Endüstri Mühendisliği Bölümü Koleksiyonu

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

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Citation - WoS: 18
Citation - Scopus: 22
A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
(2019) Jabali, Ola; Gendreau, Michel; Jans, Raf; Taş, Duygu; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning
(Informs, 2023) van den Heuvel, Wilco; Ağralı, Semra; Taşkın, Z. Caner; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We study an integrated market selection and production planning problem. There is a set of markets with deterministic demand, and each market has a certain revenue that is obtained if the market's demand is satisfied throughout a planning horizon. The demand is satisfied with a production scheme that has a lot-sizing structure. The problem is to decide on which markets' demand to satisfy and plan the production simultaneously. We consider both single and multiobjective settings. The single objective problem maximizes the profit, whereas the multiobjective problem includes the maximization of the revenue and the minimization of the production cost objectives. We develop a decomposition-based exact solution algorithm for the single objective setting and show how it can be used in a proposed three-phase algorithm for the multiobjective setting. The master problem chooses a subset of markets, and the subproblem calculates an optimal production plan to satisfy the selected markets' demand. We investigate the subproblem from a cooperative game theory perspective to devise cuts and strengthen them based on lifting. We also propose a set of valid inequalities and preprocessing rules to improve the proposed algorithm. We test the efficacy of our solution method over a suite of problem instances and show that our algorithm substantially decreases solution times for all problem instances.
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: 25
Citation - Scopus: 25
An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks
(Elsevier, 2019) Güney, Evren; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.
An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)
(Elsevier, 2020) Güney, Evren; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program thatapproximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently,we propose a linear programming relaxation based method with a provable worst casebound that converges to the current state-of-the-art 1 − 1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in termsof solution quality and this is one of the few studies that provides approximate optimalsolutions for certain real life social networks.
Citation - WoS: 46
Citation - Scopus: 53
An Optimization Model for Carbon Capture & Storage/Utilization Vs. Carbon Trading: a Case Study of Fossil-Fired Power Plants in Turkey
(2018) Uctug, Fehmi Görkem; Ağralı, Semra; Türkmen, Burçin Atılgan; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We consider fossil-fired power plants that operate in an environment where a cap and trade system is in operation. These plants need to choose between carbon capture and storage (CCS), carbon capture and utilization (CCU), or carbon trading in order to obey emissions limits enforced by the government. We develop a mixed-integer programming model that decides on the capacities of carbon capture units, if it is optimal to install them, the transportation network that needs to be built for transporting the carbon captured, and the locations of storage sites, if they are decided to be built. Main restrictions on the system are the minimum and maximum capacities of the different parts of the pipeline network, the amount of carbon that can be sold to companies for utilization, and the capacities on the storage sites. Under these restrictions, the model aims to minimize the net present value of the sum of the costs associated with installation and operation of the carbon capture unit and the transportation of carbon, the storage cost in case of CCS, the cost (or revenue) that results from the emissions trading system, and finally the negative revenue of selling the carbon to other entities for utilization. We implement the model on General Algebraic Modeling System (GAMS) by using data associated with two coal-fired power plants located in different regions of Turkey. We choose enhanced oil recovery (EOR) as the process in which carbon would be utilized. The results show that CCU is preferable to CCS as long as there is sufficient demand in the EOR market. The distance between the location of emission and location of utilization/storage, and the capacity limits on the pipes are an important factor in deciding between carbon capture and carbon trading. At carbon prices over $15/ton, carbon capture becomes preferable to carbon trading. These results show that as far as Turkey is concerned, CCU should be prioritized as a means of reducing nationwide carbon emissions in an environmentally and economically rewarding manner. The model developed in this study is generic, and it can be applied to any industry at any location, as long as the required inputs are available. (C) 2018 Elsevier Ltd. All rights reserved.
Analysis of a New Business Model To Fundraise Non-Governmental Organizations Using Fuzzy Cognitive Maps
(IOS Press, 2020) Aytore, Can; Sergi, Duygu; Ucal Sari, Irema; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Fundraising is one of the most critical issues for non-governmental organizations (NGOs) to carry out their projects. In this paper, a search engine project which aims to find additional financial sources and increase donations for NGOs is proposed. The proposed search engine project is analyzed using fuzzy cognitive maps (FCMs) to define and manage factor influences on the success of the project. FCMs are useful tools to define long term effects of important factors for a system. First casual relations of the factors are determined and then using sigmoid function for learning algorithm, the equilibrium state for the system is obtained. It is found that the factors generating monetary values are the most important ones for the project to be successful in long term.
Citation - WoS: 44
Citation - Scopus: 40
Branch-And Methods for the Electric Vehicle Routing Problem With Time Windows
(Taylor and Francis, 2021) Çatay, Bülent; Duman, Ece Naz; Taş, Duygu; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, we address the electric vehicle routing problem with time windows and propose two branch-and-price-and-cut methods based on a column generation algorithm. One is an exact algorithm whereas the other is a heuristic method. The pricing sub-problem of the column generation method is solved using a label correcting algorithm. The algorithms are strengthened with the state-of-the-art acceleration techniques and a set of valid inequalities. The acceleration techniques include: (i) an intermediate column pool to prevent solving the pricing sub-problem at each iteration, (ii) a label correcting method employing the ng-route algorithm adopted to our problem, (iii) a bidirectional search mechanism in which both forward and backward labels are created, (iv) a procedure for dynamically eliminating arcs that connect customers to remote stations from the network during the path generation, (v) a bounding procedure providing early elimination of sub-optimal routes, and (vi) an integer programming model that generates upper bounds. Numerical experiments are conducted using a benchmark data set to compare the performances of the algorithms. The results favour the heuristic algorithm in terms of both the computational time and the number of instances solved. Moreover, the heuristic algorithm is shown to be specifically effective for larger instances. Both algorithms introduce a number of new solutions to the literature.
Citation - WoS: 52
Citation - Scopus: 58
Carbon Price Forecasting Models Based on Big Data Analytics
(Taylor and Francis Ltd., 2019) Çanakoğlu, Ethem; Ağralı, Semra; Yahşi, Mustafa; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
After the establishment of the European Union's Emissions Trading System (EU-ETS) carbon pricing attracted many researchers. This paper aims to develop a prediction model that anticipates future carbon prices given a real-world data set. We treat the carbon pricing issue as part of big data analytics to achieve this goal. We apply three fundamental methodologies to characterize the carbon price. First method is the artificial neural network, which mimics the principle of human brain to process relevant data. As a second approach, we apply the decision tree algorithm. This algorithm is structured through making multiple binary decisions, and it is mostly used for classification. We employ two different decision tree algorithms, namely traditional and conditional, to determine the type of decision tree that gives better results in terms of prediction. Finally, we exploit the random forest, which is a more complex algorithm compared to the decision tree. Similar to the decision tree, we test both traditional and conditional random forest algorithms to analyze their performances. We use Brent crude futures, coal, electricity and natural gas prices, and DAX and S&P Clean Energy Index as explanatory variables. We analyze the variables' effects on carbon price forecasting. According to our results, S&P Clean Energy Index is the most influential variable in explaining the changes in carbon price, followed by DAX Index and coal price. Moreover, we conclude that the traditional random forest is the best algorithm based on all indicators. We provide the details of these methods and their comparisons.
Citation - WoS: 3
Citation - Scopus: 5
Consumer Loans' First Payment Default Detection: a Predictive Model
(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL, 2020) Sevgili, Türkan; Koç, Utku; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
A default loan (also called nonperforming loan) occurs when there is a failure to meet bank conditions and repayment cannot be made in accordance with the terms of the loan which has reached its maturity. In this study, we provide a predictive analysis of the consumer behavior concerning a loan’s first payment default (FPD) using a real dataset of consumer loans with approximately 600,000 records from a bank. We use logistic regression, naive Bayes, support vector machine, and random forest on oversampled and undersampled data to build eight different models to predict FPD loans. A two-class random forest using undersampling yielded more than 86% on all performance measures: accuracy, precision, recall, and F1-score. The corresponding scores are even as high as 96% for oversampling. However, when tested on the real and balanced dataset, the performance of oversampling deteriorates as generating synthetic data for an extremely imbalanced dataset harms the training procedure of the algorithms. The study also provides an understanding of the reasons for nonperforming loans and helps to manage credit risks more consciously.
Citation - WoS: 10
Citation - Scopus: 11
Coordination of Inbound and Outbound Transportation Schedules With the Production Schedule
(2016) Toptal, Aysegul; Sabuncuoglu, Ihsan; Koç, Utku; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
This paper studies the coordination of production and shipment schedules for a single stage in the supply chain. The production scheduling problem at the facility is modeled as belonging to a single process. Jobs that are located at a distant origin are carried to this facility making use of a finite number of capacitated vehicles. These vehicles, which are initially stationed close to the origin, are also used for the return of the jobs upon completion of their processing. In the paper, a model is developed to find the schedules of the facility and the vehicles jointly, allowing for effective utilization of the vehicles both in the inbound and the outbound. The objective of the proposed model is to minimize the sum of transportation costs and inventory holding costs. Issues related to transportation such as travel times, vehicle capacities, and waiting limits are explicitly accounted for. Inventories of the unprocessed and processed jobs at the facility are penalized. The paper contributes to the literature on supply chain scheduling under transportation considerations by modeling a practically motivated problem, proving that it is strongly NP-Hard, and developing an analytical and a numerical investigation for its solution. In particular, properties of the solution space are explored, lower bounds are developed on the optimal costs of the general and the special cases, and a computationally-efficient heuristic is proposed for solving large-size instances. The qualities of the heuristic and the lower bounds are demonstrated over an extensive numerical analysis. (C) 2016 Elsevier Ltd. All rights reserved.
Determining the Most Vulnerable Components in a Transportatıon Network
(Yıldız Technical University, 2018) Küçükaydın, Hande; Aras, Necati; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Transportation networks belong to the class of critical infrastructure networks since a small deterioration in the service provision has the potential to cause considerable negative consequences on everyday activities. Among the reasons for the deterioration we can mention the shutdown of a subway station, the closure of one or more lanes on a bridge, the operation of an airport at a much reduced capacity. In order to measure the vulnerability of transportation network, it is necessary to determine the maximum possible disruption by assuming that there is an intelligent attacker wishing to give damage to the components of the network including the stations/stops and linkages. Identifying the worst disruptions can be realized by using interdiction models that are formulated by a bilevel mathematical programming model involving two decision makers: leader and follower. In this paper, we develop such a model referred to as attacker-operator model, where the leader is a virtual attacker who wants to cause the maximum possible disruption in the transportation network by minimizing the amount of flow among the nodes of the network, while the follower is the system operator who tries to reorganize the flow in the most effective way by maximizing the flow after the disruption. The benefit of such a model to the system operator is to determine the most vulnerable stations and linkages in the transportation network on one hand, and to take precautions in preventing the negative effects of the disruption on the other hand.
Citation - WoS: 41
Citation - Scopus: 36
Electric Vehicle Routing With Flexible Time Windows: a Column Generation Solution Approach
(Taylor & Francis, 2020) Taş, Duygu; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, we introduce the Electric Vehicle Routing Problem with Flexible Time Windows (EVRPFTW) in which vehicles are allowed to serve customers before and after the earliest and latest time window bounds, respectively. The objective of this problem is to assign electric vehicles to feasible routes and make schedules with minimum total cost that includes the traveling costs, the costs of using electric vehicles and the penalty costs incurred for earliness and lateness. The proposed mathematical model is solved by a column generation procedure. To generate an integer solution, we solve an integer programming problem using the routes constructed by the column generation algorithm. We further develop a linear programming model to compute the optimal times to start service at each customer for the selected routes. A number of wellknown benchmark instances is solved by our solution procedure to evaluate the operational gains obtained by employing flexible time windows.
Citation - WoS: 10
Citation - Scopus: 13
Energy Investment Planning at a Private Company: a Mathematical Programming-Based Model and Its Application in Turkey
(2017) Ağralı, Semra; Canakoglu, Ethem; Arikan, Yildiz; Terzi, Fulya; Adıyeke, Esra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some of these plants may include a carbon capture and storage technology. We develop a stochastic mixed-integer linear program for this problem where the objective is to maximize the expected net present value of the profit obtained. We include restrictions on the maximum and minimum possible amount of investment for every type of investment option. We also enforce market share conditions such that the percentage of the total investments of the company over the total installed capacity of the country stay between upper and lower bounds. Moreover, in order to distribute the investment risk, the percentage of each type of power plant investment is restricted by some upper bound. We tested the model for a hypothetical company operating in Turkey. The results show that the model is suitable to be used for determining the investment strategy of the company.
Citation - WoS: 1
Citation - Scopus: 1
Evaluation of Learning Management Systems Using Interval Valued Intuitionistic Fuzzy-Z Numbers
(Anadolu Üniversitesi, 2023) Ucal Sarı, İrem; Sergi, Duygu; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The use of online education tools has increased rapidly with the transition to distance education caused by the pandemic. The obligation to carry out all activities of face-to-face education online made it very important for the tools used in distance education to meet the increasing needs. In line with these needs, radical changes have occurred in the learning management systems used in distance education. Therefore, in this study, it is aimed to determine the features that the systems used in distance education should have and to compare the existing systems according to these features. For this purpose, a novel fuzzy extension, interval valued intuitionistic fuzzy Z-numbers, is defined for modeling uncertainty, and AHP and WASPAS methods using proposed fuzzy numbers are developed to determine the importance of decision criteria and compare alternatives.
Citation - WoS: 49
Citation - Scopus: 52
Extension of Capital Budgeting Techniques Using Interval-Valued Fermatean Fuzzy Sets
(IOS Press, 2022) Sergi, Duygu; Sarı, İrem Uçal; Senapati, Tapan; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFS s) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques.
Citation - WoS: 5
Citation - Scopus: 6
Generation of Feasible Integer Solutions on a Massively Parallel Computer Using the Feasibility Pump
(2017) Mehrotra, Sanjay; Koç, Utku; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
We present an approach to parallelize generation of feasible mixed integer solutions of mixed integer linear programs in distributed memory high performance computing environments. This approach combines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FP instances with different starting solutions concurrently, while allowing them to share information. Our computational results suggest that the improvement resulting from parallelization using our approach is statistically significant. (C) 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 16
Gradual Covering Location Problem With Multi-Type Facilities Considering Customer Preferences
(Elsevier, 2020) Küçükaydın, Hande; Aras, Necati; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers at a given demand zone patronize different types of facilities is equal to one. However, among the same type of facilities they choose the closest facility, and the strength at which this facility covers the customer is based on two distances referred to as full coverage distance and gradual (partial) coverage distance. If the distance between the customer location and the closest facility is smaller (larger) than the full (partial) coverage distance, this customer is fully (not) covered, whereas for all distance values between full and partial coverage, the customer is partially covered. Both distance values depend on both the customer attributes and the type of the facility. Furthermore, facilities can only be opened if their revenue exceeds a certain threshold value. A final restriction is incorporated into the model by defining a minimum separation distance between the same facility types. This restriction is also extended to the case where a minimum threshold distance exists among facilities of different types. The objective of the retailer is to find the optimal locations and types of the new facilities in order to maximize its profit. Two versions of the problem are formulated using integer linear programming, which differ according to whether the minimum separation distance applies to the same facility type or different facility types. The resulting integer linear programming models are solved by three approaches: commercial solver CPLEX, heuristics based on Lagrangean relaxation, and local search implemented with 1-Add and 1-Swap moves. Apart from experimentally assessing the accuracy and the efficiency of the solution methods on a set of randomly generated test instances, we also carry out sensitivity analysis using a real-world problem instance.
Citation - WoS: 2
Citation - Scopus: 5
Increasing Procurement Efficiency Through Optimal E-Commerce Enablement Scheduling
(Emerald Group Publishing Ltd., 2019) Özlük, Özgür; Cholette, Susan; Clark, Andrew G; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Purpose: This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings. Design/methodology/approach: This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints. Findings: The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions. Originality/value: The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period; additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.

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