Endüstri Mühendisliği Bölümü Koleksiyonu

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

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A Capacitated Lot Sizing Problem With Stochastic Setup Times
(2015) Taş, Duygu
In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times (CLSP-SST).
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
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
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
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.
A Mathematical Programming-Based Approach for an Energy Investment Planning of a Private Company
(2017) Ağralı, Semra
...
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
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
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
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
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
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 - Scopus: 8
Analysis of Intelligent Software Implementations in Air Cargo Using Fermatean Fuzzy Codas Method
(Springer, 2022) Sergi, Duygu; Sarı, İrem Ucal; Kuchta, Dorota
The chapter focuses on the problem of analyzing and selecting intelligent software in Air Cargo in the concept of Aviation 4.0. First, the notions, problems and challenges linked to air cargo are discussed. Recent developments, ongoing innovative projects and unfilled gaps in the area of intelligent air cargo software are presented. Next, the proposed method to analyze a select software to be used by air cargo companies is described. It is a modified version of one of the recent multi-criteria decision-making methods, called CODAS. Its original, crisp version and its existing fuzzy extensions are first presented. Next, an original extension of the method, using Fermatean fuzzy sets, is proposed. In the application section a logistics company is considered, which is facing the problem of selecting software supporting the air cargo process. The criteria are selected by experts holding various positions in the company, and three alternatives of air cargo software provider are determined. Then, the proposed method is applied to solve the intelligent software selection problem. Finally, conclusion and future research perspectives are given.
Citation - WoS: 12
Citation - Scopus: 12
Bilevel Models on the Competitive Facility Location Problem
(Springer, 2017) Küçükaydın, Hande; Aras, Necati
Facility location and allocation problems have been a major area of research for decades, which has led to a vast and still growing literature. Although there are many variants of these problems, there exist two common features: finding the best locations for one or more facilities and allocating demand points to these facilities. A considerable number of studies assume a monopolistic viewpoint and formulate a mathematical model to optimize an objective function of a single decision maker. In contrast, competitive facility location (CFL) problem is based on the premise that there exist competition in the market among different firms. When one of the competing firms acts as the leader and the other firm, called the follower, reacts to the decision of the leader, a sequential-entry CFL problem is obtained, which gives rise to a Stackelberg type of game between two players. A successful and widely applied framework to formulate this type of CFL problems is bilevel programming (BP). In this chapter, the literature on BP models for CFL problems is reviewed, existing works are categorized with respect to defined criteria, and information is provided for each work.
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
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.
Capacity Allocation and Pricing Policies for Cloud Computing Service Providers
(Curran Associates Inc., 2018) Ünlüyurt T.; Özgür Özlük; Afghah, R.
The cloud computing is regarded as a paradigm shift in today’s IT world. As cloud computing resources behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this paper, we propose various methods for pricing and capacity allocation. We consider three types of instances offered by the service provider; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose different models. We simulate these models on a randomly generated dataset and evaluate the models for different capacities. The results we obtain indicate the sensitivity of revenue to varying policies and demonstrate the potential profit increase for cloud service providers. © 2018, Curran Associates Inc. All rights reserved.
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
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.
Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times
(2016) Michelini, S; Arda, Y; Küçükaydın, Hande
...
Combining Acceleration Techniques for Pricing in a Vrp With Time Windows
(2016) Michelini, S; Arda, Y; Küçükaydın, Hande
...
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
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
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.
Customer Segmentation Using Rfm Analysis: Real Case Application on a Fuel Company
(Emerald Publishing Limited., 2020) Ucal Sarı, İrem; Sergi, Duygu; Ozkan, Burcu
Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.

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