Switch to dijkstra_shortest_paths

Dijkstra is a well-known algorithm with good performance records, unlike Pape.

Project.toml

@@ -2,7 +2,7 @@ name = "Seleroute"
 uuid = "d2f42c57-c897-4e08-8218-a119b5759c84"
 authors = ["Thibaut Cuvelier <cuvelier.thibaut@gmail.com>"]
 repo = "https://github.com/dourouc05/Seleroute.jl.git"
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
 Cairo = "159f3aea-2a34-519c-b102-8c37f9878175"

src/compute/certain/certain_cg.jl

@@ -97,7 +97,7 @@ function _mintotload_solve_pricing_problem_master(rd::RoutingData,
         end
 
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)
@@ -218,7 +218,7 @@ function _minmaxload_solve_pricing_problem_master(rd::RoutingData,
         end
 
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)

src/compute/certain/mmf.jl

@@ -211,7 +211,7 @@ function _mmf_solve_pricing_problem(rd::RoutingData, dual_values_capacity, dual_
         rd.logmessage("Pricing for $demand")
 
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)

src/compute/oblivious/iter_pathcg.jl

@@ -56,7 +56,7 @@ function _oblivious_iterative_solve_pricing_problem_master(rd::RoutingData,
         end
 
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)
@@ -102,7 +102,7 @@ function _oblivious_iterative_solve_pricing_problem_subproblem(rd::RoutingData,
     # Perform the actual pricing.
     for demand in demands(rd)
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)

src/compute/oblivious/rr_pathcg.jl

@@ -123,7 +123,7 @@ function _oblivious_reformulation_solve_pricing_problem(rd::RoutingData, dual_va
         end
 
         # Compute the shortest path for this demand.
-        state = desopo_pape_shortest_paths(rd.g, src(demand), weight_matrix)
+        state = dijkstra_shortest_paths(rd.g, src(demand), weight_matrix)
 
         # Is there a solution? Has this path a negative reduced cost?
         if all(state.parents .== 0)

src/data/solution.jl

@@ -224,7 +224,7 @@ function flow_routing_to_path(data::RoutingData, routing::AbstractMatrix{Float64
         weight_e(e) = get_prop(ug, e, :weight)
         while ne(ug) > 0
             # Get a path arbitrarily (easiest to compute: shortest one)
-            sp = desopo_pape_shortest_paths(ug, src(d))
+            sp = dijkstra_shortest_paths(ug, src(d))
             path_vertices = enumerate_paths(sp, dst(d))
             path = map(Edge, zip(path_vertices[1:end-1], path_vertices[2:end]))
             if length(path) == 0