A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic

Abstract

Background: Distribution is a very important part of logistics and an activity that is present in every area today. One of the basic problems in distribution is how to correctly determine its price. For this reason, this paper presents a model created to determine the price of the product distribution service. Methods: The model first determines the base of the distribution price, which consists of a fixed and a variable part. The fixed part depends on the distance traveled, and the variable part is defined by fuzzy logic. To determine the variable part, a fuzzy logic system was created that depends on four input variables: inaccessibility of the client’s location, driving time, quantity of goods, and unloading time. The reason for applying fuzzy logic is its ability to set the distribution price for each client individually, without generalization. Certain criteria that affect the distribution price such as type of vehicle, quality of service, and type of goods, which could not be represented by fuzzy numbers, were considered as additional corrective factors. Results: The model was tested on hypothetical examples created by the authors from this field and on examples of company that provide distribution services. In the case study, a comparison was made between the distribution price obtained by applying the created fuzzy logic model and the price defined by the model used by the company "X". Conclusions: The model created in this way enables easy adaptation to constant changes in the prices of oil derivatives due to the COVID-19 pandemic and the war but also considers various unpredictable circumstances that may occur during delivery such as roadworks, crowds, vehicle breakdown, location inaccessibility due to bad weather, etc.

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“…Overtucking the system results in higher loader utilization but lower truck utilization, whereas under-tucking lowers shovel utilization and hence lowers production. For instance, excessive trucking reduces truck productivity while increasing shovel production until the service rate reaches its ideal level [10][11][12].…”