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Method of logistics distribution of orders based on genetic algorithms and fuzzy evaluation


A.V. Ryazanov – Post-graduate Student, Department of computer facilities, Smolensk Branch of National Re-search University “MPEI”

The article discusses the proposed method of distribution logistics orders based on the zone splitting the territory using the developed algorithm genetic clustering and fuzzy evaluation.
This method is based on the preliminary zone partitioning of the service area using the genetic clustering algorithm, using the modified Hungarian method, in which instead of the integral efficiency criteria, the values of membership functions are used for calculations. Such a replacement of the integral criterion allows to take into account a large set of factors in the distribution and make the distribution algorithm more flexible and take into account the fuzziness that arises in the process of customer service.
Zonal splitting is carried out on the basis of known coordinates of the territorial objects and the quantity received for a certain period of orders. It is preliminary expected to determine the number of orders received from all territorial objects (houses) to identify and combine these objects into zones. The zone includes a lot of objects that are in close proximity to each other and from a place with a high intensity of orders.
The considered algorithm of order distribution to all received orders in each zone is focused on the search for a vehicle that will be able to fulfill the order most effectively. Due to the fact that vehicles and orders are considered for each zone, this reduces the dimensionality of the problem being solved and thereby reduces the complexity and laboriousness of the cal-culations.
Accounting for fuzziness makes it possible to more efficiently distribute orders for drivers, equalize drivers in load and income, thereby supporting their interest in work. Using the proposed method (using the taxi service as an example) makes it possible to increase the efficiency of solving the problem of distribution of logistics orders by an average of 20%.

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May 29, 2020

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