Browsing by Author "Şahman, Mehmet Akif"

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A Comparative Application Regarding the Effects of Traveling Salesman Problem on Logistics Costs
(2019) Dündar, Abdullah Oktay; Şahman, Mehmet Akif; Tekin, Mahmut; Kıran, Mustafa Servet
The necessity of transporting goods from production facilities to buyers requires every company to manage logistics. While the quantity of products ordered has been decreasing in recent years, the number of orders has been increasing. This situation leads to higher logistics costs and more attempts to control logistics costs by business managers. One way to decrease logistics costs is the optimization of traveled distances. The Traveling Salesman Problem (TSP) attempts to optimize travel distances by changing the order of the locations to be visited. By doing so, it reduces the logistics costs associated with travel distances. However, there are also some parameters of logistics costs that are not related to travel distances. This paper examines the effects of optimization results by TSP on logistics costs, using seven different methods to consider a real logistics problem, and comparing the results. Then it discusses the variation in logistics costs due to TSP.
Citation - WoS: 36
Citation - Scopus: 41
Debohid: a Differential Evolution Based Oversampling Approach for Highly Imbalanced Datasets
(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Kaya, Ersin; Korkmaz, Sedat; Şahman, Mehmet Akif; Çınar, Ahmet Cevahir
Class distribution of the samples in the dataset is one of the critical factors affecting the classification success. Classifiers trained with imbalanced datasets classify majority class samples more successfully than minority class samples. Oversampling, which is based on increasing the minority class samples, is a frequently used method to overcome the class imbalance. More than two decades, many oversampling methods are presented for the class imbalance problem. Differential Evolution is a metaheuristic algorithm that achieves successful results in a lot of domains. One of the main reasons for this success is that DE has an effective candidate individual generation mechanism. In this work, we propose a novel oversampling method based on a differential evolution algorithm for highly imbalanced datasets, and it is named as DEBOHID (A differential evolution based oversampling approach for highly imbalanced datasets). In order to show the success of DEBOHID, 44 highly imbalanced ratio datasets are used in experiments. The obtained results are compared with nine different state-of-art oversampling methods. In order to show the independence of the experimental results to classifier, Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) are used as a classifier in the experiments. AUC and G Mean metrics are used for the performance measurements. The experimental results and statistical analyses have shown the triumph of the DEBOHID.
Citation - WoS: 10
Citation - Scopus: 13
Discrete Artificial Algae Algorithm for Solving Job-Shop Scheduling Problems
(Elsevier B.V., 2022) Şahman, Mehmet Akif; Korkmaz, Sedat
The Job-Shop Scheduling Problem (JSSP) is an NP-hard problem and can be solved with both exact methods and heuristic algorithms. When the dimensionality is increased, exact methods cannot produce proper solutions, but heuristic algorithms can produce optimal or near-optimal results for high-dimensional JSSPs in a reasonable time. In this work, novel versions of the Artificial Algae Algorithm (AAA) have been proposed to solve discrete optimization problems. Three encoding schemes (Random-Key (RK), Smallest Position Value (SPV), and Ranked-Over Value (ROV) Encoding Schemes) were integrated with AAA to solve JSSPs. In addition, the comparison of these three encoding schemes was carried out for the first time in this study. In the experiments, 48 JSSP problems that have 36 to 300 dimensions were solved with 24 different approaches obtained by integrating 3 different coding schemes into 8 state-of-the-art algorithms. As a result of the comparative and detailed analysis, the best results in terms of makespan value were obtained by integrating the SPV coding scheme into the AAA method. © 2022 Elsevier B.V.
Citation - WoS: 14
Citation - Scopus: 15
A Hybrid Binary Grey Wolf Optimizer for Selection and Reduction of Reference Points With Extreme Learning Machine Approach on Local Gnss/Leveling Geoid Determination
(ELSEVIER, 2021) Tütüncü, Kemal; Şahman, Mehmet Akif; Tuşat, Ekrem
Modeling and optimization from natural phenomena and observations of the physical earth is an extremely important issue. In the light of the developments in computer and artificial intelligence technologies, the applications of learning-based modeling and optimization techniques in all kinds of study fields are increasing. In this research, the applicability of four different state-of-the-art metaheuristic algorithms which are Particle swarm optimization (PSO), Tree-Seed Algorithm (TSA), Artificial Bee Colony (ABC) algorithm, and Grey Wolf Optimizer (GWO), in local GNSS/leveling geoid studies have been examined. The most suitable geoid model has been tried to be obtained by using different reference points via the well-known machine learning algorithms, Artificial Neural Network (ANN) and Extreme Learning Machine (ELM), at the existing GNSS/leveling points in Burdur city of Turkey. In this study, eight different hybrid approaches are proposed by using each metaheuristic algorithm together with machine learning methods. By using these hybrid approaches, the model closest to the minimum number of reference points has been tried to be obtained. Furthermore, the performance of the hybrid approaches has been compared. According to the comparisons, the hybrid approach performed with GWO and ELM has achieved better results than other proposed hybrid approaches. As a result of the research, it has been seen that the most suitable local GNSS/Leveling geoid can be determined with a lower number of reference points in an appropriate distribution. (C) 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 4
A Mathematical Model for Multi-Period Multi-Stage Multi-Mode Multi-Product Capacitated Wheat Supply Network Design Problem and a Case Study
(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2022) Dündar, Abdullah Oktay; Tekin, Mahmut; Peker, Kenan; Şahman, Mehmet Akif; Karaoğlan, İsmail
Purpose: The purpose of this paper is to develop a cost-effective network design model, including transport and storage that will enable a decision at a tactical and strategic level to be taken to optimize the structure of the Turkey wheat supply chain. Theory and Methods: The proposed model was developed using 0-1 mixed integer linear programming. First, the model was tested with the data obtained from the interviews with 103 farmers to investigate whether the model worked properly. Then, the case study method was used to test the model with the data obtained from a Flour Mill Company which is one of the first 500 industrial enterprises in Turkey. Results: The results were obtained by using IBM ILOG CPLEX Optimization Studio 12.6.2.0. Although farmers used eighteen different-capacity vehicles for transport, five different-capacity vehicles were used in the model. Smaller capacity vehicles were used by the farmers; however, the model proposed larger-capacity vehicles. Wheat was transported by farmers with 10-ton vehicles using 100 trips, 20-ton vehicles using 115 trips and 30-ton vehicles using 13 trips. However, the model proposed transportation using 10-ton vehicles and 13 trips, 20-ton vehicles and 22 trips and 30-ton vehicles and 151 trips. The proposed model reduced the number of smaller-capacity vehicle trips and increased the number of larger-capacity vehicle trips. As a result, the model has significantly reduced total transportation costs in wheat supply chain in Turkey. Conclusion: In this study, a mathematical model for multi-period multi-stage multi-mode multi-product capacitated was developed for wheat supply chain in Turkey. The developed supply network design model selected a vehicle of the appropriate capacity to suit the amount of load to be transported in the wheat supply chain and decrease the number of vehicle trips. This improvement gained by the model can be applied to other sectors. The model showed that supply warehouses, which have been considered for rental to the private sector, can be effectively used for cost-effectiveness. Additionally, the consolidation of the purchased wheat provides an alternative way for the wheat to be transported, in particular by rail.