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Atık su arıtma ağları için kapasite genişletmeli dinamik kapasiteli tesis yerleşimi ve ağ dizaynı problemi
In our rapidly changing world, especially the increase in population and industrialization activities results in significant increases in wastewater production, which leads to the problem of treatment of the wastewater produced. Therefore, developing effective wastewater treatment networks continues to be vital to ensure environmental sustainability. In this context, wastewater treatment facilities must be located in the most appropriate places to make the best use of limited resources and the network required for wastewater transportation should be designed to meet future needs with the least cost. Although the facility location and network design problems in the literature are related to this issue, they do not cover some specific constraints for wastewater transport and often have a static structure in which time is not taken into consideration. In this thesis, firstly, a dynamic mixed-integer linear programming model for wastewater treatment facility location selection and network design is proposed in which facilities and links are assumed to be capacitated. Later on, a more complicated model which allows the facilities to be expanded in case of need according to time-dependent demand is proposed and defined on a three-dimensional space. Several test experiments are conducted for the proposed models and they are solved with Python modeling library for optimization DOcplex and CPLEX version of 20.1. Moreover, extreme test instances are generated and their results are observed to verify the proposed models.
Taşıma sürelerine duyarlı tarifeli gemi çizelgeleme ve konteyner rotalama problemi için matsezgisel bir yaklaşım
Along with globalization, due to the gradual growth in commercial cargo volumes in maritime transportation, the use of operations research based decision support systems has gained great importance. Employment of proper transportation models incorporating cost and service quality considerations ensures that the right decisions are made at operational, tactical, and strategic levels. In this context, for an available transportation fleet, tactical planning of ship schedules and the routing of containers depending on the operational needs constitute the main problems to be solved in maritime transportation. Simultaneous handling of these problems is of great importance for obtaining high quality solutions based on the revenue, operational costs, and service quality measures. Similarly, transit times of the schedule and customer demand pattern are also important issues, which should be considered within the solution process. The problem under consideration is described as the integrated Liner Ship Scheduling and Container Routing Problem (LSSCRP). Since the problem is of NP-hard complexity, its optimal solution in a reasonable time is not possible in general. In this thesis, a matheuristic solution approach for the integrated problem LSSCRP is developed, where the effect of the transit times on customer demand is taken into consideration. For this purpose, the liner ship scheduling and the container routing with transit time sensitive demand are solved iteratively in a matheuristic framework employing a variable neighborhood search (VNS) algorithm. The performance of the developed LSSCRP matheuristic is tested on a set of different problems.
Öncelikli kuyrukları dikkate alan havuz temelli stokastik sipariş toplama sistemi
E-commerce is a phenomenon that shapes the shopping needs of today's world and enables every product to reach our homes with a single click, shaping the life of the consumption society of the 21st century. E-commerce is a market whose users are increasing every year. Especially in the post-covid period, people far from e-commerce applications have also started to use this system. In this thesis, the bottleneck created by online orders and the increasing workload in online order retailers on special days are focused on. Markov models are a widely used method in modeling the preparation and waiting process of such orders. Future orders can be determined with Markov chains and the waiting times in the queue can be found as exact solutions without the need for simulation. Classifying orders and prioritizing certain order classes can avoid bottlenecks. Priority orders are generally expected to include more valuable product lines, as retailers want to deliver more valuable items sooner to satisfy relevant customers sooner. In this study, an order fulfillment model using the priority model is envisaged and two models are considered with priority orders. In the first model, we assume different service rates for different priority classes. Thus, we aim to specify the conditions where the priority order model decreases expected waiting time and increase the customer satisfaction. In the second model, same service rates for different priority classes are assumed but order cancellations are also considered by introducing a threshold waiting time level for every customer's waiting sensitivity. As waiting time sensitivities, deterministic and exponential values are considered. Moreover, the proportion of serviced customers as well as the expected waiting time is regarded as another performance measure of the system because order cancellations are possible. Lastly, numerical results are obtained to show our statements.
Evde sağlık hizmetleri problemi için veriye dayalı bir çözüm yöntemi
Development in technology and healthcare has extended the average life expectancy globally and has led to an increase in the elderly population. Due to the change in population and increase in health awareness, the demand and need of individuals for healthcare services that prevent and treat diseases have increased as well. Especially during the COVID-19 pandemic, solutions have been developed for patients and elderly people who should not leave their houses to receive treatment at home. These services are called Home Health Care (HHC) systems. In the HHC system, there are patients who need to receive treatment at home and health care personnel who provide various health services to patients at their homes. In this context, human resource planning in the HHC system is crucial. The decisions that need to be made in human resources planning for HHC system consist of assigning the health care personnel to patients, scheduling the visits of health care personnel to patients, and constructing routes for the visits. The problem that integrates these three decisions is called the Home Health Care Problem (HHCP). Since HHCP is of NP-Hard complexity, solving it in an integrated manner within a reasonable time is not always possible. In this thesis, a HHCP variant that takes continuity of care, time windows of patients, and workload of operators into consideration is solved using a two-phase solution approach where the assignment and scheduling problems are handled in the first stage and the routing problem is handled in the second stage. In order to deal with the possible infeasibility in the routing stage and achieve high-quality results, the solution obtained from the two-phase approach is improved using a matheuristic framework that employs a Variable Neighborhood Search metaheuristic. Within the framework, the resulting routes are evaluated by a data driven method that estimates the route durations using the geographical locations of the patients from the historical routing information. In this context, the main topic of this thesis is to solve the HHCP using a matheuristic framework and to develop a data driven method to improve the solution obtained by the two-phase approach.
GENeSYS-MOD Türkiye: Türkiye makro enerji sisteminin düşük karbonlu geleceği için nicel senaryolar
Global energy consumption is increasing constantly. Countries are most affected economically and environmentally by these changes. A change in the supply-demand balance in the energy sector may result in an increase or decrease in the price of energy and investments made in the energy sector to meet the rising demand are economic effects. Additionally, the sustainability of energy systems is getting more significant as a result of these environmental effects of chosen energy sources. As a result of the changes, energy system models are widely used to create strategic energy policies, environmental goals, and technical advancements. The first oil crisis that occurred in 1973 is defined by excessive price increase and insufficient petroleum supply on the global market. As a result of the crisis, there is an increase in interest in energy supply that is based on multiple sources, energy efficiency, and energy conservation. Consequently, a significant increase in the quantity and complexity of energy system models used for analyzing the change is observed. The purpose of this thesis is to better provide insights and evaluate how the Turkish energy sector will change over time, as well as to provide the country with the necessary policy decisions based on realistic, meaningful, and reliable findings. In this context, the quantitative scenarios for low-carbon futures of the Turkish energy system are analyzed at aggregated (country level) and regionally disaggregated (NUTS-1 level) levels. Four different scenarios are used for the future of Turkish energy system. They are quantified and implemented for the Turkish energy system using the open-source, "bottom-up" structure global energy system model, GENeSYS-MOD v3.0. All scenario results at both levels are compared. Disaggregating ensures more investment in photovoltaic technologies, more hydrogen generation and usage by 2050 and integration of electricity and hydrogen storage systems as early as 2025. The insights for the Turkish energy system are improved with the disaggregated model results. The major reason for these differences is the better estimation of regional renewable capacity factors (wind and solar) in the disaggregated level compared to aggregated level.
AC ile çalışan evlerden DC ile çalışan evlere geçiş
Accelerating solar systems and increasing electronic appliances in residential buildings have caused researchers to assert rising application of direct current (DC) powered systems in homes. Thanks to this application, cost savings in electric bills, energy savings and reduction in CO2 emission have been provided in some projects in some countries such as America. In this paper, we state a scenario that a home with traditional alternating current (AC) powered appliances are replaced with DC devices. The location of the presented home is in Istanbul in Turkey. It is a 4-person home. Its electric is sourced from electricity grid supplied from natural gas power plants. However, in our projected scenario, the home gains its electricity from solar photovoltaic system. It is off-grid. It has solar panels, battery and charge controller. The questions which devices can be replaced, how much savings in terms of energy and electric bill is provided, what are economical, technical, social and environmental problems in this AC-DC transition and which system between DC and AC for residential buildings is more preferable based on the AHP method are answered in the presented study. Results show that 74.129-percentage cost saving in the electric bill is provided. However, the problems that nonexistence in some residential DC devices in the market, immaturity of DC system in terms of technical issue, added system costs, less information about DC by experts and related people and a public bias that DC is unsafe exist. In this study, based on these defined advantages and disadvantages of DC and AC system, criteria and sub-criteria have been determined for analytic hierarchy process (AHP) method. By the way of AHP, the result that DC system is more preferable than AC system for residential buildings has been reached.
Hazır giyim sektöründe transfer, indirim ve iade kararlarının optimizasyonu
Due to its effect on profit, costs, and service levels, supply chain management has played a critical role in the fashion industry. A high degree of demand uncertainty makes it hard to respond to the customers' needs. Customers require wider product variety and higher levels of responsiveness at lower prices. These enforce the retailers to utilize supply chain management strategies that enable them to satisfy their customers' needs. Still, it is inevitable to stock-out or to have excess inventory as a result of mismanagement of available resources. While stock-outs affect the service level and cause lost sales, excess inventory is sold with reduced prices at lower profit margins at the end of the selling season. Thus, to overcome these challenges, supply chain decisions have to be made effectively. Transshipment, markdown, and return decisions are among the critical decisions of the fashion supply chain. The problem of reallocating the available inventory of the stores among each other to decrease the chances of stock-out and excess inventory is called the transshipment problem. After allocating the initial inventory to the stores according to the predicted demand, the forecast may be updated in the light of new market information. Transshipment re-balances the inventories of the stores based on the updated demand forecast. Return, however, is the problem of deciding the products, which will be surplus at the end of the products' life cycle, to be sent to the retailer's warehouse. Finally, markdown is another tool adopted by retailers to accelerate the offloading of slow-moving products. In this dissertation, we study these three problems motivated by the logistics operations of a fast-fashion retailer in Turkey. Similar to any other real-world originated fashion supply chain problem, business rules have to be considered. First, we study the transshipment problem and consider specific operational restrictions. Second, we extend the transshipment problem by considering markdown and return decisions respecting the business rules. Next, we consider the effect of price on the sales of products. We proposed Simulated Annealing metaheuristic, a Lagrangian relaxation-based, and a Benders decomposition-based heuristics to solve these problems efficiently.