Mitigating Environmental Impact of Perishable Food Supply Chain by a Novel Configuration: Simulating Banana Supply Chain in Sri Lanka

As the world is moving into a sustainable era, achieving zero hunger has become one of the top three Sustainable Development Goals, applying a considerable amount of pressure on the agri-food systems to make decisions contemplating the sustainability dimensions. Accordingly, making effective supply...

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Bibliographic Details
Published in:Sustainability 2022-09, Vol.14 (19), p.12060
Main Authors: Chandrasiri, Chethana, Dharmapriya, Subodha, Jayawardana, Janappriya, Kulatunga, Asela K, Weerasinghe, Amanda N, Aluwihare, Chethana P, Hettiarachchi, Dilmini
Format: Article
Language:English
Subjects:
Agribusiness
Agricultural production
Algorithms
Analysis
Case studies
Climate change
Clustering
Configurations
Decisions
Dynamic characteristics
Environmental impact
Food chains
Food security
Food supply
Global temperature changes
Global warming
Global warming potential
Harvesting
Heuristic methods
Hunger
Inventory
Lead time
Linear programming
Literature reviews
Logistics
Nutrition
Optimization
Perishable foods
Planning
Post-harvest decay
Problem solving
Route planning
Simulation
Supply chains
Sustainability
Sustainable development
System effectiveness
Transportation models
Wholesale industry
Wholesalers
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Summary:As the world is moving into a sustainable era, achieving zero hunger has become one of the top three Sustainable Development Goals, applying a considerable amount of pressure on the agri-food systems to make decisions contemplating the sustainability dimensions. Accordingly, making effective supply chain decisions holistically while achieving sustainability goals has become a major challenge faced by the present agri-food systems. Thus, to address the challenge, a novel supply chain configuration addressing multiple supply chain decisions to reduce global warming potential (GWP) and post-harvest losses have been presented by taking the banana supply chain in Sri Lanka as a case study. In the proposed approach, farmers have been clustered based on their geo positions using K-Means clustering followed by route planning within clusters using a heuristics approach. Retailer points are catered by assigning to wholesalers optimally modeling as an assignment model and then route planning executed using a heuristic approach. The solution generated from the above approaches has been implemented on a simulation platform to calculate the overall supply chain performance including the transportation component, in terms of the net GWP, post-harvest losses, and lead time including routing operations. Simulated supply chain performance has been compared with the existing system and verified the performance of the proposed supply chain configuration. The suggested configuration has reduced the net GWP by 15.3%, post-harvest loss by 2.1%, lead time by 28.2%, and travel distance by 20.47%. The proposed configuration can be further improved by adding dynamic characteristics to the model.
ISSN:2071-1050
2071-1050
DOI:10.3390/su141912060