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Migratory beekeeping routing: a combinatorial optimization problem in apiculture
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AI
DOI：10.1007/s10479-024-06351-4.png)
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Abstract
En 中文
Commercial beekeeping is an emerging business segment which not only generates income, but also contributes to the conservation of biodiversity. In practice, complex decision optimization issues prevailing in beekeeping requires attention to help this business flourish and improve its profitability. Migratory beekeeping is an important initiative in this regard for the continued movement of bee colonies to different locations for beekeeping production. In this article, a migratory beekeeping routing problem (MBRP) which is considered as a variant of the VRP, and considers mainly homogeneous beekeepers, restricted flowering periods at flowering regions, environment maximum capacity of flowering regions, multiple home region and flexible terminal region, as well as selection of best markets for honey produced in different floral regions is studied. We propose a variable neighborhood search (VNS) algorithm to solve the MBRP and apply thirty computational instances to test it. The results indicate the feasibility and efficiency of the VNS algorithm to achieve acceptably good near-optimal solutions while reducing computation time when compared to the exact algorithms in the existing paper. The performance of the proposed VNS is also compared with the ant colony system (ACS) based metaheuristics. We also provide cost-benefit analyses on maintaining the biological balance and extending the flowering period to get extensive information on the issue. Practically, the outcome of this paper can help commercial apiculture organizations to change outdated beekeeping production and operation methods, thus enhancing the production efficiency and reducing costs.
Keywords:
Migratory beekeeping routing problem
MBRP
Variable neighborhood search
VNS
Vehicle routing problem
VRP
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