Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942

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A Lot-Sizing Problem in Deliberated and Controlled Co-Production Systems
(Taylor and Francis, 2021) Kabakulak, Banu; Ağralı, Semra; Taşkın, Z. Caner; Pamuk, Bahadır; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We consider an uncapacitated lot sizing problem in co-production systems, in which it is possible to produce multiple items simultaneously in a single production run. Each product has a deterministic demand to be satisfied on time. The decision is to choose which items to co-produce and the amount of production throughout a predetermined planning horizon. We show that the lot sizing problem with co-production is strongly NP-Hard. Then, we develop various mixed-integer linear programming (MILP) formulation of the problem and show that LP relaxations of all MILPs are equal. We develop a separation algorithm based on a set of valid inequalities, lower bounds based on a dynamic lot-sizing relaxation of our problem and a constructive heuristic that is used to obtain an initial solution for the solver, which form the basis of our proposed Branch & Cut algorithm for the problem. We test our models and algorithms on different data sets and provide the results.
Citation - WoS: 6
Citation - Scopus: 6
A Strong Integer Programming Formulation for Hybrid Flowshop Scheduling
(Taylor & Francis, 2019) Ağralı, Semra; Ünal, A. Tamer; Taşkın, Z. Caner; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We consider a hybrid flowshop scheduling problem that includes parallel unrelated discrete machines or batch processing machines in different stages of a production system. The problem is motivated by a bottleneck process within the production system of a transformer producer located in the Netherlands. We develop an integer programming model that minimises the total tardiness of jobs over a finite planning horizon. Our model is applicable to a wide range of production systems organised as hybrid flowshops. We strengthen our integer program by exploiting the special properties of some constraints in our formulation. We develop a decision support system (DSS) based on our proposed optimisation model. We compare the results of our initial optimisation model with an improved formulation as well as with a heuristic that was in use at the company before the implementation of our DSS. Our results show that the improved optimisation model significantly outperforms the heuristic and the initial optimisation model in terms of both the solution time and the strength of its linear programming relaxation.
Citation - WoS: 10
Citation - Scopus: 16
Minimizing the Misinformation Spread in Social Networks
(Taylor and Francis, 2019) Güney, Evren; Kuban, İ. Kuban Altınel; Tanınmış, Kübra; Aras, Necati; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The Influence Maximization Problem has been widely studied in recent years, due to rich application areas including marketing. It involves finding k nodes to trigger a spread such that the expected number of influenced nodes is maximized. The problem we address in this study is an extension of the reverse influence maximization problem, i.e., misinformation minimization problem where two players make decisions sequentially in the form of a Stackelberg game. The first player aims to minimize the spread of misinformation whereas the second player aims its maximization. Two algorithms, one greedy heuristic and one matheuristic, are proposed for the first player’s problem. In both of them, the second player’s problem is approximated by Sample Average Approximation, a well-known method for solving two-stage stochastic programming problems, that is augmented with a state-of-the-art algorithm developed for the influence maximization problem.
Citation - WoS: 6
Citation - Scopus: 11
Mixcycle: Unsupervised Speech Separation Via Cyclic Mixture Permutation Invariant Training
(IEEE, 2022) Karamatlı, Ertuğ; Kırbız, Serap; 02.05. Department of Electrical and Electronics Engineering; 02. Faculty of Engineering; 01. MEF University
We introduce two unsupervised source separation methods, which involve self-supervised training from single-channel two-source speech mixtures. Our first method, mixture permutation invariant training (MixPIT), enables learning a neural network model which separates the underlying sources via a challenging proxy task without supervision from the reference sources. Our second method, cyclic mixture permutation invariant training (MixCycle), uses MixPIT as a building block in a cyclic fashion for continuous learning. MixCycle gradually converts the problem from separating mixtures of mixtures into separating single mixtures. We compare our methods to common supervised and unsupervised baselines: permutation invariant training with dynamic mixing (PIT-DM) and mixture invariant training (MixIT). We show that MixCycle outperforms MixIT and reaches a performance level very close to the supervised baseline (PIT-DM) while circumventing the over-separation issue of MixIT. Also, we propose a self-evaluation technique inspired by MixCycle that estimates model performance without utilizing any reference sources. We show that it yields results consistent with an evaluation on reference sources (LibriMix) and also with an informal listening test conducted on a real-life mixtures dataset (REAL-M).
Citation - WoS: 6
Citation - Scopus: 5
Optimal Keyword Bidding in Search-Based Advertising With Budget Constraint and Stochastic Ad Position
(Taylor & Francis, 2019) Özlük, Özgür; Selçuk, Barış; Küçükaydın, Hande; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
This paper analyses the search-based advertising problem from an advertiser’s view point, and proposes optimal bid prices for a set of keywords targeted for the advertising campaign. The advertiser aims to maximise its expected potential revenue given a total budget constraint from a search-based advertising campaign. Optimal bid prices are formulated by considering various characteristics of the keywords such that the expected revenue from a keyword is a function of the ad’s position on the search page, and the ad position is a stochastic function of both the bid price and the competitive landscape for that keyword. We explore this problem analytically and numerically in an effort to generate important managerial insights for campaign setters.
Citation - WoS: 30
Citation - Scopus: 36
Prioritization of Public Services for Digitalization Using Fuzzy Z-Ahp and Fuzzy Z-Waspas
(Springer, 2021) Ucal Sarı, İrem; Sergi, Duygu; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.
Qubo Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem
(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Guney, Evren; Ehrenthal, Joachim; Hanne, Thomas; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.
Citation - WoS: 5
Citation - Scopus: 5
Risk Averse Investment Strategies for a Private Electricity Generating Company in a Carbon Constrained Environment
(Taylor & Francis, 2019) Çanakoğlu, Ethem; Ağralı, Semra; Adıyeke, Esra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We study a private electricity generating company that plans to enter a partially regulated market that operates under an active cap and trade system. There are different types of thermal and renewable power plants that the company considers to invest in over a predetermined planning horizon. Thermal power plants may include a carbon capture and storage technology in order to comply with the carbon limitations. We develop a time-consistent multi-stage stochastic optimization model for this investment problem, where the objective is to minimize the conditional value at risk (CV@R) of the net present value of the profit obtained through the planning horizon. We implement the model for a hypothetical generating company located in Turkey. The results show that the developed model is appropriate for determining risk averse investment strategies for a company that operates under carbon restricted market conditions.

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