A classification method, solution for warehouse locating

Warehouse locating is the key factor of supply chain management in today’s competitive market. The final goal is to minimize the overall expected cost of warehousing and distribution. There are many factors that impact the best location for minimizing the cost of warehouse.  Explained by Patrick O’Healy, there are many elements play a role in finding a warehouse location. The most critical elements for finding the warehouse location are price of physical location, labor cost, and accessibility to the market. Among these elements, finding the price of physical location and labor cost is straightforward, but business have hard time finding the best location to cover all market areas with the minimum cost of transportation.

In this post, I am introducing a classification method to find the best location of your warehouse(s) in order to minimize the needed transportation of goods from the warehouse to multiple market destinations.  Each market area is shown as a point on the map with the distinct coordination. I am trying to explain how to find the best location (best coordination) to minimize the transportation of goods with a classification method, which is explained in my paper. To simplify the problem, we assume that markets are one dimension variables and they are equally big.


To find the optimized location, the distances between any two markets are calculated (i.e. N (N-1)/2 distances for N markets).  The first two points with the minimum distance compose the first marketing area and are assumed as one point with the value equal to the mean value of two points. This procedure repeats recursively for the N-1 markets and continues till all points compose one marketing area. The result of calculated distances is shown in the figure below for this simplified problem.


By defining the number of the number of warehouses needed in the area, you are able to find the best location. Shown in the second figure, it is possible to open three bigger but further warehouse locations (The left figure) or six closer but bigger warehouse locations (the right figure). As a conclusion, this method of clustering is helping us to find the best location of warehouse. For the real warehouse locating problem, I am able to customize this method of clustering to include the size of market in each region, labor cost and etc. Please comment or message me for any further questions.


Author: Amin Sabzehzar

MBA student Mechanical Engineer University of Nevada, Reno

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