Ayşenur Doğanİrem BiliciOsman Kaan DemiralMehmet Serdar ErdoğanOzgur KabadurmusDoğan, AyşenurDemiral, Osman KaanErdoğan, Mehmet SerdarBilici, İremKabadurmuş, ÖzgürM.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. Gençyilmaz2025-10-0620209789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 97898197480519789811509490978303031342521954364, 219543562195-435610.1007/978-3-030-31343-2_572-s2.0-85076227398https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076227398&doi=10.1007%2F978-3-030-31343-2_57&partnerID=40&md5=ff6dc187e96c8618028ae08d931396ffhttps://gcris.yasar.edu.tr/handle/123456789/9315https://doi.org/10.1007/978-3-030-31343-2_57One of the most costly operations in logistics is the distribution of goods. Inefficient vehicle routes increase distribution costs especially for companies performing distribution operations daily. Vehicle Routing Problem (VRP) addresses this inefficiency and optimizes the distribution routes of vehicles. In this study we developed a decision support system to solve the Vehicle Routing Problem with Time Windows and Split Delivery and applied it to a real-life case company. The data of the problem were obtained by a real logistic company which is one of the leading Turkish logistics companies located in Izmir Turkey. The company distributes goods to the customers located in various cities in Turkey and currently does not use any decision-making tool to optimize the routes of its trucks. We formulated the mathematical model as Mixed Integer Linear Programming (MILP) and solved it by using IBM OPL CPLEX. Our proposed decision support system clusters the customers into geographical groups and then optimizes the routes within the clusters. The results of the decision support system can be manually adjusted by the decision maker to fine-tune the routes. We demonstrated the efficiency of our proposed methodology on the regional distribution of the company. The results of the study showed that our proposed model decreases the total distribution distance by 16% and total distribution time by approximately 13%. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessHeterogeneous Fleet, Mixed Integer Linear Programming Model, Optimization, Time Windows, Vehicle Routing Problem, Artificial Intelligence, Decision Making, Distribution Of Goods, Fleet Operations, Integer Programming, Vehicle Routing, Vehicles, Case-studies, Distribution Costs, Distribution Operations, Heterogeneous Fleet, Life Case, Logistics Company, Mixed Integer Linear Programming Model, Optimisations, Time Windows, Vehicle Routing Problems, Decision Support SystemsArtificial intelligence, Decision making, Distribution of goods, Fleet operations, Integer programming, Vehicle routing, Vehicles, Case-studies, Distribution costs, Distribution operations, Heterogeneous fleet, Life case, Logistics company, Mixed integer linear programming model, Optimisations, Time windows, Vehicle Routing Problems, Decision support systemsVehicle Routing ProblemMixed Integer Linear Programming ModelOptimizationTime WindowsHeterogeneous FleetConference Object