Optimizing Warehouse Locations
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Massachusetts Institute of Technology *
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CTL.SC1X
Subject
Industrial Engineering
Date
Feb 20, 2024
Type
docx
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4
Uploaded by CaptainPowerCrane14
Optimizing Warehouse Locations for Efficient Nationwide
Distribution
If we plan to establish a company and need to determine the optimal location for a
warehouse between two cities based on demand, how do we precisely decide where
to
place
the
warehouse?
As commonly known, cities are pinpointed on the globe using latitude and longitude
coordinates, and individuals worldwide typically employ the basic Pythagorean
theorem to calculate distances between two locations. However, this method has
limitations, particularly in larger areas where distances are extensive. To overcome
this challenge and accurately determine distances between cities, Solver can be
employed to identify the optimal warehouse location. This process aims to minimize
shipping distances for all shipments, offering a more precise solution in scenarios
where traditional methods fall short.
The data employed in both the scenarios is generic.
The objective is to identify the most efficient location
for a warehouse, aiming to
minimize
shipping distances for all shipments nationwide.
The goal here is to minimize the Total Distance by finding the best location for
warehouse 1. The "X" in the table represents the distance from the warehouse to each respective
city.
So, to get the result for this type of case we are going to run Solver in Excel. Keeping the goal in solver by minimizing the total distance. And what exactly we are
going to change is B2:C2.
The solution we get is 36.81N, 92.48 W. Utilizing maps on any website provides location information pinpointed to an area
within
Missouri
.
In a similar manner, we can now simultaneously identify the locations of two
warehouses. However, the data we will use will differ as we need additional
information to retrieve the specific locations.
The sole additions comprise two distance calculations (city to warehouse 1 and city
to warehouse 2). In this instance, the assumption is that each city will be supplied
from the warehouse that is in close proximity. The primary goal remains to
minimize the overall distance.
The "X" in the table represents the distance from the warehouse to each respective
city.
So, to get the result for this type of case we are going to run Solver in Excel again. Keeping the goal in solver by minimizing the total distance. And what exactly we are
going to change is B2:C3.
In a unique approach, we'll deviate from using GRG Nonlinear, as it may not yield
the optimal outcome and might result in identical latitude and longitude for both
warehouses. Instead, we'll employ GRG Nonlinear with multiple optima, specifically
with a default population size of 100 (indicating 100 runs). The constraints set for
this optimization include ensuring that the latitudes of both warehouses are greater
than or equal to zero, less than 90 (avoiding the North Pole), and the longitudes are
greater than or equal to zero but less than or equal to 150 (excluding areas beyond
Hawaii).
The solution we get is:
Latitude
Longitude
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Warehouse 1
34.93187
117.7915884
Warehouse 2
38.16407
84.02897632
Utilizing maps on any website offers location details precisely highlighting an area in
California
and another one in Kentucky
.