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Ranking of sectors in organized industrial zones according to Natech risk criteria: An application for Gaziantep province in Türkiye
(Elsevier Sci Ltd, 2024) Güven, Emel; Pınarbaşı, Mehmet; Alakaş, Hacı Mehmet; Eren, Tamer
Technological accidents triggered by natural disasters are called Natech accidents. Natech accidents have the impact of increasing the negative effects of disasters. One of the places at risk for a Natech accident is the Organized Industrial Zones (OIZs), where many industrial establishments are located. Although businesses within OIZs are evaluated in terms of security, Natech risk assessment is often ignored. This situation causes Natech accidents to occur. For these reasons, the study focused on evaluating OIZs regarding Natech risk. Many criteria such as electrical resources, natural gas, distance between buildings and so forth are taken into account in Natech's risk assessment. However, it is challenging to make clear decisions and predictions about these criteria due to the nature of the disaster. There is inherently uncertainty and confusion during and after a disaster. For this reason, using fuzzy sets to evaluate criteria is more appropriate. Therefore, the study uses Pythagorean fuzzy sets, which have a wider fuzzy evaluation area. This study also includes the evaluation of alternative sectors in terms of Natech risk. For this purpose, Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) and Pythagorean Fuzzy the Technique for Order of Preference by Similarity to Ideal Solution (PFTOPSIS) methods are used. OIZ in Gaziantep has the largest area compared to those in other cities in T & uuml;rkiye, is chosen as the application area of the study. As a result of the study, it is determined that the release of hazardous substances and flammable criteria is the most crucial criterion, and the most dangerous sector in terms of Natech risk is the chemical sector.
Prioritizing Individuals Who Will Have Covid-19 Vaccine with Multi-Criteria Decision Making Methods
(Gazi Univ, 2023) Yazıcı, Emre; Üner, Sabire İrem; Demir, Asli; Dinler, Sevda; Alakaş, Hacı Mehmet
This study aims to prioritize individuals in vaccination for the effective use of the COVID-19 vaccine, which has limited supply and does not seem possible to be supplied by all countries at the same time. In the study, multi-criteria decision-making methods (MCDM), which offer practical solutions to decision problems, were used considering the structure of the problem. First, the analytic hierarchy process (AHP) method was used to calculate the weights of the criteria. Then, the ranking of the priorities of the individuals was carried out with the PROMETHEE method. Here, the AHP and PROMETHEE methods are used in an integrated manner. It has been determined that the highest priority individual in vaccination is a healthcare worker with a high potential for transmission. In order of priority, the second individual was identified as workers. In this study, a hierarchical structure was created to prioritize individuals who will be vaccinated against COVID-19 and the problem was solved in two stages. A health policy proposal was made to health managers to use limited vaccine resources by prioritizing individuals effectively. In terms of efficient and effective use of resources during possible pandemic periods, the application process of the study provides an exemplary solution for decision-makers and contributes to the solution of similar decision problems encountered both in the literature and in real life. At the same time, offers solution for disasters that require effective use of limited resources, etc. The implementation process of the study may also be taken into account in exceptional circumstances.
Balancing of cost-oriented U-type general resource-constrained assembly line: new constraint programming models
(Springer, 2023) Alakaş, Hacı Mehmet; Pınarbaşı, Mehmet
In simple assembly line balancing problems, it is assumed that the resources required to perform the tasks are available at the relevant station for task assignment. However, each task may need different resource types depending on the difficulty, complexity and technical requirements of the tasks in real life. For this reason, tasks and resources belonging to these tasks must be assigned to the relevant station while balancing the line. In this study, the resource-constrained U-shaped assembly line balancing problem (U-GRCALBP) is discussed. According to the literature research, there is no study dealing with U-GRCALBP. Two different constraint programming (CP) models that define the resource constraints as and/or constraint types with concurrent resource types have been developed. In these models, the sum of resource usage costs and station opening cost minimization is aimed. The models are explained with an illustrative example and the efficiency of the models is tested by deriving new resource constraints on five different data set each taking into account four different cycle times and the number of two and four resource types. The number of stations obtained, the total number of resources used, total station opening and resource usage costs, and CPU time are used as performance criteria The results are compared with the traditional U-type assembly line balancing problem results. The proposed CP models give superior performance on all data sets, especially in terms of total cost. The numerical results show that both CP models are effective in solving the problem. Furthermore, some managerial implications are presented to be useful for professionals, organizations, and society.
Assembly line balancing type-1 problem with assignment restrictions: A constraint programming modeling approach
(Pamukkale Univ, 2021) Pınarbaşı, Mehmet; Alakas, Haci Mehmet
The assembly line balancing problem (ALBP) contains some constraints which are cycle time/number of stations and precedence relations between tasks. However, due to the technological and organizational limitations, several other restrictions, such as linked tasks, incompatible tasks, station, and resource constraints, can be encountered in real production systems. In this study, we evaluate the effect of these restrictions on ALBP. For this purpose, a Constraint Programming (CP) model is proposed. The objective of the model is to minimize the number of stations for given cycle time (Type-1 problem). We investigate the solution quality of the proposed CP model according to the mixed-integer programming (MIP) and ABSALOM in terms of the several performance measurements such as the number of proofing optimal solution, number of the optimal solution, number of the best solution, relative gap between the solution with the optimal solution and average total solution time. Furthermore, the proposed approach is tested on the literature test instances, and the comparison results between models are reported. Although assignment restrictions increase the complexity of the problem, numerical experiments demonstrate that CP is an effective and high-quality solution method in solving ALBP.
Applications Of The Moora And Topsis Methods For Decision Of Electric Vehicles In Public Transportation Technology
(Vilnius Gediminas Tech Univ, 2022) Hamurcu, Mustafa; Eren, Tamer
The technological development of buses among the new alternative concepts is evaluated in this paper. Bus transportation is an important system in the public transportation, which is cheap, flexible and, in many cases, in terms of capacity and speed. But increasing car traffic in the city centre and increasing the emission such as Carbon Dioxide (CO2) in the air are some of the dangerous problems for urban life. Therefore, it is needed the public transportation to stop in-creasing car traffic and needed the cleaner technology for air and environmental quality. Electric Buses (EBs) can play an important role for resident's life quality with improving the urban air quality. However, planners and managers have dif-ficulty in decision-making due to diversified EBs together with the developing technology. Multi-criteria decision-making (MCDM) methods that are analytic decision processes, prepare a good solution for this problem. In this study, 5 EBs are assessed under the special criteria with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization on the basis of the Ratio Analysis (MOORA) methods. These 2 methods are MCDM meth-ods that are used to aim of ranking of alternatives in the complex decision problem. These methods are applied to select the best EB under the 6 criteria. Finally, E5-Bus is selected as the best option that rank of the 1st at all the 3 methods. Besides, MOORA and TOPSIS methods were compared. The results are shown alongside the best bus selection for public transportation that MOORA method is also a strong tool for solving vehicle selection problems in transportation. The proposed model has been validated using existing real applications. The proposed multi-criteria analysis can be used for advising decision-makers in their decision-making process for Electric Vehicles (EVs) in the area of clean transportation.
Analytic network process and TOPSIS methods with selection of optimal investment strategy
(Yildiz Technical Univ, 2013) Görgülü, İnci; Korkmaz, Melek; Eren, Tamer
In this paper, optimal investment strategy selection problem is handled, that is very important for companies, and a analytic network process (ANP) and technique for order preference by similarity to an ideal solution (TOPSIS) based approach is proposed to solve this selection problem. ANP method is used to determine the importance degree of main criteria and subcriteria, TOPSIS method is developed to rank the alternatives. A silver firm's data were analyzed to select optimal investment strategy.
Analysis of operations research methods for decision problems in the industrial symbiosis: a literature review
(Springer Heidelberg, 2022) Yazıcı, Emre; Alakaş, Hacı Mehmet; Eren, Tamer
Industrial symbiosis (IS) is an approach that aims to use resources efficiently by cooperating between independent enterprises in raw materials, energy, and similar sectors. As a result of cooperation, businesses gain economic, environmental, and social benefits. Especially in recent years, IS applications have become widespread due to the problems experienced in the supply of resources. The presence of more than one enterprise in cooperation creates a complex network structure in IS applications. In this complex system, many decision problems are encountered in establishing and effectively maintaining the industrial symbiosis network. Operations research techniques are at the forefront of the methods used to solve decision problems. This study examined studies using operations research techniques in industrial symbiosis. Studies were divided into four classes according to the methods they used: exact methods, heuristic methods, multi-criteria decision-making, and simulation. In the literature review, the studies in the Web of Science (WOS) database are systematically presented by scanning with the determined keywords. As a result of the study, it was analyzed which method was preferred and where the methods could be applied in industrial symbiosis.
Analysis of factors affecting industrial symbiosis collaboration
(Springer Heidelberg, 2022) Sonel, Edanur; Gür, Şeyda; Eren, Tamer
The rapidly increasing population causes an increase in consumption amounts day by day. This leads to negative effects such as the reduction of limited resources. In order to eliminate or reduce such negative effects, sustainable approaches are adopted for the future. Industrial symbiosis is one of these sustainable approaches. Industrial symbiosis is when two or more economic enterprises operating independently of each other form beneficial partnerships. In this study, the factors affecting industrial symbiosis collaboration were determined by literature review and by analyzing these factors; it is aimed to eliminate inefficiencies and to ensure the sustainability of established relations. The criteria determined are weighted with the Analytical Network Process method, which is one of the multi-criteria decision-making methods, and it is aimed to calculate the degree of importance and priority.
Analysis of criteria acting on coronavirus
(Yildiz Technical Univ, 2023) Sonel, Edanur; Gür, Şeyda; Eren, Tamer
Viruses are obligate intracellular parasites that can infect animals, plants, bacteria and many more microorganisms. Viruses are spread by aphids and insects in plants, and by bloodsuck-ing insects in animals. In humans, they can cause flu by spreading through respiratory, hu-man-to-human contact, water and food. Some viruses, however rare, infect humans from an-imals. Coronavirus is one of the viruses transmitted from animals to humans. The new type of coronavirus (Covid-19), which is on the world agenda, is similar to seasonal flu, previously unidentified and contagious virus in humans. In this study, new types of coronavirus and its effects were investigated and studies related to coronavirus conducted worldwide were ana-lyzed. As the treatment process in the world continues, every country has started to take its measures. The measures taken by countries, especially Turkey, have been identified. It is aimed to calculate the importance and priority values of the measures with the Analytic Network Process and Fuzzy Analytic Hierarchy Process methods, which is one of the multi-criteria decision-making methods. As a result of the values obtained, it is planned to prioritize the measures taken while struggling coronavirus and contribute to the struggle against coronavi-rus by giving emphasis to the priority measures.
An integrated model for evaluating the risk factors of crypto-currencies under fuzzy environment
(Pergamon-Elsevier Science Ltd, 2024) Bulut, Merve; Uyar, Mehmet Erkin; Özcan, Evrencan
While blockchain technology and cryptocurrencies offer numerous advantages and innovations, it is essential to be aware that their implementation processes are fraught with a myriad of risk factors. A study in which risks in cryptocurrencies are evaluated using quantitative data is an important need for investors and academics. As far as it is known from the literature, although blockchain technology is frequently used in different fields, there is no study examining the risks in cryptocurrencies with fuzzy set theory. Unlike the common return and security focused studies in the literature, a comprehensive risk factor determination has been made by evaluating a total of 21 criteria that directly and indirectly affect this virtual currency. Within the study, investors have been provided with insights into the alternatives relevant to the risks under determined through the utilization of an alternative set of considerable magnitude, uncommonly encountered in the existing literature. Considering the complexity and uncertainty of the problem, an Analytic Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution method integrated with Spherical fuzzy sets have been used. The most preferred cryptocurrencies have been evaluated according to a set of criteria ranging from their financial structures to their environmental impacts. Ethereum, which stands out with its blockchain structure, has been the best choice. Tether, designed to provide a stable price point, and Dogecoin, designed as a payment system, appeared to be the worst ones.
An Expert System Approach For High School Type Selection
(Gazi Univ, 2011) Ersöz, Süleyman; Aktepe, Adnan
An expert system is a computer program that imitates the procedures by which experts solve problems. This paper describes an expert system application which provides advice to primary school students who are seeking assistance in determining high school type. The system gathers information about student grade point average (GPA), centralized exam score, personality type and socio-economic factors. It assesses student qualifications for seven different school types in Turkey. The expert system recommends a school type for the students and produces a short report explaining the reasons of recommendation. Student GPA and centralized exam score are considered as technical criteria. In addition, social and socio-economic factors such as personality type, parental involvement etc.affecting the decision process are also considered and included in the model.
An Evaluation of the Market Strategies and Decisions of the Contracted Broiler Enterprises in Bolu, Sakarya and Ankara via Analytical Hierarchy Process
(Gorgan Univ Agricultural Sciences & Natural Resources, 2021) Tuncel, Seyfettin; Eren, Tamer; Gür, Şeyda; Şen, Gökhan; Sipahi, Cevat
This study was performed to evaluate the decisions of the contracted broiler enterprises for the integrated company selection in terms of 5 criteria (C1: Stability, C2: Crisis management, C3: Profitability C4: Flexibility, C5: Supply). In this study, 68 enterprises were selected with stratified random sampling among the broiler enterprises, operating in Sakarya (12.0%), Bolu (11.0%) and Ankara (3.2%) those constitute 24.2% of contracted broiler enterprises in Turkey in 2017. The research was conducted with two large scales (A and B) and two small scales (C and D) integrated companies in the broiler sector, where broiler enterprises produce under a contract. According to these 5 criteria, the optimum selection of integrated companies of broiler enterprises among A, B, C, and D was analyzed using Analytical Hierarchy Process (AHP). As a result of the analysis, the importance level of C1, C2, C3, C4, and C5 criteria among 5 criteria was found strategically significant at 46.0%, 20.0%, 18.0%, 10.0% and 6.0%, respectively. Thus, Cl criterion was determined as the most dominant criterion. The selection weights of integrated companies A, B, C, and D were 34.5%, 36.7%, 12.6% and 17.1%, respectively. This result shows that working with large-scale A and B integrated companies is strategically advantageous for broiler enterprises.
Advanced forecast models for the climate and energy crisis: The case of the California independent system operator
(Pergamon-Elsevier Science Ltd, 2025) Bulut, Merve; Aydilek, Hüseyin; Erten, Mustafa Yasin; Özcan, Evrencan
Climate change across the globe, especially extreme temperature events, is increasing pressures on energy systems. The extraordinary situation that California and the West faced in late August and early September 2022, when record temperatures led to a spike in electricity demand, provided an important backdrop for the resilience and sustainability of clean energy technologies. The electricity market managed by the California Independent System Operator is considered in this study to examine the potential impacts on electricity demand spikes and system resilience. The methodology of the research involves analyzing the system operator's responses to electricity demand using advanced deep learning algorithms, convolutional neural network - long-short term memory and attention mechanism models. 1, 3 and 7-days forecasts of electricity demand were made using models in the day ahead market. In 1-day forecasts, while the former models have a mean absolute percentage error value of 12.40%, the latter model has a lower error rate of 10.36%. Overall findings obtained from various scenarios show that the long-short term memory - attention mechanism can more effectively understand complex patterns in energy demand and has the potential to increase system stability against such extreme weather events. The advanced horizon of the study offers an important perspective on how clean energy technologies, especially battery energy storage systems, can provide solutions to today's priority problems such as climate change and extreme temperature.
Actual-time modeling of a subway vehicle and optimal driving management with GA and ABC algorithms
(Academic Publication Council, 2022) Arıkan, Yağmur; Şen, Tolga; Çam, Ertuğrul
The optimization of operations of subway systems has critical importance in terms of energy efficiency and costs. Therefore, driving management of subway vehicles has been gaining more importance day by day. Optimal Driving Management (ODM) is the optimization of the velocity trajectory of a subway vehicle by considering operating conditions and travel time. In this study, the driving of a subway vehicle has been modeled dynamically with all parameters that affect driving. So, a realistic model has been prepared. Then, a new objective function has been proposed to reduce energy consumption by using the subway vehicle's acceleration and braking forces parameters for ODM. The Artificial Bee Colony algorithm (ABC) and Genetic algorithm (GA) have been used on the prepared model to determine the driving dynamics of the subway vehicle. The performance of the algorithms has been evaluated in the real line network, which has multiple stations with different characteristics. The energy consumption has been reduced by 10.47% in GA and 8.92% in ABC compared to the actual driving values. Moreover, the results of the study have been analyzed in terms of passenger comfort, cost, and emission values.
A two-stage stochastic model for an industrial symbiosis network under uncertain demand
(Elsevier Science Inc, 2024) Daş, Gülesin Sena; Yeşilkaya, Murat; Birgören, Burak
Industrial Symbiosis (IS) networks are structures built by volunteer companies with the aim of exchanging unused or residual resources, benefiting all participating companies. The profitability of these volunteer companies is critical as it affects the sustainability of these networks. Fluctuations in a participating company's production level can potentially disrupt its network by altering the quantity and availability of wastes and by-products. In light of these considerations, we analyse the impact of fluctuations in demand for final products of the companies on company profitability, and waste and by-product usage. For this purpose, we formulated a two-stage stochastic programming model and solved it using the Sample Average Approximation (SAA) method. We tested our model on a theoretical IS network comprising companies in the forest products industry. The results demonstrate that companies in the network keep exchanging by-products and remain profitable despite uncertainties in demand. Consequently, we conclude that the established network exhibits resilience to demand fluctuations, which is an important aspect of its sustainability.
A Two-Phase Approach for Reliability-Redundancy Optimization of a Communication Satellite
(Gazi Univ, 2024) Tetik, Taha; Daş, Gülesim Sena; Birgören, Burak
The development and launch of communication satellite projects pose significant challenges and costs. The expenses can range from several hundred million dollars, contingent on factors such as mission objectives, satellite system size and complexity including the launch vehicle, and ground infrastructure. Satellites must be designed to withstand harsh conditions in space, such as the extreme temperatures, radiation, and other hazards, while delivering reliable communication services to its users. However, once a satellite is launched, physical maintenance interventions become infeasible in the event of technical problems. Thus, reliability is a critical aspect for these expensive systems. This study aims to minimize the cost of a high-tech communication satellite by addressing design considerations that meet customer reliability requirements without exceeding power and redundant equipment limits. To achieve this goal, we propose an integer non -linear programming model in this research. To solve the satellite design problem, we adopt a two-stage solution approach. Conventional industrial practices in satellite design often involve iterative attempts to determine the redundancy level of onboard units based on customer reliability requirements. These processes rely heavily on the experience of design engineers who evaluate a limited number of alternatives to determine the number of redundant units, resulting in sub -optimal outcomes. In contrast, our proposed approach systematically handles the problem and yields optimal results. Our findings demonstrate that the proposed two-phase approach can achieve optimal redundancy levels within seconds.
A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect
(Mdpi, 2022) Ersöz, Olcay Özge; İnal, Ali Fırat; Aktepe, Adnan; Türker, Ahmet Kürşad; Ersöz, Süleyman
With the rapid progress of network technologies and sensors, monitoring the sensor data such as pressure, temperature, current, vibration and other electrical, mechanical and chemical variables has become much more significant. With the arrival of Big Data and artificial intelligence (AI), sophisticated solutions can be developed to prevent failures and predict the equipment's remaining useful life (RUL). These techniques allow for taking maintenance actions with haste and precision. Accordingly, this study provides a systematic literature review (SLR) of the predictive maintenance (PdM) techniques in transportation systems. The main focus of this study is the literature covering PdM in the motor vehicles' industry in the last 5 years. A total of 52 studies were included in the SLR and examined in detail within the scope of our research questions. We provided a summary on statistical, stochastic and AI approaches for PdM applications and their goals, methods, findings, challenges and opportunities. In addition, this study encourages future research by indicating the areas that have not yet been studied in the PdM literature.
A set partitioning based goal programming model for the team formation problem
(Wiley, 2022) Daş, Gülesin Sena; Altınkaynak, Büşra; Göçken, Tolunay; Türker, Ahmet Kürşad
In this paper, we offer a multi-objective set-partitioning formulation for team formation problems using goal programming. Instead of selecting team members to teams, we select suitable teams from a set of teams. This set is generated using a heuristic algorithm that uses the social network of potential team members. We then utilize the proposed multi-objective formulation to select the desired number of teams from this set that meets the skill requirements. Therefore, we ensure that selected teams include individuals with the required skills and effective communication with each other. Two real datasets are used to test the model. The results obtained with the proposed solution are compared with two well-known approaches: weighted and lexicographic goal programming. Results reveal that weighted and lexicographic goal programming approaches generate almost identical solutions for the datasets tested. Our approach, on the other hand, mostly picks teams with lower communication costs. Even in some cases, better solutions are obtained with the proposed approach. Findings show that the developed solution approach is a promising approach to handle team formation problems.
A sample application for scheduling search and rescue teams in an earthquake disaster
(Yildiz Technical Univ, 2024) Akdas, Elif; Eren, Tamer
Disasters that occur unexpectedly cause many material and moral losses. T & uuml;rkiye is an earthquake zone that frequently experiences earthquakes as a result of the convergence of the plates. The period in which earthquakes will occur is unknown, but the damage caused by the necessary planning can be reduced. Disaster management, which is a dynamic and versatile process, is a system that requires effective organization to minimize losses and damages. In the response phase of disaster management, search, and rescue teams work against time pressure by undertaking complex tasks in their live-focused debris work in multiple and scattered disaster areas. In the 6 February 2023 Kahramanmara & scedil; earthquakes, it has been seen how important it is to dispatch the teams to the regions quickly. In this study, the problem of scheduling 380 Disaster and Emergency Management Presidency (AFAD) search and rescue teams to be dispatched to 8 areas where destruction was experienced was handled by considering the earthquake scenario in a province located in a risky region. The purpose of the problem is to dispatch the appropriate number of teams to the correct disaster areas. The mathematical model created with the goal programming method is solved with the IBM ILOG optimization program. According to the solution results obtained, the optimal assignment and schedule of the teams are created by providing the targeted constraints.
A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant
(Polish Maintenance Soc, 2021) Özcan, Evrencan; Yumuşak, Rabia; Eren, Tamer
Countries need to develop sustainable energy policies based on the principles of environmental sensitivity, reliability, efficiency, economy and uninterrupted service and to maintain their energy supply in order to increase their global competitiveness. In addition to this impact of sustainable energy supply on the global world, maintenance processes in power plants require high costs due to allocated time, materials and labor, and generation loss. Thus, the maintenance needs to be managed within a system. This makes analytical and feasible maintenance planning a necessity in power plants. In this context, this study focuses on maintenance strategy optimization which is the first phase of maintenance planning for one of the large-scale hydroelectric power plants with a direct effect on Turkey's energy supply security with its one fifth share in total generation. In this study, a new model is proposed for the maintenance strategy optimization problem considering the multi-objective and multi-criteria structure of hydroelectric power plants with hundreds of complex equipment and the direct effect of these equipment on uninterrupted and cost-effective electricity generation. In the model, two multi-criteria decision-making methods, AHP and COPRAS methods, are integrated with integer programming method and optimal maintenance strategies are obtained for 571 equipment.

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