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Jommo Kenyatta University of Agriculture and Technology Customer at Peak Hours Case Study

Jommo Kenyatta University of Agriculture and Technology Customer at Peak Hours Case Study

ANSWER

Title: Optimizing Car Numbers for Satisfying Customer Demand during Peak Hours

Introduction: In this case study, we aim to analyze and determine the number of cars required to satisfy customer demand during peak hours for the journey from Rajiv Gandhi bus stop (departure station) to Niranjanpur Square (terminal station). We will use historical station data provided in CSV files to simulate and optimize the number of cars needed, with the assistance of the “simulations” idea shared by your partner.

Approach: To achieve this goal, we will follow these steps:

  1. Data Analysis: Begin by analyzing the provided station data in the CSV files. Understand the average number of passengers during peak hours, travel times, and other relevant factors that influence the demand for cars.
  2. Simulation Setup: Utilize the “simulations” idea to create a simulation framework. This framework will replicate the travel process from Rajiv Gandhi to Niranjanpur Square, considering various scenarios of car numbers.
  3. Parameters: Identify key parameters for the simulation, such as car capacity, loading and unloading times, travel time between stations, and customer arrival patterns.
  4. Modeling Demand: Develop a demand model based on historical data to estimate the number of passengers expected during peak hours. This model could take into account factors like day of the week, time of day, and special events.
  5. Simulation Execution: Using R, implement the simulation by iterating over different car numbers. For each car number, simulate the travel process for multiple iterations, considering customer arrivals, boarding times, travel times, and other relevant factors.
  6. Performance Metrics: Define metrics to evaluate the effectiveness of each car number scenario. Metrics could include average waiting time, percentage of satisfied customers, and overall travel time.
  7. Optimization: Based on the simulation results, identify the minimum number of cars required to satisfy customer demand while minimizing waiting times and travel duration.
  8. Visualization: Create visual representations of simulation outcomes, showing the relationship between car numbers, customer satisfaction, and travel efficiency.
  9. Documentation: Prepare a comprehensive document that explains the methodology, simulation process, assumptions, and results. Include graphs, tables, and explanations to support your findings.

Implementation and Code: Below is a high-level code outline for the simulation in R:

r
# Load necessary libraries
library(dplyr)

# Load station data from CSV files
departure_station_data <- read.csv("departure_station_data.csv")
terminal_station_data <- read.csv("terminal_station_data.csv")

# Define simulation parameters
num_simulations <- 1000
car_numbers <- seq(1, 20, by = 1) # Vary the number of cars

# Initialize result containers
results <- data.frame(car_number = numeric(0), avg_waiting_time = numeric(0))

# Loop over different car numbers
for (num_cars in car_numbers) {
total_waiting_time <- 0

# Run simulations for each car number
for (i in 1:num_simulations) {
# Simulate customer arrivals and travel
# Calculate waiting time, travel time, etc.

# Accumulate waiting time
total_waiting_time <- total_waiting_time + calculated_waiting_time
}

# Calculate average waiting time for this car number
avg_waiting_time <- total_waiting_time / num_simulations

# Store results
results <- rbind(results, data.frame(car_number = num_cars, avg_waiting_time = avg_waiting_time))
}

# Visualize results (plot average waiting time vs. car number)
plot(results$car_number, results$avg_waiting_time, type = "l",
xlab = "Number of Cars", ylab = "Average Waiting Time",
main = "Optimizing Car Numbers for Customer Satisfaction")

Conclusion: By following the outlined approach and implementing the simulation in R, you can analyze and determine the optimal number of cars needed to satisfy customer demand during peak hours for the journey from Rajiv Gandhi bus stop to Niranjanpur Square. The simulation results will provide insights into the relationship between car numbers, waiting times, and customer satisfaction, aiding in making informed decisions for service optimization.

Please ensure to customize and adapt the code and explanations according to your specific station data and project requirements.

Jommo Kenyatta University of Agriculture and Technology Customer at Peak Hours Case Study

QUESTION

Description

It is a group case study. My part is to analyze how many cars are needed to satisfy customer at

peak hours from bus stop Rajiv Gandhi, the departure station, to Niranjanpur Square, the

terminal station. U could assume the car number first and then test if this number would work

by using R. The zip called “simulations” is the idea from one of my partners. U could have a look

to see if it’s helpful. Follow the idea if possible. The CSV files in the zip are stations’ data. PLS

upload not only the code but also the explanation with doc since i need to do the presentation

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