CODEWITHSUNDEEP
pythonlinear-programminghaversinecentroid
Darshan
Darshan

Reputation: 1

How to find weighted average distance based additional centroids for demand centres considering few existing centroids

I am new to stackoverflow and I have specific problem regarding supply chain. I have many demand locations (latitude and longitude) along with its demand. I also have 2 supply locations (latitude and longitude) along with its capacity. I want to find additional 2 supply locations (latitude and longitude) with least weighted average haversine distance considering existing supply locations.

Please help me with which algorithm to use (probably some combination of clustering and nearest neighbour). I am using Python for this programming.

consider following example for better understanding. Demand location details

Demand location Lat Long Demand (V)
D1 13.2 78.5 100
D2 14.6 75.2 200
D3 12.4 77.0 400
D4 15.6 74.5 150
D5 13.3 76.1 200
D6 15.6 77.9 250
D7 12.8 73.2 300
D8 15.3 76.7 50
D9 14.0 73.4 500
D10 17.2 78.6 550
D11 11.9 72.0 100

Supply location details

Demand location Lat Long Capacity(C)
S1 14.4 73.7 1000
S2 16.7 76.8 1500

haversine distance between demand and supply location is D so that Min -> D*V considering

  1. All demand is fulfilled
  2. Volume served from S1 & S2 does not exceed its capacity (C).

Expected output is

  1. Lat/long of S3 & S4
  2. Volume served from S1, S2, S3 & S4 to each demand locations

I tried creating through non-linear programming in excel solver but I am not getting desire output.

Thanks in advance

Regards Darshan Gosalia

Upvotes: 0

Views: 60

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