Location-allocation with green logistics and transport modeling: consideration of carbon emissions, recycling processes, and shipping frequency
Sina Abbasi et al.
Abstract
This paper presents a new mathematical model for designing a reverse logistics network (RLN) that considers carbon emissions, energy recovery, and shipping frequency. This model minimizes operating costs and environmental impacts through a multi-objective mixed-integer programming (MOMIP). Given the significance of reverse logistics (RL) in waste management, this mathematical model aims to reduce logistics system costs and lower emissions of environmental pollutants. Within the RL system, the processes of collection, reconstruction, recycling, energy recovery, and disposal of products operate as an independent network. This system analyzes the physical flow of goods from consumers to primary suppliers, encompassing the processing, control, conversion, and maintenance of the flow of raw materials, parts, finished products, inventory, and capital. To solve this model, mathematical methods utilize the weighted sum method (WSM). The results of solving the mathematical model reveal that altering the weighting of the objectives produces a decreasing trend for the first objective function and an increasing trend for the second objective function, thus confirming the trade-off between the model’s objectives. In companies, this results in a more efficient use of resources and improved services for customers. Moreover, emphasizing economic and environmental responsibility enhances the public image of companies. The novelty of this work lies in its comprehensive and integrated approach to environmentally friendly RL, bridging the gaps between cost optimization, carbon footprint reduction, and recycling efficiency. Its contributions are both theoretical and practical.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.