Erdoğan, Mehmet Serdar
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Araş.Gör.
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01.01.06.03. Lojistik Yönetimi Bölümü
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Former Staff
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Sustainable Development Goals
1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Scholarly Output
5
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1
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0/1
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WoS Citation Count
4
Scopus Citation Count
20
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WoS Citations per Publication
0.80
Scopus Citations per Publication
4.00
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1
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1
| Journal | Count |
|---|---|
| 19th International Symposium for Production Research ISPR 2019 | 2 |
| Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi | 1 |
| International Symposium for Production Research ISPR 2020 | 1 |
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5 results
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Conference Object Citation - Scopus: 1Building a Decision Support System for Vehicle Routing Problem: A Real-Life Case Study from Turkey(Springer Science and Business Media Deutschland GmbH, 2020) Ayşenur Doğan; İrem Bilici; Osman Kaan Demiral; Mehmet Serdar Erdoğan; Ozgur Kabadurmus; Doğan, Ayşenur; Demiral, Osman Kaan; Erdoğan, Mehmet Serdar; Bilici, İrem; Kabadurmuş, Özgür; M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. GençyilmazOne 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.Conference Object Citation - Scopus: 8Container Demand Forecasting Using Machine Learning Methods: A Real Case Study from Turkey(Springer Science and Business Media Deutschland GmbH, 2021) Ayhan Darendeli; Aylin Alparslan; Mehmet Serdar Erdoğan; Ozgur Kabadurmus; Erdoğan, Mehmet Serdar; Darendeli, Ayhan; Kabadurmuş, Özgür; Alparslan, Aylin; N.M. Durakbasa , M.G. GençyılmazThe container demands in ports significantly fluctuate over time and accurate container demand forecasting is essential for logistics companies because they can make their future business plans accordingly. In maritime transportation container slot agreements are generally made two times in a year. A slot is one Twenty-Foot Equivalent Unit (TEU) space in a container ship and early booking of a slot is less costly for a company. Therefore the accurate prediction of future container demands is crucial for companies to reduce their costs and increase their profits. In this study we developed various forecasting models using machine learning methods to accurately predict the future container demands for the largest maritime transportation and logistics company of Turkey. The main aim is to provide accurate container demand forecasts for the company so that it can optimize the container slot bookings. To forecast the container demand we used the company`s internal demand data as well as various external data such as gross domestic product (GDP) inflation rate and exchange rate. We built four forecasting models based on Linear Regression Boosted Decision Tree Regression Decision Forest Regression and Artificial Neural Network Regression algorithms. The performances of these methods were evaluated according to Coefficient of Determination Mean Absolute Error Root Mean Square Error Relative Absolute Error and Relative Squared Error. The case study showed that Boosted Decision Tree Regression and Decision Forest regression methods yield the best forecasting accuracy. © 2020 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 1The Impacts of Foldable Containers Street-Turn and Depot-Direct Strategies on Empty Container Repositioning Cost(Springer Science and Business Media Deutschland GmbH, 2020) Mehmet Serdar Erdoğan; Ozgur Kabadurmus; Erdoğan, Mehmet Serdar; Kabadurmuş, Özgür; M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. GençyilmazIn global logistics the trade imbalances between the exporting and importing countries (and regions) cause the empty container repositioning problem. While export dominant countries face the difficulty to find empty containers to be loaded and shipped import dominant countries have a surplus of empty containers and try to get rid of them because they are stacked at storage areas of the seaports. Transporting empty containers to the export dominant countries is as costly as transporting the loaded ones because they keep the same storage space and require the same time and handling operations. In this study we formulated a novel Mixed-Integer Linear Programming (MILP) model considering a multi-period and multi-region shipping network to minimize the total cost for empty container repositioning operations. We investigated the impacts of foldable containers street-turn and depot-direct strategies on the container repositioning cost. To test our proposed model a hypothetical case study has been developed. The total costs of different strategies (i.e. street-turn depot-direct foldable containers) and their combinations are compared and the results are discussed. © 2022 Elsevier B.V. All rights reserved.Master Thesis Sustainable, Reliable and Multimodal Supply Chain Design(2018) Erdoğan, Mehmet Serdar; Kabadurmuş, ÖzgürEmerging issues and new challenges of globalization force companies to design their supply chains not only for minimizing cost but also for considering other factors. Supply chain managers are exposed to the new environmental regulations to reduce their carbon emissions and compelled to consider other overlooked factors, such as risk. In this thesis, a multi-echelon multimodal supply chain network design problem with multiple product and components that takes economic, environmental and risk factors into account was considered. The problem is modeled as a mixed integer linear problem (MILP) and constrained by a carbon cap-and-trade scheme and a total risk threshold. The problem realistically portrays supply chain network design considering green and risk factors simultaneously that has not been modeled previously. The proposed model has been tested on randomly generated hypothetical test instances. The impacts of different risk thresholds are analyzed in order to observe how risk affects supply chain cost. Unit carbon price changes are also investigated. The effects of transportation mode differences on cost and emission are also tested by comparing unimodal and multimodal transportation options. The results reveal that multimodal transportation yields a lower supply chain cost and less environmental harm. Also, as the carbon price increases, total carbon emission reduces. Lastly, as the managers become more risk aversive, supply chain tend to be more costly.Article Citation - WoS: 4Citation - Scopus: 10Bi-Objective green vehicle routing problem minimizing carbon emissions and maximizing service level(GAZI UNIV FAC ENGINEERING ARCHITECTURE, 2023) Ozgur Kabadurmus; Mehmet Serdar Erdogan; Erdoğan, Mehmet Serdar; Kabadurmus, OzgurIn this study a bi-objective Green Vehicle Routing Problem is presented as an extension of the well-known Vehicle Routing Problem. Green Vehicle Routing Problem aims to improve routing decisions of companies using Alternative Fuel Vehicles to reduce carbon emissions. The presented problem herein has two objectives that are the minimization of total carbon emissions and the maximization of service level. While total carbon emission is assumed to be proportional to total distance cargo delivery time window violations of customers are considered as an indicator of service level. The problem was modeled as Mixed-Integer Linear Programming and epsilon-constraint method which is a multi-objective optimization method is developed to solve it. To effectively solve large problem instances a clustering-based heuristic method is proposed. The heuristic method achieved a good performance by finding near Pareto-optimal solutions that are found by the MILP model. Our proposed mathematical model and heuristic method are tested on seven realistically designed hypothetical case studies. According to the results the minimization of carbon emission and maximization of service level are two conflicting objectives. As the service level increases the number of vehicles and carbon emissions also increase. As carbon emission increases and time windows violation decreases more vehicles and alternative fuel stations are used.
