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EKİCİ, Ali

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Now showing 1 - 10 of 27
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    Cyclic delivery schedules for an inventory routing problem
    (Informs, 2015-11) Ekici, Ali; Özener, Okan Örsan; Kuyzu, G.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan
    We consider an inventory routing problem where a common vendor is responsible for replenishing the inventories of several customers over a perpetual time horizon. The objective of the vendor is to minimize the total cost of transportation of a single product from a single depot to a set of customers with deterministic and stationary consumption rates over a planning horizon while avoiding stock-outs at the customer locations. We focus on constructing a repeatable (cyclic) delivery schedule for the product delivery. We propose a novel algorithm, called the Iterative Clustering-Based Constructive Heuristic Algorithm, to solve the problem in two stages: (i) clustering, and (ii) delivery schedule generation. To test the performance of the proposed algorithm in terms of solution quality and computational efficiency, we perform a computational study on both randomly generated instances and real-life instances provided by an industrial gases manufacturer. We also compare the performance of the proposed algorithm against an algorithm developed for general routing problems.
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    Supplier selection and order allocation in the presence of suppliers with exact annual capacity
    (Inderscience Enterprises Ltd., 2021) Ekici, Ali; Özener, Okan Örsan; Elyasi, Milad; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; ELYASI, Milad
    In this paper, we focus on the supplier selection and order quantity allocation for a single retailer. The retailer orders a product from multiple suppliers with capacities, adds value to the product and fulfils the demand while meeting the minimum quality level. There is a distinct difference between our work and the prior works in the literature in that we assume the (annual) capacities of the suppliers to be exact annual capacities, i.e., the total order amount in a given calendar/fiscal year from a supplier must be less than or equal to its capacity. First, we discuss the implications of this exact annual capacity assumption on the ordering policy of the retailer. Next, to determine an ordering policy, we propose a heuristic algorithm using a novel idea of iteratively updating the annual ordering cost estimates. We demonstrate the efficacy of the proposed algorithm on randomly generated instances.
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    Conference paperPublicationMetadata only
    Integrated blood collection and appointment scheduling
    (Institute of Industrial Engineers, 2012) Mobasher, A.; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali
    Considering the processing requirements of donated blood, we study an integrated blood collection and appointment scheduling problem. We develop a mixed integer programming model to maximize the amount of donated blood that can be delivered to the processing center before spoilage as well as determine the schedule of donation appointments. Since the problem complexity and the computational time of MIP formulation is exponentially increasing by including more donation sites, we propose an insertion/saving heuristic algorithm to find a good feasible solution and develop a large neighborhood search method to improve the solution further.
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    A non-clustered approach to platelet collection routing problem
    (Elsevier, 2023-12) Talebi Khameneh, R.; Elyasi, Milad; Özener, Okan Örsan; Ekici, Ali; Industrial Engineering; ÖZENER, Okan Örsan; EKİCİ, Ali; Elyasi, Milad
    One of the blood components that can be extracted from whole blood is the platelet, which has a wide range of uses in medical fields. Due to the perishable nature of platelets, it is recommended that the separation occurs within six hours after the donation. Moreover, platelets constitute less than one percent of the whole blood volume, yet they are highly demanded. Given the importance of platelets in healthcare, their perishability, and their limited supply, an effective platelet supply chain leans on well-managed whole blood collection operations. In this study, we consider a blood collection problem (BCP) focusing on the collection of whole blood donations from the blood donation sites (BDSs). Different from the basic form of BCP, we consider the processing time limit (PTL) of blood and arbitrary donation patterns of donors as well as relaxing the assumption of assigning each blood collection vehicle (BCV) to a set of BDSs. Therefore, we define the non-clustered maximum blood collection problem (NCMBCP) as a variant of BCP. In this study, we examine routing decisions for platelet collections while relaxing the clustering requirement from the BDSs, which results in a significant increase in the complexity of the problem. In order to solve the problem, we propose a hybrid genetic algorithm (HGA) and an invasive weed optimization (IWO) algorithm that provide considerable improvements over the best solution in the literature for the clustered variant of the problem and outperform it (on average) by 8.68% and 8.16%, respectively.
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    Modeling influenza pandemic and planning food distribution
    (Informs, 2014) Ekici, Ali; Keskinocak, P.; Swann, J. L.; Industrial Engineering; EKİCİ, Ali
    Based on the recent incidents of H5N1, H1N1, and influenza pandemics in history (1918, 1957, and 1968) experts believe that a future influenza pandemic is inevitable and likely imminent. Although the severity of influenza pandemics vary, evidence suggests that an efficient and rapid response is crucial for mitigating morbidity, mortality, and costs to society. Hence, preparing for a potential influenza pandemic is a high priority of governments at all levels (local, state, federal), nongovernmental organizations (NGOs), and companies. In a severe pandemic, when a large number of people are ill, infected persons and their families may have difficulty purchasing and preparing meals. Various government agencies and NGOs plan to provide meals to these households. In this paper, in collaboration with the American Red Cross, we study food distribution planning during an influenza pandemic. We develop a disease spread model to estimate the spread pattern of the disease geographically and over time, combine it with a facility location and resource allocation network model for food distribution, and develop heuristics to find near-optimal solutions for large instances. We run our combined disease spread and facility location model for the state of Georgia and present the estimated number of infections and the number of meals needed in each census tract for a one-year period along with a design of the supply chain network. Moreover, we investigate the impact of voluntary quarantine on the food demand and the food distribution network and show that its effects on food distribution can be significant. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources and respond dynamically.
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    Book ChapterPublicationMetadata only
    Blood supply chain management and future research opportunities
    (Springer, 2018) Ekici, Ali; Özener, Okan Örsan; Göktürk, Elvin Çoban; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; GÖKTÜRK, Elvin Çoban
    In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.
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    A column generation-based approach for the adaptive stochastic blood donation tailoring problem
    (Taylor & Francis, 2023) Elyasi, Milad; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Dolgui, A.; Industrial Engineering; ÖZENER, Okan Örsan; YANIKOĞLU, Ihsan; EKİCİ, Ali; Elyasi, Milad
    Managing blood donations is a challenging problem due to the perishability of blood, limited donor pool, deferral time restrictions, and demand uncertainty. The problem addressed here combines two important aspects of blood supply chain management: the inventory control of blood products and the donation schedule. We propose a stochastic scenario-based reformulation of the blood donation management problem that adopts multicomponent apheresis and utilises donor pool segmentation into here-and-now and wait-and-see donors. We propose a flexible donation scheme that is resilient against demand uncertainty. This scheme enables more flexible donation schedules because wait-and-see donors may adjust their donation schedules according to the realised values of demand over time. We propose a column generation-based approach to solve the associated multi-stage stochastic donation tailoring problem. The numerical results show the effectiveness of the proposed optimisation model, which provides solutions with less than a 7% optimality gap on average with respect to a lower bound. It also improves the operational cost of the standard donation scheme that does not use wait-and-see donors by more than 18% on average. Utilising multicomponent apheresis and flexible wait-and-see donations are suggested for donation organisations because they yield significant cost reductions and resilient donation schedules.
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    Pricing decisions in a strategic single retailer/dual suppliers setting under order size constraints
    (Informa Group, 2016) Ekici, Ali; Özener, Başak Altan; Özener, Okan Örsan; Economics; Industrial Engineering; EKİCİ, Ali; ÖZENER, Başak Altan; ÖZENER, Okan Örsan
    In this paper, we study a duopolistic market of suppliers competing for the business of a retailer. The retailer sets the order cycle and quantities from each supplier to minimize its annual costs. Different from other studies in the literature, our work simultaneously considers the order size restriction and the benefit of order consolidation, and shows non-trivial pricing behaviour of the suppliers under different settings. Under asymmetric information setting, we formulate the pricing problem of the preferred supplier as a non-linear programming problem and use Karush–Kuhn–Tucker conditions to find the optimal solution. In general, unless the preferred supplier has high-order size limit, it prefers sharing the market with its competitor when retailer’s demand, benefit of order consolidation or fixed cost of ordering from the preferred supplier is high. We model the symmetric information setting as a two-agent non-zero sum pricing game and establish the equilibrium conditions. We show that a supplier might set a ‘threshold price’ to capture the entire market if its per unit fixed ordering cost is sufficiently small. Finally, we prove that there exists a joint-order Nash equilibrium only if the suppliers set identical prices low enough to make the retailer place full-size orders from both.
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    HYGAR: a hybrid genetic algorithm for software architecture recovery
    (ACM, 2022) Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah; Ekici, Ali; Sözer, Hasan; Industrial Engineering; Computer Science; EKİCİ, Ali; SÖZER, Hasan; Elyasi, Milad; Simitcioğlu, Muhammed Esad; Saydemir, Abdullah
    Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.
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    An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics
    (Elsevier, 2019-11) Ekici, Ali; Elyasi, Milad; Özener, Okan Örsan; Sarıkaya, M. B.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; Elyasi, Milad
    In this paper, we analyze a variant of the unrelated parallel machine scheduling problem with the objective of minimizing the total tardiness and earliness in the presence of sequence-dependent setups, unequal release times, machine-job compatibility restrictions and workload balance requirements. This study is motivated by the production scheduling operations at a television manufacturer, Vestel Electronics. Vestel produces LCD/LED TVs and has a significant market share in the consumer electronics sector in Europe. TV manufacturing is planned based on a make-to-order strategy, and Vestel uses 15 assembly lines to produce 110 different product groups and 3817 different models. Once the orders are received, production scheduling is performed at the beginning of each month, and the goal is to satisfy the demand on time as much as possible. The decision maker has to consider several factors including job-assembly line compatibility, the release and due dates of the jobs and a workload balance among different assembly lines when forming the production schedule. To address this problem, we propose a wide range of heuristics including (i) a sequential algorithm, (ii) a tabu search algorithm, (iii) a random set partitioning approach, and (iv) a novel matheuristic approach utilizing the local intensification and global diversification powers of a tabu search algorithm. Through a computational study, we observe that all the proposed approaches not only significantly outperform the current practice but also provide solutions with around 5% less optimality gap compared to a benchmark algorithm in the literature.