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ALBEY, Erinç

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Erinç

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Now showing 1 - 10 of 24
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    Optimal pricing and inventory strategies for fashion products under time-dependent interest rate and demand
    (Elsevier, 2021-04) Akan, M.; Albey, Erinç; Güler, M. G.; Industrial Engineering; ALBEY, Erinç
    In this work we consider the dynamic pricing problem of a retailer operating in a market with a single fashion item and under time-dependent interest rate. The demand is assumed to be deterministic and dependent on the price and decay with time, i.e., the market shrinks throughout the horizon. Using an optimal-control-theoretic approach, we analytically derive the optimal pricing and inventory strategy for the retailer over a finite horizon setting. We further analyze the ramifications of the optimal pricing decision for different initial inventory levels dictated by the relationship between the supplier and the retailer; and for varying market interest rates. Optimal dynamic pricing policy is a continuous function, which is almost impossible to use in practice. This is handled using approximate piece-wise constant pricing policies. The trade-off between dynamic pricing policy and approximate policies is also investigated.
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    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, Taha
    It 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.
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    A data-driven matching algorithm for ride pooling problem
    (Elsevier, 2022-04) Şahin, Ahmet; Sevim, İ.; Albey, Erinç; Güler, M. G.; Industrial Engineering; ALBEY, Erinç
    This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, since it aims to find a matching between drivers and riders. Proposed algorithm is a machine learning algorithm based on rank aggregation idea, where every feature in a multi-feature dataset provides a ranking of candidate drivers and weight for each feature is learned from past data through an optimization model. Once weight learning and candidate ranking problems are considered simultaneously, resulting optimization model becomes a nonlinear bilevel optimization model, which is reformulated as a single level mixed-integer nonlinear optimization model. To demonstrate the performance of the proposed algorithm, a real-life dataset from a mobile application of a ride pooling start-up company is used and company's current approach is considered as benchmark. Results reveal that proposed algorithm correctly predicts the first choice of riders 17% to 28% better compared to the benchmark in different scenarios. Similarly, proposed algorithm offers recommendation lists in which the preferred driver is ranked 0.38 to 1.12 person closer (to the rider's actual choice) compared to the benchmark.
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    Rounding heuristics for multiple product dynamic lot-sizing in the presence of queueing behavior
    (Elsevier, 2018-12) Kang, Y.; Albey, Erinç; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç
    We present heuristics for solving a difficult nonlinear integer programming (NIP) model arising from a multi-item single machine dynamic lot-sizing problem. The heuristic obtains a local optimum for the continuous relaxation of the NIP model and rounds the resulting fractional solution to a feasible integer solution by solving a series of shortest path problems. We also implement two benchmarks: a version of the well-known Feasibility Pump heuristic and the Surrogate Method developed for stochastic discrete optimization problems. Computational experiments reveal that our shortest path based rounding procedure finds better production plans than the previously developed myopic heuristic and the benchmarks.
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    Economic lot sizing problem with inventory dependent demand
    (Springer Nature, 2020-11) Önal, Mehmet; Albey, Erinç; Industrial Engineering; ÖNAL, Mehmet; ALBEY, Erinç
    We consider an economic lot sizing problem where the demand in a period is a piecewise linear and concave function of the amount of the available inventory after production in that period. We show that the problem isNPhard even when the production capacities are time invariant, and propose a polynomial time algorithm to the case where there are no capacity restrictions on production.
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    Economic lot sizing problem with tank scheduling
    (Elsevier, 2023-07-01) Önal, Mehmet; van den Heuvel, W.; Dereli, Meryem Merve; Albey, Erinç; Industrial Engineering; ÖNAL, Mehmet; ALBEY, Erinç; Dereli, Meryem Merve
    We introduce a multiple-item economic lot sizing problem where items are produced through the fermentation of some raw materials. Fermentation takes place in specialized tanks that have finite capacities, and duration of the fermentation process is item dependent. When fermentation starts, the tanks are not available for the duration of the fermentation process. We analyze the complexity of this problem under various assumptions on the number of items and tanks. In particular, we show that several cases of the problem are (strongly) NP-hard, and we propose polynomial time algorithms to some single item cases. In addition, we propose a quick and simple heuristic approach for one of the multiple item cases.
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    A robust optimization approach for production planning under exogenous planned lead times
    (IEEE, 2019) Albey, Erinç; Yanıkoğlu, İhsan; Uzsoy, R.; Industrial Engineering; ALBEY, Erinç; YANIKOĞLU, Ihsan
    Many production planning models applied in semiconductor manufacturing represent lead times as fixed exogenous parameters. However, in reality, lead times must be treated as realizations of released lots' cycle times, which are in fact random variables. In this paper, we present a distributionally robust release planning model that allows planned lead time probability estimates to vary over a specified ambiguity set. We evaluate the performance of non-robust and robust approaches using a simulation model of a scaled-down wafer fabrication facility. We examine the effect of increasing uncertainty in the estimated lead time parameters on the objective function value and compare the worst-case, average optimality, and feasibility of the two approaches. The numerical results show that the average objective function value of the robust solutions are better than that of the nominal solution by a margin of almost 20% in the scenario with the highest uncertainty level.
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    Çevik yöntemlerde cosmic i̇şlev puanı ve hikaye puanının birlikte kullanımı
    (CEUR-WS, 2017) Ertaban, C.; Gezgin, S.; Bağrıyanık, S.; Albey, Erinç; Karahoca, A.; Industrial Engineering; Turhan, Ç.; Coşkunçay, A.; Yazıcı, A.; Oğuztüzün, H.; ALBEY, Erinç
    Hikaye Puanı (SP: Story Point), Scrum ve Kanban gibi çevik yöntemlerde kullanılan en yaygın metriklerden birisidir. Subjektif bir metrik olsa da kullanışlı ve basit olması nedeniyle çevik ekiplerin birikim listelerinde bulunan kullanıcı hikayelerinin uygun bölümlere ayrılmasında, maliyet tahminlemesinde ve ekiplerin hız ve kapasitelerinin hesaplanmasında yaygın bir şekilde kullanılmaktadır. Cosmic işlev puanı (CFP: Cosmic Function Point) ise yazılım işlevsel kapsam büyüklüğünün ölçümünde kullanılan ve aynı zamanda bir ISO standardı da (ISO 19761) olan objektif bir metriktir. Bu çalışmada Türkiye’nin en büyük teknoloji ve iletişim hizmetleri sağlayıcı firmalarından birinin çevik yazılım geliştirme prensiplerine göre çalışırken hem Hikaye Puanı hem de CFP metriklerini birlikte kullanım deneyimleri paylaşılmış; iki metriğin benzerlikleri ve farklılıkları irdelenmiştir. Sonuç olarak SP metriğinin kapsam boyutlandırma toplantıları sırasında kullanıcı hikayelerinin çevik mantıkla uygun kapsam büyüklüğüne bölünmesinde daha etkin bir araç olduğu, CFP’nin ise çevik ekiplerin ürettiği çıktıların miktarının ve kalitesinin zaman içindeki trendinin ölçülmesinde ve yine çevik ortamlarda dış kaynak hak edişlerinin belirlenmesinde daha başarılı sonuçlar verdiği sonucuna varılmıştır. Ek olarak CFP’nin Efor tahminlemesinde kullanılıp kullanılamayacağı yönünde bir doğrusal regresyon modeli için ön analiz yapılmış ve ilk sonuçlar paylaşılmıştır.
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    International roaming traffic optimization with call quality
    (SciTePress, 2019) Şahin, Ahmet; Demirel, Kenan Cem; Albey, Erinç; Gürsun, Gonca; Industrial Engineering; ALBEY, Erinç; Şahin, Ahmet; Demirel, Kenan Cem; Gürsun, Gonca
    In this study we focus on a Steering International Roaming Traffic (SIRT) problem with single service that concerns a telecommunication’s operators’ agreements with other operators in order to enable subscribers access services, without interruption, when they are out of operators’ coverage area. In these agreements, a subscriber’s call from abroad is steered to partner operator. The decision for which each call will be forwarded to the partner is based on the user’s location (country/city), price of the partner operator for that location and the service quality of partner operator. We develop an optimization model that considers agreement constraints and quality requirements while satisfying subscribers demand over a predetermined time interval. We test the performance of the proposed approach using different execution policies such as running the model once and fixing the roaming decisions over the planning interval or dynamically updating the decisions using a rolling horizon approach. We present a rigorous trade off analysis that aims to help the decision maker in assessing the relative importance of cost, quality and ease of implementation. Our results show that steering cost is decreased by approximately 25% and operator mistakes are avoided with the developed optimization model while the quality of the steered calls is kept above the base quality level.
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    A CART-based genetic algorithm for constructing higher accuracy decision trees
    (SciTePress, 2020) Ersoy, Elif; Albey, Erinç; Kayış, Enis; Industrial Engineering; Hammoudi, S.; Quix, C.; Bernardino, J.; ALBEY, Erinç; KAYIŞ, Enis; Ersoy, Elif
    Decision trees are among the most popular classification methods due to ease of implementation and simple interpretation. In traditional methods like CART (classification and regression tree), ID4, C4.5; trees are constructed by myopic, greedy top-down induction strategy. In this strategy, the possible impact of future splits in the tree is not considered while determining each split in the tree. Therefore, the generated tree cannot be the optimal solution for the classification problem. In this paper, to improve the accuracy of the decision trees, we propose a genetic algorithm with a genuine chromosome structure. We also address the selection of the initial population by considering a blend of randomly generated solutions and solutions from traditional, greedy tree generation algorithms which is constructed for reduced problem instances. The performance of the proposed genetic algorithm is tested using different datasets, varying bounds on the depth of the resulting trees and using different initial population blends within the mentioned varieties. Results reveal that the performance of the proposed genetic algorithm is superior to that of CART in almost all datasets used in the analysis.