An optimization model for clustering a distributed transport network of an industrial enterprise

Economic & mathematical methods and models

Modern conditions of the development of domestic industrial enterprises engender the growing importance of the issues in the area of minimizing the logistic costs when designing the corresponding distribution networks. Due to this circumstance, it seems expedient to create an optimization model for clustering the distributed transport network of an industrial enterprise. The created model allows to determine both the required number of identical transport vehicles to serve contractors (consumers) within the examined distribution network and the formation of optimal routes for transport movement according to the criterion of minimizing the total transportation costs with constraints on a vehicle’s capacity and duration of consumer’s service within each route. The initial data for implementing the optimization model include the characteristics of the distribution network structure concerning the composition of its basic elements (transportation points) and the corresponding connections (pathways), the characteristics of transport vehicles concerning the conditionally variable and conditionally fixed costs, the duration of movement on the transportation network’s connections and the vehicle’s load capacity. To estimate the adequacy of the created model, it was implemented on a practical example for solving the task in the area of small-batch truck traffic routing within Saint Petersburg and Leningrad Oblast. The main stages of implementing the formulated task were the following: forming the initial data for implementing the optimization model; implementing the optimization model for different variants of the vehicle model; analyzing the implementation results for the optimization model for every alternative variant of the vehicle model; forming the dependences of the performing indicators for the routes versus the load capacity of the vehicles used. The analysis of the formed dependences confirmed the adequacy of thecreated optimization model and, as a consequence, its high practical importance.