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An Optimization Model for Carbon-Constrained Fresh Agri-Food E-Commerce Supply Chains: A Genetic Algorithm and Particle Swarm Optimization Approach
Novrianty Rizky1 , Elisa Kusrini1 , Agus Mansur *2 , Muhammad Ridwan Andi Purnomo1
1- Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia
2- Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia , agusmansur@uii.ac.id
Abstract:   (44 Views)
Fresh agri-food e-commerce operates through complex multi-echelon supply chains, where perishability and transportation increase both operational costs and carbon emissions. Although previous studies have relied on mixed-integer linear programming (MILP) and exact optimization methods, the performance of metaheuristics under carbon-constrained conditions remains largely unexplored. This study extends a carbon-constrained MILP model by incorporating single-sourcing decisions and a planning-horizon emission cap. A feasibility-preserving random-key decoder with a shadow carbon price gene is embedded into Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), both calibrated using a Taguchi L9 design. In a real case study in Java, Indonesia, PSO achieved IDR 605.67 billion, only 0.44% below the exact optimum of IDR 608.33 billion, whereas GA recorded a 5.19% gap. PSO significantly outperformed GA (Mann-Whitney U test, p = 0.0002). The proposed framework provides a solver-independent approach for carbon-constrained agri-food supply chain optimization.
 
Keywords: Mixed-integer linear programming, Agri-food, Food loss and waste, Carbon emission, Genetic alghorithm, Particle swarm optimization
Full-Text [PDF 798 kb]   (17 Downloads)    
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2025/05/26 | Accepted: 2026/11/2
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.