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Solving CVRP with ACO
Minimizing Travel Cost for Complex Delivery Problems
This scenario involves the Capacitated Vehicle Routing Problem,
solved using the meta-heuristics algorithm Ant Colony Optimization. Basically, VRP is a network consisting of a number of nodes
(sometimes called cities) and arcs connecting one to all others along with the corresponding costs.
Mostly, the aim is to minimize the cost in visiting each customer once and only once. The term
"capacitated" is added due to some capacity constraints on the vehicles (vcap).
Enter the problem. Some company wants to deliver loads to a number of customers. In this case, we
have 24 nodes based on the location of Germany's train stations (don't ask why). The delivery
always starts from and ends at the depot, visiting a list of customers in other cities. And then
a number of questions arise:
- How do we minimize the travel cost in terms of distance?
- How many trucks are required?
- Which cities are visited by the truck #1, #2. etc.?
- depot: [0..23], def = 0
- vcap: [200..400], def = 400
There is a way to set all the demands, but I don't think you are ready for that. 😉
VCAP: 400 vol.
ACTIVE: 14 customers
- Düsseldorf Hbf (40 vol.)
- Frankfurt Hbf (45 vol.)
- Hannover Hbf (50 vol.)
- Stuttgart Hbf (20 vol.)
- Dresden Hbf (65 vol.)
- Hamburg Hbf (100 vol.)
- Bremen Hbf (70 vol.)
- Leipzig Hbf (90 vol.)
- Karlsruhe Hbf (95 vol.)
- Ulm Hbf (90 vol.)
- Köln Hbf (80 vol.)
- Mainz Hbf (25 vol.)
- Würzburg Hbf (60 vol.)
- Freiburg Hbf (25 vol.)
Tour 1
COST: 1497.441 km
LOAD: 400 vol.
- Würzburg Hbf | 60 vol.
- Stuttgart Hbf | 20 vol.
- Ulm Hbf | 90 vol.
- Karlsruhe Hbf | 95 vol.
- Freiburg Hbf | 25 vol.
- Frankfurt Hbf | 45 vol.
- Mainz Hbf | 25 vol.
- Düsseldorf Hbf | 40 vol.
Tour 2
COST: 1307.311 km
LOAD: 375 vol.
- Hannover Hbf | 50 vol.
- Hamburg Hbf | 100 vol.
- Bremen Hbf | 70 vol.
- Leipzig Hbf | 90 vol.
- Dresden Hbf | 65 vol.
Tour 3
COST: 481.046 km
LOAD: 80 vol.
- Köln Hbf | 80 vol.
![](/static/leaflet/images/icons/marker-icon-blue.png)
LOAD: 400 vol.
- Würzburg Hbf | 60 vol.
- Stuttgart Hbf | 20 vol.
- Ulm Hbf | 90 vol.
- Karlsruhe Hbf | 95 vol.
- Freiburg Hbf | 25 vol.
- Frankfurt Hbf | 45 vol.
- Mainz Hbf | 25 vol.
- Düsseldorf Hbf | 40 vol.
![](/static/leaflet/images/icons/marker-icon-red.png)
LOAD: 375 vol.
- Hannover Hbf | 50 vol.
- Hamburg Hbf | 100 vol.
- Bremen Hbf | 70 vol.
- Leipzig Hbf | 90 vol.
- Dresden Hbf | 65 vol.
![](/static/leaflet/images/icons/marker-icon-green.png)
LOAD: 80 vol.
- Köln Hbf | 80 vol.
#generations: 10 for global, 5 for local
#ants: 5 times #active_customers
ACO
Rel. importance of pheromones α = 1.0
Rel. importance of visibility β = 10.0
Trail persistance ρ = 0.5
Pheromone intensity Q = 10
See this wikipedia page to learn more.
NETWORK Depo: [0] Kassel-Wilhelmshöhe | Number of cities: 24 | Total loads: 855 vol. | Vehicle capacity: 400 vol. Loads: [0, 0, 40, 45, 50, 0, 20, 65, 100, 0, 70, 90, 0, 0, 95, 90, 80, 0, 0, 25, 60, 0, 0, 25] ITERATION Generation: #1 Best cost: 3702.059 | Path: [0, 2, 16, 19, 3, 14, 6, 15, 0, 4, 10, 8, 11, 7, 23, 0, 20, 0] Best cost: 3557.345 | Path: [0, 3, 19, 14, 6, 15, 20, 23, 2, 0, 4, 10, 8, 11, 7, 0, 16, 0] Best cost: 3355.940 | Path: [0, 23, 14, 6, 15, 20, 3, 19, 2, 0, 4, 10, 8, 11, 7, 0, 16, 0] Generation: #3 Best cost: 3296.517 | Path: [0, 20, 6, 15, 14, 23, 19, 3, 2, 0, 4, 10, 8, 11, 7, 0, 16, 0] Generation: #6 Best cost: 3286.735 | Path: [0, 20, 6, 15, 14, 23, 3, 19, 2, 0, 4, 10, 8, 11, 7, 0, 16, 0] OPTIMIZING each tour... Current: [[0, 20, 6, 15, 14, 23, 3, 19, 2, 0], [0, 4, 10, 8, 11, 7, 0], [0, 16, 0]] [2] Cost: 1308.248 to 1307.311 | Optimized: [0, 4, 8, 10, 11, 7, 0] ACO RESULTS [1/400 vol./1497.441 km] Kassel-Wilhelmshöhe -> Würzburg Hbf -> Stuttgart Hbf -> Ulm Hbf -> Karlsruhe Hbf -> Freiburg Hbf -> Frankfurt Hbf -> Mainz Hbf -> Düsseldorf Hbf --> Kassel-Wilhelmshöhe [2/375 vol./1307.311 km] Kassel-Wilhelmshöhe -> Hannover Hbf -> Hamburg Hbf -> Bremen Hbf -> Leipzig Hbf -> Dresden Hbf --> Kassel-Wilhelmshöhe [3/ 80 vol./ 481.046 km] Kassel-Wilhelmshöhe -> Köln Hbf --> Kassel-Wilhelmshöhe OPTIMIZATION RESULT: 3 tours | 3285.798 km.