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A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time
Kayhan Alamatsaz1,2, Abbas Ahmadi3, Seyed Mohammad Javad Mirzapour Al-E-Hashem1
1Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran.
Environmental Science and Pollution Research International
|August 20, 2021
Summary
This study introduces a novel approach to the green capacitated location routing problem (G-CLPR), optimizing facility locations and delivery routes to minimize costs and carbon emissions. The research combines advanced algorithms to efficiently solve complex, real-world logistics challenges.
Area of Science:
- Operations Research
- Logistics and Supply Chain Management
- Environmental Sustainability
Background:
- The location routing problem (LRP) integrates facility location and vehicle routing decisions.
- Growing concerns about environmental impact necessitate green logistics solutions, focusing on reducing greenhouse gas emissions.
- Existing LRP models often lack practical features like time windows, traffic congestion, and capacitated facilities/vehicles.
Purpose of the Study:
- To address the green capacitated location routing problem (G-CLPR) by incorporating realistic factors such as time windows, traffic congestion, and capacity constraints.
- To develop a novel, computationally efficient approach for solving large-scale, stochastic G-CLPR instances.
- To minimize both total operating costs and total emitted carbon dioxide in logistics operations.
Main Methods:
- Development of a mixed-integer programming model for the G-CLPR.
- Utilization of scenario production for solving the stochastic model.
- Application of a hybrid Progressive Hedging Algorithm (PHA) and Genetic Algorithm (GA) for large-scale problems.
- Employing Nondominated Sorting Genetic Algorithm II (NSGA-II) and epsilon constraints for bi-objective optimization.
Main Results:
- The proposed hybrid PHA-GA approach demonstrates satisfactory performance in solving the G-CLPR.
- The method is computationally efficient in finding promising solutions for large-scale instances.
- Sensitivity analysis confirms the robustness and efficiency of the proposed solution method.
Conclusions:
- The study successfully integrates practical features into the G-CLPR, making it more applicable to real-world scenarios.
- The novel combination of PHA and GA offers an effective solution for complex, bi-objective logistics optimization.
- The findings highlight the potential for significant cost and emission reductions in green logistics operations.
Keywords:
Bi-objective optimizationGenetic algorithmGreen location routing problemNSGA-IIProgressive hedging algorithmTime windowMore Related Videos
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