Endüstri Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/279

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Department: search.filters.department.Çankaya UniversityPublisher: search.filters.publisher.Springer Heidelberg

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Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 5
    Citation - Scopus: 3
    Optimum Bidding Strategy for Wind and Solar Power Plants in Day-Ahead Electricity Market
    (Springer Heidelberg, 2021) Keysan, Ozan; Satir, Benhur; Ozcan, Mehmet
    There are two possible strategies for wind power plants (WPPs) and solar power plants (SPPs) to maximize their income in day ahead markets (DAM) in the presence of imbalance cost: joint bidding (JB) via collaboration by participating to balancing groups and deployment of storage technologies. There are limited studies in the literature covering the comparative analysis of "JB strategy" with "battery deployment (BD) strategy". In the existence of balancing responsibility, the comparative analysis of these strategies is the main contribution of this study to the literature. Our Second contribution is the analysis of the impact of different regulatory regimes, which are set by the regulatory authority, on total income. JBM (joint bidding model), which is the model for joint bidding via different collaboration groups, is developed for the analysis of JB strategy, BDM (battery deployment model), which is the model covering the deployment of storage technology, is developed for the analysis of BD strategy. The impact of each strategy on total income is analyzed. According to the analysis of the results of the models, while JB strategy, which is sensitive to the regulatory regime, increases the total annual income of the collaboration groups up to 0.65%, BD strategy seems not feasible and financially viable. On the other hand, extra income values per MW of battery for SPP is between $218 and $400 /MW-year, while these values are between $2460 and $6795/MW-year for the group of 15 WPPs. Therefore, deployment of battery for WPPs creates extra income more than tenfold of that of SPP. BD strategy can be viable provided that the levelized cost of deployment of battery drops below the extra income values achieved per MW of battery.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Intermodal Humanitarian Logistics Using Unit Load Devices
    (Springer Heidelberg, 2022) Kavlak, Hasan; Ertem, Mustafa Alp; Satir, Benhur
    Intermodal freight transportation facilitates today's global trade. The benefits of intermodal freight transportation have been studied and are more observable in commercial logistics; however, the potential benefits of humanitarian logistics have not been thoroughly investigated. This research aims to present a resilient transportation framework by modeling intermodal transportation utilizing interoperable loading devices during disaster responses. We developed an integer programming model based on a time-space network by considering route and vehicle availabilities that are allowed to change with time. We consider vehicles with varying capacities in three transportation modes (i.e., ground, maritime, and air). The contribution of this study is threefold: (1) Two compatible unit load devices are proposed for humanitarian logistics; (2) a mathematical model that includes integer variable representation for vehicle fleets in different transportation modes is developed; and (3) intermodal transportation is compared with single-mode transportation using a real-life dataset. Our main results are as follows: In terms of cost, intermodal transportation is effective when demand occurs in consecutive periods and response time is short. Inventory is held more in intermodal transportation when it is cost-effective to use transportation modes with large capacities. Thus, the benefits of the responsiveness of intermodal transportation outweigh the costs of mode interchange and inventory holding for sudden-onset disasters where quick responses are needed within a short time.
  • Article
    Citation - WoS: 24
    Citation - Scopus: 27
    Bi-Objective Adaptive Large Neighborhood Search Algorithm for the Healthcare Waste Periodic Location Inventory Routing Problem
    (Springer Heidelberg, 2022) Aydemir-Karadag, Ayyuce
    There has been an unexpected increase in the amount of healthcare waste during the COVID-19 pandemic. Managing healthcare waste is vital, as improper practices in the waste system can lead to the further spread of the virus. To develop effective and sustainable waste management systems, decisions in all processes from the source of the waste to its disposal should be evaluated together. Strategic decisions involve locating waste processing centers, while operational decisions deal with waste collection. Although the periodic collection of waste is used in practice, it has not been studied in the relevant literature. This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. Due to the complexity of the problem, a two-step approach is proposed. The first stage provides a mixed-integer linear model that generates visiting schedules to source nodes. The second stage offers a Bi-Objective Adaptive Large Neighborhood Search Algorithm (BOALNS) that processes the remaining decisions considered in the problem. The performance of the algorithm is tested on several hypothetical problem instances. Computational analyses are conducted by comparing BOALNS with its other two versions, Adaptive Large Neighborhood Search Algorithm and Bi-Objective Large Neighborhood Search Algorithm (BOLNS). The computational experiments demonstrate that our proposed algorithm is superior to these algorithms in several performance evaluation metrics. Also, it is observed that the adaptive search engine increases the capability of BOALNS to achieve high-quality Pareto-optimal solutions.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 5
    A General Production and Financial Planning Model: Case of a Poultry Integration
    (Springer Heidelberg, 2020) Satir, Benhur; Yildirim, Gonca
    We propose a general linear programming formulation for a poultry integration to facilitate decision making in production and financial planning. The formulation is motivated by a medium-size application and by involving practitioners from the industry. It is a realistic and strategic model since we incorporate all divisions in a complex poultry integration, (including, but not limited to, feed mill, breeder coops, incubation house, broiler coops, slaughterhouse and distribution centers) as well as the interrelations among these divisions. The horizon we consider is in years, which makes the plan a strategic level plan in this fast-paced industry. Through extensive experimentation with various end-customer demand scenarios, we found out that the quantity of breeder chicks to buy at certain times during the planning horizon is the robust key decision variable in the overall system.