Gigio Gomes

Data Professional

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Certifications

MO-210 - Microsoft Office Specialist: Excel Associate

PL-300 - Microsoft Certified: Power BI Data Analyst Associate

Projects

Market share of Container Terminals in the Port of Santos

Animation that shows the market share of container port terminal in the Port of Santos.

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Transport Cost Minimization

A company owns factories in Palmas-TO and Campo Grande-MS (i = 1, 2), each with a monthly production capacity of 1.5k tons of starch. There are three distribution centers located in São Paulo-SP, Belo Horizonte-MG and Salvador-BA (j = 1, 2, 3), as shown in the map below:

The starch demands for each distribution center are shown in the following table:

The distances between the factories and distribution centers have been calculated and are shown below:

The aim is to optimize the supply of starch to the three distribution centers by reducing transportation costs. The starch production cost is the same at each factory, as well as the specific transportation cost per ton of product, which ct = $ 0.053 / (ton * km). We need to determine the quantity of starch each factory should deliver to each distribution center.

Design Variables
The problem has 6 design variables, which are the quantities of starch each factory i should ship to center j, represented as x_ij ≥ 0 ∈ R¹.

Objective Function
The function we want to minimize is the monthly cost of starch suply, given by:
f = ctΣdistance_ijx_ij

Constraints
Some requirements must be met. The first ist the maximum production capacity of each factory, and the second is the demand of each distribution center. Therefore, there are five constraints in the problem, consisting of two innequalities g_i and three equalities h_j:

Solution
The MATLAB^® optimization toollbox is used to solve the problem, and the implementation is shown below:

And then the solution is found:

Conclusion
The optimized transportation cost is $ 131,500 per month, and the quantity of starch monthly transported from each factory to each distribution center is described below (in tons):

In the map below, the flow of transportation is visualized, with line widths corresponding to the quantities delivered from each factory to each distribution center:

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The port of Santos is the largest in Latin America and handled more than 147 Million tons in 2021, 35% which in containerized cargo.Unlike other ports in Brazil, where only one port operator acts in this market, in Santos there is a great competition among the container terminals.This project offers a dynamic visualization of the market share evolution of container terminals in the port of Santos, Brazil. Using R programming with libraries such as tidyverse, ggplot2, and gganimate, viewers can observe the shifting market positions of terminals over the years through an animated timeline. The data for this visualization is public and available on Santos Port Authority website.For access to the code and files, please visit my GitHub repository.

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A tanker truck has five compartments, (j = 1, 2, ..., 5), with capacities V_j = 2,700, 2,800, 1,100, 1,800 and 3,400 gallons, respectively, totaling 11,800 gallons.

The company needs to deliver three types of fuel (i = 1, 2 and 3), which are premium, super and regular, respectively, with a combined demand of 11,800 gallons. This demand, however, will not be met, since each compartment in the tank can only carry one type of fuel.The objective is to optimize the allocation of fuel in order to minimize the shortage costs while meeting the maximum allowable shortage requirements as follows:

Design Variables
The problem has 18 design variables, 15 of which are binary and 3 have real values:
x_i, fuel shortage;
y_ij = 1, if the fuel i will be stored in the compartment j, and y_ij = 0, otherwise.

Objective Function
The objective is to minimize the fuel shortage x, weighted by its associated cost C. Thus, the objective function is f = Σx_iC_i = 10x₁ + 8x₂ + 6x₃.

Constraints
There are eleven constraints in the problem:
Σy_ij ≤ 1, each compartment can contain at most one type of fuel.;
0 <= x_i ≤ Short_{max i}, the shortage of each fuel type should not exceed the maximum allowable shortage.

Solution
The problem is classified as a Mixed Integer Linear Programming (MILP) problem, once we are dealing with linear objective function and constraints, and the decision variables are a mix of real and binary values.Excel's Solver is used to solve the problem, naming the cells range properly and adopting the LP Simplex algorithm as follows:

Here we see the problem parameters:

And the optmization problem before running the algorithm:

After running the solver, we got the optimized allocation to minimize the objective function (ShortageCost):

Therefore, the company should allocate the fuel as follows:

The minimum shortage cost is $ 2,600, with the fuel shortage as follows:

The fuel quantity allocate in each tank can be calculated using the following expression in the formula bar:

Obtaining the following result:

Note that if overdelivery is not desirable, we can calculate the fuel quantity allocation using the following expression:

So we get:

This problem can be adapted by changing the parameters to minimize shortage cost for any scenario. The .xlsx file is available on my github repository.

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Thank you for reaching out! Your message has been sent and will be responded to shortly. We appreciate your interest and will do our best to get back to you as soon as possible.In the meantime, feel free to explore more about my work here.Best regards,
Geovane Gomes (Gigio)