Code: Multi-objective closed-loop supply chain inventory model with learning and forgetting under carbon emission policies using NSGA-II, MOPSO, and TOPSIS

Description

This repository contains MATLAB and Python code developed for the study titled: "Multi-objective closed-loop supply chain inventory model with learning and forgetting under carbon emission policies using NSGA-II, MOPSO, and TOPSIS" submitted to Applied Soft Computing. The README.txt file provides a description of the included files and instructions on how to use the code.

Steps to reproduce

#Radme.txt Description of the files and how to use the code ## Contents #Matlab -nsga2_model1_CT.m ( Carbon tax policy) -nsga2_model2_CCO.m ( Carbon cap and offset policy) -nsga2_model3_CCT.m( Carbon cap and trade policy) -mopso_model1_CT.m (Carbon tax policy) -mopso_model2_CCO.m ( Carbon cap and offset policy) -mopso_model3_CCT.m ( Carbon cap and trade policy) -topsis.m (choose the best solution from pareto front) -skill_level.m (calculation for skill level for the next cycle) #Python -hyper_volume (calculate the hypervolume from pareto front using Pymoo package) -pareto_front.csv ## Requirements - MATLAB R2022b or later - Optimization Toolbox - PYTHON 3.12 or later -Pymoo package ## How to Run 1. Open the corresponding MATLAB script file based on the selected carbon emissions policy (for either NSGA-II or MOPSO). 2. Run the script to load the sample input data and execute the optimization algorithm. 3. For **NSGA-II**: - The output objective values will be saved in the workspace folders `fp1` (first objective) and `fp2` (second objective). 4. For **MOPSO**: - The output objective values will be saved in the workspace folders `f1` (first objective) and `f2` (second objective). 5. Plot the Pareto fronts over five consecutive cycles using the values from the first and second objectives. 6. Save the resulting Pareto front data into a file named `pareto_front.csv`. 7. Run the Python script `hyper_volume.py` to compute the hypervolume metric from the saved `pareto_front.csv`. 8. To select the best solution from the Pareto front, open and run the `topsis.m` file in MATLAB, providing it with the objective values as input. ### Optional: You may change parameters within to test different scenarios or datasets.

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