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

<i>Background</i>: 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...

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Main Authors: Predrag Grozdanović, Anđela Gligorijević, Milan Andrejić, Miloš Nikolić, Milorad Kilibarda
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Logistics
Subjects:
Online Access:https://www.mdpi.com/2305-6290/7/3/62
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author Predrag Grozdanović
Anđela Gligorijević
Milan Andrejić
Miloš Nikolić
Milorad Kilibarda
author_facet Predrag Grozdanović
Anđela Gligorijević
Milan Andrejić
Miloš Nikolić
Milorad Kilibarda
author_sort Predrag Grozdanović
collection DOAJ
description <i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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". <i>Conclusions</i>: 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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spelling doaj-art-e0cf8128c9eb4c37b6dcc2c5ceae8cf12025-08-20T02:51:03ZengMDPI AGLogistics2305-62902023-09-01736210.3390/logistics7030062A New Model for Determining the Price of Product Distribution Based on Fuzzy LogicPredrag Grozdanović0Anđela Gligorijević1Milan Andrejić2Miloš Nikolić3Milorad Kilibarda4Faculty of Transport and Traffic Engineering, University of Belgrade, 11010 Belgrade, SerbiaFaculty of Transport and Traffic Engineering, University of Belgrade, 11010 Belgrade, SerbiaFaculty of Transport and Traffic Engineering, University of Belgrade, 11010 Belgrade, SerbiaFaculty of Transport and Traffic Engineering, University of Belgrade, 11010 Belgrade, SerbiaFaculty of Transport and Traffic Engineering, University of Belgrade, 11010 Belgrade, Serbia<i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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". <i>Conclusions</i>: 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.https://www.mdpi.com/2305-6290/7/3/62distributionlogisticscostspricefuzzy logicmodel
spellingShingle Predrag Grozdanović
Anđela Gligorijević
Milan Andrejić
Miloš Nikolić
Milorad Kilibarda
A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
Logistics
distribution
logistics
costs
price
fuzzy logic
model
title A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
title_full A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
title_fullStr A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
title_full_unstemmed A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
title_short A New Model for Determining the Price of Product Distribution Based on Fuzzy Logic
title_sort new model for determining the price of product distribution based on fuzzy logic
topic distribution
logistics
costs
price
fuzzy logic
model
url https://www.mdpi.com/2305-6290/7/3/62
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