diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 8cf249d..ce13b3a 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-11-19T17:32:16","documenter_version":"1.1.2"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-11-20T19:58:12","documenter_version":"1.1.2"}} \ No newline at end of file diff --git a/dev/algorithms/index.html b/dev/algorithms/index.html index cee2e4c..3d5abb5 100644 --- a/dev/algorithms/index.html +++ b/dev/algorithms/index.html @@ -18,7 +18,7 @@ julia> result.cost -3.0source

Transportation Problem

OperationsResearchModels.solve — Method
solve(t)

Arguments

a::TransportationProblem: The problem in type of TransportationProblem

Output

TransportationResult: The custom data type that holds problem, solution, and optimum cost.

Description

Solves a transportation problem given by an object of in type TransportationProblem.

Example

julia> t = TransportationProblem(
+3.0
source

Transportation Problem

OperationsResearchModels.solve — Method
solve(t)

Arguments

a::TransportationProblem: The problem in type of TransportationProblem

Output

TransportationResult: The custom data type that holds problem, solution, and optimum cost.

Description

Solves a transportation problem given by an object of in type TransportationProblem.

Example

julia> t = TransportationProblem(
                    [   1 1 1 1; 
                        2 2 2 2; 
                        3 3 3 3], 
@@ -49,7 +49,7 @@
 Cost:
 600.0
 Solution:
-[-0.0 -0.0 -0.0 100.0; 100.0 -0.0 -0.0 -0.0; -0.0 -0.0 100.0 -0.0; -0.0 100.0 -0.0 -0.0]
source

Shortest Path and Maximum Flow

solve(c::Vector{Connection}; problem::Union{::ShortestPathProblem, ::MaximumFlowProblem} = ShortestPathProblem)

Arguments

Example

julia> conns = [
+[-0.0 -0.0 -0.0 100.0; 100.0 -0.0 -0.0 -0.0; -0.0 -0.0 100.0 -0.0; -0.0 100.0 -0.0 -0.0]
source

Shortest Path and Maximum Flow

solve(c::Vector{Connection}; problem::Union{::ShortestPathProblem, ::MaximumFlowProblem} = ShortestPathProblem)

Arguments

Example

julia> conns = [
                    Connection(1, 2, 3),
                    Connection(1, 3, 2),
                    Connection(1, 4, 4),
@@ -101,7 +101,7 @@
   "model"     => A JuMP Model…
   "objective" => 10.5921
   "z"         => [-0.0 -0.0 … 0.0 0.0; -0.0 -0.0 … 0.0 0.0; … ; 0.0 0.0 … -0.0 0.0; 0.0 0.0 … 0.0 -0.0]
-  "y"         => [0.0, -0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, -0.0, 0.0  …  0.0, 0.0, 0.0, -0.0, 1.0, 0.0, -0.0,…
source

Minimum Spanning Tree

OperationsResearchModels.MinimumSpanningTree.mst — Function
mst(connections)

Arguments

  • connections::Vector{Connection}: Vector of Connections

Description

Obtains the minimum spanning tree.

Output

  • ::MstResult: A MstResult object that holds the results.

Examples

julia> conns = Connection[
+  "y"         => [0.0, -0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, -0.0, 0.0  …  0.0, 0.0, 0.0, -0.0, 1.0, 0.0, -0.0,…
source

Minimum Spanning Tree

OperationsResearchModels.MinimumSpanningTree.mst — Function
mst(connections)

Arguments

  • connections::Vector{Connection}: Vector of Connections

Description

Obtains the minimum spanning tree.

Output

  • ::MstResult: A MstResult object that holds the results.

Examples

julia> conns = Connection[
                        Connection(1, 2, 10),
                        Connection(2, 3, 10),
                        Connection(3, 4, 10),
@@ -124,7 +124,7 @@
  3-element Vector{Connection}:
   Connection(3, 4, 10, "x34")
   Connection(1, 4, 10, "x14")
-  Connection(2, 3, 10, "x23")
source

CPM (Critical Path Method)

OperationsResearchModels.CPM.cpm — Function
cpm(activities)

Arguments

  • activities::Vector{CpmActivity}

Output

  • ::CpmResult: The object holds the results

Description

Calculates CPM (Critical Path Method) and reports the critical path for a given set of activities.

Example

julia> A = CpmActivity("A", 2);
+  Connection(2, 3, 10, "x23")
source

CPM (Critical Path Method)

OperationsResearchModels.CPM.cpm — Function
cpm(activities)

Arguments

  • activities::Vector{CpmActivity}

Output

  • ::CpmResult: The object holds the results

Description

Calculates CPM (Critical Path Method) and reports the critical path for a given set of activities.

Example

julia> A = CpmActivity("A", 2);
 julia> B = CpmActivity("B", 3);
 julia> C = CpmActivity("C", 2, [A]);
 julia> D = CpmActivity("D", 3, [B]);
@@ -147,7 +147,7 @@
  "I"
 
  julia> result.path == [B, E, G, I]
-true
source

PERT (Project Evalutation and Review Technique)

OperationsResearchModels.CPM.pert — Function
pert(activities)

Arguments

  • activities::Vector{PertActivity}: Vector of Pert Activities.

Example

julia> A = PertActivity("A", 1, 2, 3)
+true
source

PERT (Project Evalutation and Review Technique)

OperationsResearchModels.CPM.pert — Function
pert(activities)

Arguments

  • activities::Vector{PertActivity}: Vector of Pert Activities.

Example

julia> A = PertActivity("A", 1, 2, 3)
 PertActivity("A", 1.0, 2.0, 3.0, PertActivity[])
 
 julia> B = PertActivity("B", 3, 3, 3)
@@ -169,4 +169,4 @@
 8.0
 
 julia> result.stddev
-0.0
source
+0.0source diff --git a/dev/index.html b/dev/index.html index e0805b5..760e551 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -- · OperationsResearchModels.jl
GitHub

Operations Research Models

The OperationsResearchModels package includes basic Operations Research subjects such as Transportation Problem, Assignment Problem, Minimum Spanning Tree, Shortest Path, Maximum Flow, and p-medians method for selecting location of facilities.

Package content is incrementaly updated.

Please refer the Algorithms section for the detailed documentation.

+- · OperationsResearchModels.jl
GitHub

Operations Research Models

The OperationsResearchModels package includes basic Operations Research subjects such as Transportation Problem, Assignment Problem, Minimum Spanning Tree, Shortest Path, Maximum Flow, and p-medians method for selecting location of facilities.

Package content is incrementaly updated.

Please refer the Algorithms section for the detailed documentation.