Optimization in Car Factory Production

The project involves the creation of optimization algorithms, including backtracking, greedy algorithms, and metaheuristic approaches, aimed at enhancing the efficiency of production processes in car factories.

Nomenclature:

• C: Number of cars
• M: Improvements
• K: Classes (subset of improvements)
• n_e: Set of n consecutive cars at station e
• c_e: At most c_e of the n_e require the installation of the improvement

Penalties:

Non-compliance with n_e and c_e constraints results in penalties. The total penalty is the sum of the penalties from all stations. Exceeding c_e incurs a penalty of k-c_e euros, where k is the number of consecutive cars that required the improvement and exceeded the allowed maximum at that station.

• Note: Incomplete car windows (start and end of the sequence) are also considered.

Input Format:

• 3 strictly positive integers: C, M, K. Number of cars, improvements, and classes.
• M strictly positive integers with the c_e for each improvement (improvements are named {0...n} and are made at different stations).
• M strictly positive integers indicating n_e (also for each improvement)
• K lines, each containing: an integer identifying the class, an integer indicating the number of cars to produce in this class, and M 0s and 1s indicating whether the class requires an improvement or not {0...n}

Input:

The program will receive two command-line arguments:

• the name of the input file
• the name of the output file

Example of Input and Output

Input:          Output:
10 5 3          3 2.3
1 1 1 2 1       0 1 0 1 2 0 2 0 2 1 
2 2 2 3 2
0 4 1 1 0 0 1
1 3 0 1 0 1 0
2 3 0 0 1 0 0

Authors

• Ignacio Gris Martín
• Marc Camps Garreta