalag.cs.kuleuven.be/papers/ade/regulartiles/index.html
De Lara, M.L.D. and Rabajante, J.F.Population assignments in grids with neighborhood constraint. International IndustrialEngineering Conference: Research, Applications and Best Practices August 2010, Cebu,Philippines.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 42 / 42
Appendix References
References
Taha, H.A.Operations Research: An Introduction. Prentice Hall, 2006.
Diaby, M.Linear programming formulation of the vertex colouring problem. Int. J. Mathematics inOperational Research, 2(3):259–289, 2010.
Esteves, R.J.P., Villadelrey, M.C. and Rabajante, J.F.Determining the Optimal Distribution of Bee Colony Locations To Avoid OverpopulationUsing Mixed Integer Programming. Journal of Nature Studies, 9(1):79–82, 2010.
Kaatz, F.H., Bultheel, A. and Egami, T.Order in mathematically ideal porous arrays: the regular tilings.http://nalag.cs.kuleuven.be/papers/ade/regulartiles/index.html
De Lara, M.L.D. and Rabajante, J.F.Population assignments in grids with neighborhood constraint. International IndustrialEngineering Conference: Research, Applications and Best Practices August 2010, Cebu,Philippines.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 42 / 42
Appendix References
References
Taha, H.A.Operations Research: An Introduction. Prentice Hall, 2006.
Diaby, M.Linear programming formulation of the vertex colouring problem. Int. J. Mathematics inOperational Research, 2(3):259–289, 2010.
Esteves, R.J.P., Villadelrey, M.C. and Rabajante, J.F.Determining the Optimal Distribution of Bee Colony Locations To Avoid OverpopulationUsing Mixed Integer Programming. Journal of Nature Studies, 9(1):79–82, 2010.
Kaatz, F.H., Bultheel, A. and Egami, T.Order in mathematically ideal porous arrays: the regular tilings.http://nalag.cs.kuleuven.be/papers/ade/regulartiles/index.html
De Lara, M.L.D. and Rabajante, J.F.Population assignments in grids with neighborhood constraint. International IndustrialEngineering Conference: Research, Applications and Best Practices August 2010, Cebu,Philippines.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 42 / 42
Appendix References
References
Taha, H.A.Operations Research: An Introduction. Prentice Hall, 2006.
Diaby, M.Linear programming formulation of the vertex colouring problem. Int. J. Mathematics inOperational Research, 2(3):259–289, 2010.
Esteves, R.J.P., Villadelrey, M.C. and Rabajante, J.F.Determining the Optimal Distribution of Bee Colony Locations To Avoid OverpopulationUsing Mixed Integer Programming. Journal of Nature Studies, 9(1):79–82, 2010.
Kaatz, F.H., Bultheel, A. and Egami, T.Order in mathematically ideal porous arrays: the regular tilings.http://nalag.cs.kuleuven.be/papers/ade/regulartiles/index.html
De Lara, M.L.D. and Rabajante, J.F.Population assignments in grids with neighborhood constraint. International IndustrialEngineering Conference: Research, Applications and Best Practices August 2010, Cebu,Philippines.
Bosaing et al. (IMSP, UPLB) Assignment Problems... ICMA 2011 42 / 42
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