Abstract
The fresh agricultural product (FAP) has highly perishable, hard to storage, and huge cost during the picking, storage, and transportation stage. In this paper, we aim to develop a multi-objective optimization model to minimize the total cost of collecting the FAP, where from the acquisition points aside the planting areas to the collection center, and the cost of transferring them to the processing factories (demand points) as well as to minimize the freshness decay during these processes. A epsilon constraint algorithm is used to convert the objective, freshness decay into constraints, and the model is transformed into a single-objective optimization model. According to the characteristics of the model, a hybrid algorithm based on genetic algorithm is developed. In order to verify the effectiveness of the model and algorithm, an example of yellow peach in Yanling, Hunan, China is constructed. And a comparison algorithm, simulated annealing algorithm, is presented. The results show that the effectiveness and efficiency of the hybrid algorithm based on genetic algorithm is better than that of simulated annealing algorithm. The insights of the sensitivity analysis indicate that the model and algorithm presented in this paper can be extended to other FAPs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
