Gökçe, Mahmut Ali

Profile Picture
Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12629
Name Variants
Mahmut Ali Gökçe
Mahmut Ali Gokce
Job Title
Dr.Öğr.Üyesi
Email Address
Main Affiliation
01.01.09.03. Endüstri Mühendisliği Bölümü
Status
Current Staff
Website
ORCID ID
0000-0001-9809-1462
Scopus Author ID
36484461000
Turkish CoHE Profile ID
Google Scholar ID
os9p_d8AAAAJ
WoS Researcher ID
G-6295-2018
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mahmut.ali.gokce.jpg (9.82 KB)

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
6
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
10
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
2
Research Products
LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
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Documents

16

Citations

220

Go to WoS profile
Scholarly Output

30

Articles

3

Views / Downloads

0/0

Supervised MSc Theses

3

Supervised PhD Theses

1

WoS Citation Count

160

Scopus Citation Count

232

Patents

0

Projects

0

WoS Citations per Publication

5.33

Scopus Citations per Publication

7.73

Open Access Source

4

Supervised Theses

4

JournalCount
4th International Conference on Intelligent and Fuzzy Systems (INFUS)3
International Conference on Intelligent and Fuzzy Systems INFUS 20223
European Transport Research Review2
9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling Management and Control (IFAC MIM)2
International Symposium for Production Research ISPR 20202
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Scholarly Output Search Results

Now showing 1 - 10 of 30
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    Conference Object
    Multi-period vehicle routing & Replenishment problem of neighbourhood disaster stations for pre-disaster humanitarian relief logistics
    (Elsevier B.V., 2019) Mahmut Ali Gökçe; Elif Ercan; D. Ivanov , A. Dolgui , F. Yalaoui
    Natural disasters are uncontrollable situations that affect human life directly. Despite the researches and technological progress it is still not possible to predict when or where the natural disaster will occur beforehand. Natural disasters cause severe loss of lives and damages. In addition they cause physical financial social and environmental losses. Pre-disaster during disaster and post-disaster activities are significant in order to decrease the losses caused by natural disasters. This study is about one of the pre-disaster activities. In the pre-disaster management process a new activity is being tried in Turkey. Called “Neighborhood Disaster Stations” containers filled with emergency relief items such as medicines painkillers antiseptics and canned goods are located at the different predetermined locations. It is important to keep items in these stations usable at all times. Since these commodities have expiration dates they need to be replenished periodically in order to remain useable at any time. The proper time for the replenishment should be determined by considering the probability of reselling or re-using the commodities with the maximum return as much as possible. However replenishing frequently will result in large operational costs. Therefore there is a trade-off between routing costs and replenishment. We propose a novel mixed integer-programming model in order to solve this problem. The proposed model determines the replenishment policy for each commodity in the containers and generates the route of each vehicle within a given planning horizon. The objective of this study is to maximize the total profit which is the difference between expected revenue from reselling and the transportation cost for total routing costs for time periods in the planning horizon. The model determines the replenishment date of each commodity in each disaster container and provides optimal route for each vehicle within planning horizon. The proposed mixed integer programming model is solved optimally for a small instance in IBM ILOG CPLEX Optimization Studio 12.8 and validation of the model is done. Copyright © © 2021 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 1
    Multi Model Multiple Line Balancing Problem Modeling and Real-Life Application
    (Springer Science and Business Media Deutschland GmbH, 2021) Onur Orhan; Doğukan Başgöl; Muharrem Eren Tekin; Ahmet Gülseven; Mahmut Ali Gökçe; Evren Demir; Cansu Yurtseven; Orhan, Onur; Tekin, Muharrem Eren; Gökçe, Mahmut Ali; Demir, Evren; Yurtseven, Cansu; Gülseven, Ahmet; Başgöl, Doğukan; N.M. Durakbasa , M.G. Gençyılmaz
    This study deals with multi-line balancing for multiple products. This problem originates from a real-life problem at a global heat boiler manufacturing company. The objective is to develop a solution method that enables create efficient line balances in a dynamic environment which maximizes the weighted average line efficiency and reduces waste of time for frequent line balancing procedures. Since weighted average efficiency is a key performance indicator for the company this paper focuses on the optimization of this measure. The proposed method is demonstrated with an implementation at three assembly lines where great majority of the company’s production takes place. For this problem an algorithm has been developed that creates a set of feasible scenarios (a scenario pool) in which total demand of each model is divided between available lines under capacity restrictions. For every scenario in the pool a custom multi model assembly line balancing MIP is created automatically and run. From the results best (most efficient) assignment of models’ demand to lines and corresponding line balances are obtained. Furthermore a user-friendly dynamic decision support system (DSS) is developed and proposed solution approaches are embedded in this DSS for the daily operations of the company. The developed DSS enables users to do an efficient line balancing in short computational time and provides the results with detailed line balancing reports. As this problem can be faced in various areas the proposed solution approaches can also be applied to different sectors and factories with little modification. © 2020 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 1
    Matheuristic Algorithm for Automated Guided Vehicle (AGV) Assisted Intelligent Order Picking
    (Springer Science and Business Media Deutschland GmbH, 2023) Simge Güçlükol Ergin; Mahmut Ali Gökçe; Gökçe, Mahmut Ali; Ergin, Simge Güçlükol; C. Kahraman , I.U. Sari , B. Oztaysi , S. Cevik Onar , S. Cebi , A.C. Tolga
    Warehouse management plays an important role in the supply chain. It has received attention from academics and the industry for many years. During this time warehouse management processes have evolved like all other systems in the industry. Preparing orders faster has a key role for companies with an increasing demand from online shopping. The requirements for rapid response to customers have increased. Meanwhile the use of automated guided vehicles (AGVs) has increased because they are used to collect items by pickers in warehouses to prepare customer orders. Assignments of orders to AGVs and routing of AGVs should be done quickly and efficiently to prepare orders. Due to the NP-hardness of the problem it is difficult to solve realistic-size problems in an acceptable amount of time especially when orders from online shopping flow at a fast pace. In this study a new matheuristic algorithm was proposed to minimize the total traveled distance by all AGVs to collect orders with a minimum number of AGVs. This helps to reach a more sustainable system. Minimizing the distance of all AGVs helps for rapid responses to customers’ orders with minimum total energy/cost. Experimental results were provided to compare the computational complexities and performances of the models in this paper. The proposed novel matheuristic algorithm was shown to create intelligent order picking operations in warehouses within a reasonable computational time. © 2023 Elsevier B.V. All rights reserved.
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    Review
    Citation - WoS: 121
    Citation - Scopus: 186
    State-of-art review of traffic signal control methods: challenges and opportunities
    (SPRINGER, 2020) Syed Shah Sultan Mohiuddin Qadri; Mahmut Ali Gokce; Erdinc Oner; Gokce, Mahmut Ali; Qadri, Syed Shah Sultan Mohiuddin; Oner, Erdinc
    Introduction Due to the menacing increase in the number of vehicles on a daily basis abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years sometimes may not even be feasible. For this reason traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems provide insights research gaps and possible directions for future work for researchers interested in the field. Methods In analyzing the complex dynamic behavior of traffic streams simulation tools have a prominent place. Nowadays microsimulation tools are frequently used in TST related researches. For this reason a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion Our review also shows that approximately 77% of the papers included utilizes a microsimulation tool in some form. Therefore it seems useful to include a review categorization and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.
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    Conference Object
    Citation - WoS: 6
    Citation - Scopus: 7
    A novel arc-routing problem of electric powered street sweepers with time windows and intermediate stops
    (Elsevier B.V., 2019) Cansu Yurtseven; Mahmut Ali Gökçe; Gokce, Mahmut Ali; Yurtseven, Cansu; D. Ivanov , A. Dolgui , F. Yalaoui
    Waste collection is an important public service performed by municipalities. Powered street sweeping is an important part of waste collection. Recently electric powered street sweepers are used at an increasing rate for due to their energy efficiency. It is important to provide this public service at a minimum cost. We propose a novel mathematical model for determining routing of electric powered street sweepers considering a heterogeneous fleet with different capacities and battery levels to perform a predetermined service with realistic operational constraints. The objective is to minimize energy consumption used to provide the service travelling and disposal operations. © 2021 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Master Thesis
    Development of Simulation-Based Framework to Evaluate Urban Delivery Policies Using Electric Scooters
    (2025) Rala, Zeynep; Gökçe, Mahmut Ali
    The process of order delivery has become very important as part of urban logistics operations with the rapid increase in electronic commerce (e-commerce). Efficiency of processing and delivery of online orders is crucial for customer satisfaction and viability of the business. Also, solutions are needed for decreasing the carbon footprint of this delivery operations for sustainability reasons. This study presents a simulation-based framework that enables evaluation of different policies for online order processing and delivery in an urban setting utilizing electric scooters with swappable batteries as a sustainable alternative to fossil fuel-powered counterparts under realistic conditions. There is also a detailed computational study. The results from an experimental design using a data set with order arrival times, delivery locations, and the weight and size of packages are also presented. Factor considered for policies include order grouping by location and arrival time, courier routing, and battery swapping rules. The model also highlights the impact of integrating electric vehicles (EVs) into urban logistics by reducing emissions and noise, improving efficiency, and enhancing customer satisfaction. This research offers a comprehensive framework for sustainable delivery that optimizes both operational and environmental performance.
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    Doctoral Thesis
    Bağlantılı sinyalize dönel kavşaklar için sinyal sürelerinin optimizasyonu
    (2022) Qadri, Syed Shah Sultan Mohiuddin; Gökçe, Mahmut Ali; Öner, Erdinç
    Managing high traffic volumes and mitigating traffic congestion including slow-moving traffic at intersections from the urban traffic networks is the key challenge for urban administration. The increase in the number of vehicles on a daily basis in the urban network is causing a continuous deterioration in the traffic situation. This deterioration leads to many detrimental consequences on health, the economy, and the environment. Due to this, it is important to manage the flow of vehicles within these networks efficiently. As an effective intersection design, the roundabout is rapidly gaining attention and popularity among traffic engineers because of its capacity for the mobility of the number of vehicles. This capacity can further be improved through their signalization. The appropriate traffic signal timing concerning the traffic conditions is critical in providing smooth traffic flow. Inappropriate traffic signal timings not only cause delays and inconvenience to drivers but also increase environmental pollution due to excessive fuel consumption and the emission of greenhouse gases. Thus, it is important to investigate the different signal timings to ensure that implemented plan will improve the capacity and the performance of the network. The investigation can be conducted via either field testing or the use of a reliable simulation tool. As the microscopic simulation is safer, less expensive, and faster than field implementation and testing, which is why the simulation models are widely used in both transportation operations and management analysis. Many researchers have made an effort to improve the efficiency of traffic signals using different approaches. However, few studies have been done concerning traffic signal timings at roundabouts. Roundabouts have a different flow dynamic compared to regular intersections. With the increasing use of signalized roundabouts, especially in metropolitan areas, the traffic signal timings of roundabouts need to be studied. This dissertation introduces a simulation-based optimization framework for finding the optimal green phase timings of signal heads located at an isolated and a network of connected signalized roundabouts. In the developed framework, SUMO is used as a simulation tool whereas the genetic algorithm is used as an optimization algorithm. Two realistic simulation models for the roundabouts (isolated and the network) located in downtown Izmir, Turkey are also developed in order to test the frameworks' performances. The results of the proposed frameworks have been compared with the current settings and with Webster's results. It was found the proposed framework outperforms both the current settings and Webster in all performance measures that are outlined in this study.
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    Conference Object
    Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge
    (Springer Science and Business Media Deutschland GmbH, 2022) Selen Burçak Akkaya; Mahmut Ali Gökçe; C. Kahraman , S. Cevik Onar , B. Oztaysi , I.U. Sari , A.C. Tolga , S. Cebi
    Use of Automated Guided Vehicles (AGVs) for material handling purposes has become increasingly popular. They introduce flexibility to the system by increasing the speed responsiveness and freight capacity as well as enabling increased productivity safety efficient resource utilization and reducing costs. These advantages can be realized by intelligent assignment of AGVs to jobs and routing of AGVs to meet production plans. We look at the problem of scheduling and routing of a heterogenous fleet of AGVs consisting of different types based on purpose of use freight and battery charge capacity used for handling transfer jobs in a production environment. The objective is to optimize the schedules and routes of AGVs by minimizing the penalty cost for the late delivery of a parts and energy consumption of the vehicles. To this end a novel mixed integer linear programming model for a heterogenous fleet of AGVs along with charging and energy consumption is proposed where partial recharging is allowed. Proposed model is validated and verified with a test case using IBM OPL CPLEX and results are provided. © 2022 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - WoS: 13
    Citation - Scopus: 16
    Multi-Period Vehicle Routing & Replenishment Problem of Neighbourhood Disaster Stations for Pre-Disaster Humanitarian Relief Logistics
    (ELSEVIER, 2019) Mahmut Ali Gokce; Elif Ercan; Gökçe, Mahmut Ali; Ercan, Elif
    Natural disasters are uncontrollable situations that affect human life directly. Despite the researches and technological progress it is still not possible to predict when or where the natural disaster will occur beforehand. Natural disasters cause severe loss of lives and damages. In addition they cause physical financial social and environmental losses. Pre-disaster during disaster and post-disaster activities are significant in order to decrease the losses caused by natural disasters. This study is about one of the pre-disaster activities. In the pre-disaster management process a new activity is being tried in Turkey. Called Neighborhood Disaster Stations containers filled with emergency relief items such as medicines painkillers antiseptics and canned goods are located at the different predetermined locations. It is important to keep items in these stations usable at all times. Since these commodities have expiration dates they need to be replenished periodically in order to remain useable at any time. The proper time for the replenishment should be determined by considering the probability of reselling or re-using the commodities with the maximum return as much as possible. However replenishing frequently will result in large operational costs. Therefore there is a trade-off between routing costs and replenishment. We propose a novel mixed integer-programming model in order to solve this problem. The proposed model determines the replenishment policy for each commodity in the containers and generates the route of each vehicle within a given planning horizon. The objective of this study is to maximize the total profit which is the difference between expected revenue from reselling and the transportation cost for total routing costs for time periods in the planning horizon. The model determines the replenishment date of each commodity in each disaster container and provides optimal route for each vehicle within planning horizon. The proposed mixed integer programming model is solved optimally for a small instance in IBM ILOG CPLEX Optimization Studio 12.8 and validation of the model is done. (C) 2019 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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    Article
    Simheuristic Framework for Optimizing Urban Mobility at Signalized Roundabouts
    (DAAAM International Vienna, 2026) Gokce, M. A.; Qadri, S. S. S. M.; Oner, E.
    Managing high traffic volumes and traffic congestion at signalized intersections remains a critical urban challenge. Appropriate traffic signal timing (TST) and phase sequencing are essential for ensuring smooth traffic flow. This study presents a microscopic simulation-based heuristic optimization (Simheuristic) framework using the Genetic Algorithm (GA) for optimizing the TST of Four-Legged Two-stops Signalized Roundabouts (FLTSR). The framework is tested using the actual traffic flow through a microscopic simulation model developed in Simulation for Urban Mobility (SUMO). Within this framework, the integrated GA searches for the green TSTs to minimize vehicular queue lengths, while SUMO is used to evaluate those timings. Additionally, four different phase sequence settings are evaluated to find the efficient configuration. The proposed approach is benchmarked against Webster's method and the existing TST plan. In the best-case scenario, the proposed framework improves vehicular flow by mitigating the average time loss, average waiting time, and the average number of vehicles in a queue at the FLTSR up to 35.83 %, 51.91 %, and 50.97 %, respectively, compared to the current setting. (Received in November 2025, accepted in January 2026. This paper was with the authors 1 month for 1 revision.)