Person: ÖZENER, Okan Örsan
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Okan Örsan
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ÖZENER
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ArticlePublication Metadata only 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, MiladIn 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.ArticlePublication Metadata only Cyclic delivery schedules for an inventory routing problem(Informs, 2015-11) Ekici, Ali; Özener, Okan Örsan; Kuyzu, G.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan ÖrsanWe 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.ArticlePublication Metadata only 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, MiladManaging 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.ArticlePublication Metadata only 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 ÖrsanIn 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.ArticlePublication Metadata only 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, MiladIn 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.ArticlePublication Metadata only Allocating cost of service to customers in inventory routing(Informs, 2013) Özener, Okan Örsan; Ergun, Ö.; Savelsbergh, M.; Industrial Engineering; ÖZENER, Okan ÖrsanVendor-managed inventory VMI replenishment is a collaboration between a supplier and its customers, where the supplier is responsible for managing the customers' inventory levels. In the VMI setting we consider, the supplier exploits synergies between customers, e.g., their locations, usage rates, and storage capacities, to reduce distribution costs. Due to the intricate interactions between customers, calculating a fair cost-to-serve for each customer is a daunting task. However, cost-to-serve information is useful when marketing to new customers or when revisiting routing and delivery quantity decisions. We design mechanisms for this cost allocation problem and determine their characteristics both analytically and computationally.ArticlePublication Metadata only 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, MiladOne 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.ArticlePublication Metadata only An effective formulation of the multi-criteria test suite minimization problem(Elsevier, 2020-10) Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; ÖZENER, Okan Örsan; SÖZER, HasanTest suite minimization problem has been mainly addressed by employing heuristic techniques or integer linear programming focusing on a specific criterion or bi-criteria. These approaches fall short to compute optimal solutions especially when there exists overlap among test cases in terms of various criteria such as code coverage and the set of detected faults. Nonlinear formulations have also been proposed recently to address such cases. However, these formulations require significantly more computational resources compared to linear ones. Moreover, they are also subject to shortcomings that might still lead to sub-optimal solutions. In this paper, we identify such shortcomings and we propose an alternative formulation of the problem. We have empirically evaluated the effectiveness of our approach based on a publicly available dataset and compared it with respect to the state-of-the-art based on the same objective function and the same set of criteria including statement coverage, fault-revealing capability, and test execution time. Results show that our formulation leads to either better results or the same results, when the previously obtained results were already the optimal ones. In addition, our formulation is a linear formulation, which can be solved much more efficiently compared to non-linear formulations.ArticlePublication Metadata only Lane-exchange mechanisms for truckload carrier collaboration(Informs, 2011-02) Özener, Okan Örsan; Ergun, Ö.; Savelsbergh, M.; Industrial Engineering; ÖZENER, Okan ÖrsanBecause of historically high fuel prices, the trucking industry's operating expenses are higher than ever and thus profit margins are lower than ever. To cut costs, the trucking industry is searching for and exploring new ideas. We investigate the potential of collaborative opportunities in truckload transportation. When carriers serve transportation requests from many shippers, they may be able to reduce their repositioning costs by exchanging one or more of them. We develop optimization models to determine the maximum benefit that can be derived from collaborating. We also develop various exchange mechanisms which differ in terms of information sharing requirements and side payment options that allow carriers to realize some or all of the costs savings opportunities.ArticlePublication Metadata only Neural network estimators for optimal tour lengths of traveling salesperson problem instances with arbitrary node distributions(Informs, 2024) Varol, Taha; Özener, Okan Örsan; Albey, Erinç; Industrial Engineering; ÖZENER, Okan Örsan; ALBEY, Erinç; Varol, TahaIt is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour lengths of potential clusters as a preliminary step, transforming the two-phase approach into a less myopic solution framework. We introduce quick and highly accurate Traveling Salesperson Problem (TSP) tour length estimators based on neural networks (NNs) to facilitate this. Our approach combines the power of NNs and theoretical knowledge in the routing domain, utilizing a novel feature set that includes node-level, instance-level, and solution-level features. This hybridization of data and domain knowledge allows us to achieve predictions with an average deviation of less than 0.7% from optimality. Unlike previous studies, we design and employ new instances replicating real-life logistics networks and morphologies. These instances possess characteristics that introduce significant computational costs, making them more challenging. To address these challenges, we develop a novel and efficient method for obtaining lower bounds and partial solutions to the TSP, which are subsequently utilized as solution-level predictors. Our computational study demonstrates a prediction error up to six times lower than the best machine learning (ML) methods on their training instances and up to 100 times lower prediction error on out-of-distribution test instances. Furthermore, we integrate our proposed ML models with metaheuristics to create an enumeration-like solution framework, enabling the improved solution of massive scale routing problems. In terms of solution time and quality, our approach significantly outperforms the state-of-the-art solver, demonstrating the potential of our features, models, and the proposed method.