Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

Browse

Now showing 1 - 20 of 216

Metadata only
Adapted infinite kernel learning by multi-local algorithm
(World Scientific Publishing Co, 2016-05) Özöğür Akyüz, S.; Üstünkar, Gürkan; Weber, G. W.
The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.
Metadata only
Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
(IEEE, 2023) Selçuk, Yağmur Selenay; Göktürk, Elvin Çoban; Industrial Engineering; GÖKTÜRK, Elvin Çoban; Selçuk, Yağmur Selenay
Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time.
Open Access
Agile performance indicators for team performance evaluation in a corporate environment
(The ACM Digital Library, 2018) Ertaban, Cihangir; Sarıkaya, E.; Bağrıyanık, S.; Ertaban, Cihangir
Software development is a must for almost all industries including services, production, health and even construction. Being so widespread, software development industry needs metrics for especially two reasons; performance evaluation of development teams and continuous improvement. Moreover, use of metrics and measurements provides the ability to understand the problems and waste in the value stream so that they can be eliminated. This paper proposes a model of metrics -to be called as Agile Performance Indicators- which is also being developed and tested in the largest Digital Operator in Turkey.
Metadata only
Aid allocation for camp-based and urban refugees with uncertain demand and replenishments
(Wiley, 2021-12) Azizi, S.; Bozkır, Cem Deniz Çağlar; Trapp, A. C.; Kundakcıoğlu, Ömer Erhun; Kurbanzade, Ali Kaan; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Bozkır, Cem Deniz Çağlar; Kurbanzade, Ali Kaan
There are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp-based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and volatility in funding. Aid allocation should be carried out in a manner that properly balances the need of ensuring sufficient aid for camp-based refugees, with the ability to share excess inventory, when available, with urban refugees that at times seek nearby camp-based aid. We develop an inventory management policy to govern a camp's sharing of aid with urban refugee populations in the midst of uncertainties related to camp-based and urban demands, and replenishment cycles due to funding issues. We use the policy to construct costs associated with: (i) referring urban populations elsewhere, (ii) depriving camp-based refugee populations, and (iii) holding excess inventory in the refugee camp system. We then seek to allocate aid in a manner that minimizes the expected overall cost to the system. We propose two approaches to solve the resulting optimization problem, and conduct computational experiments on a real-world case study as well as on synthetic data. Our results are complemented by an extensive simulation study that reveals broad support for our optimal thresholds and allocations to generalize across varied key parameters and distributions. We conclude by presenting related discussions that reveal key managerial insights into humanitarian aid allocation under uncertainty.
Metadata only
Algorithmic expedients for the prize collecting Steiner tree problem
(Elsevier, 2010) Haouari, Mohamed; Layeb, S. B.; Sherali, H. D.; Industrial Engineering; HAOUARI, Mohamed
This paper investigates the Prize Collecting Steiner Tree Problem (PCSTP) on a graph, which is a generalization of the well-known Steiner tree problem. Given a root node, edge costs, node prizes and penalties, as well as a preset quota, the PCSTP seeks to find a subtree that includes the root node and collects a total prize not smaller than the specified quota, while minimizing the sum of the total edge costs of the tree plus the penalties associated with the nodes that are not included in the subtree. For this challenging network design problem that arises in telecommunication settings, we present two valid 0-1 programming formulations and use them to develop preprocessing procedures for reducing the graph size. Also, we design an optimization-based heuristic that requires solving a PCSTP on a specific tree-subgraph. Although, this latter special case is shown to be NP-hard, it is effectively solvable in pseudo-polynomial time. The worst-case performance of the proposed heuristic is also investigated. In addition, we describe new valid inequalities for the PCSTP and embed all the aforementioned constructs in an exact row-generation approach. Our computational study reveals that the proposed approach can solve relatively large-scale PCSTP instances having up to 1000 nodes to optimality.
Metadata only
Allocating cost of service to customers in inventory routing
(Informs, 2013) Özener, Okan Örsan; Ergun, Ö.; Savelsbergh, M.; Industrial Engineering; ÖZENER, Okan Örsan
Vendor-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.
Metadata only
Application of sequence-dependent traveling salesman problem in printed circuit board assembly
(IEEE, 2013-06) Alkaya, A. F.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
Optimization issues regarding the automated assembly of printed circuit boards attracted the interest of researchers for several decades. This is because even small gains in assembly time result in very important benefits in mass production. In this paper, the focus is on a particular placement machine type that has a rotational turret and a stationary component magazine. So far, this type of machine received little attention among the researchers. In this paper, the feeder configuration, placement sequencing, and assembly time minimization problems are formulated explicitly and completely (without simplifying assumptions) using nonlinear integer programming. In addition, the placement sequencing problem is shown to be a recently introduced new generalization of the traveling salesman problem (the sequence-dependent traveling salesman). These formulations show the complexity of the problems and the need for effective heuristic designs for solving them. We propose three heuristics that improve previously suggested solution methods and give comparable results when compared to simulated annealing that is a widely accepted good performing metaheuristic on combinatorial optimization problems. The heuristics are experimentally shown to improve previous methods significantly in assembly time that implies a huge economic benefit. The heuristics proposed could also be applied to other placement machines with similar operation principles.
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, Milad
In 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.
Metadata only
Applying migrating birds optimization to credit card fraud detection
(Springer Science+Business Media, 2013) Elikucuk, I.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
We discuss how the Migrating Birds Optimization algorithm (MBO) is applied to statistical credit card fraud detection problem. MBO is a recently proposed metaheuristic algorithm which is inspired by the V flight formation of the migrating birds and it was shown to perform very well in solving a combinatorial optimization problem, namely the quadratic assignment problem. As analyzed in this study, it has a very good performance in the fraud detection problem also when compared to classical data mining and genetic algorithms. Its performance is further increased by the help of some modified neighborhood definitions and benefit mechanisms.
Metadata only
An approach for predicting employee churn by using data mining
(IEEE, 2017) Yiğit, İ. O.; Shourabizadeh, Hamed
Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs.
Metadata only
Approximation algorithms for single machine scheduling with one unavailability period
(Springer Nature, 2009-03) Kacem, I.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
In this paper, we investigate the single machine scheduling problem with release dates and tails and a planned unavailability time period. We show that the problem admits a fully polynomial-time approximation scheme when the tails are equal. We derive an approximation algorithm for the general case and we show that the worst-case bound of the sequence yielded by Schrage’s algorithm is equal to 2 and that this bound is tight. Some consequences of this result are also presented.
Metadata only
The attractive traveling salesman problem
(Elsevier, 2010-05-16) Erdoğan, Güneş; Cordeau, J.-F.; Laporte, G.; Industrial Engineering; ERDOĞAN, Güneş
In the Attractive Traveling Salesman Problem the vertex set is partitioned into facility vertices and customer vertices. A maximum profit tour must be constructed on a subset of the facility vertices. Profit is computed through an attraction function: every visited facility vertex attracts a portion of the profit from the customer vertices based on the distance between the facility and customer vertices, and the attractiveness of the facility vertex. A gravity model is used for computing the profit attraction. The problem is formulated as an integer non-linear program. A linearization is proposed and strengthened through the introduction of valid inequalities, and a branch-and-cut algorithm is developed. A tabu search algorithm is also implemented. Computational results are reported.
Metadata only
Benchmarking nonlinear optimization software in technical computing environments
(Springer Science+Business Media, 2013-04) Pinter, Janos D.; Kampas, F. J.; Industrial Engineering; PINTER, Janos
Our strategic objective is to develop a broadly categorized, expandable collection of test problems, to support the benchmarking of nonlinear optimization software packages in integrated technical computing environments (ITCEs). ITCEs—such as Maple, Mathematica, and MATLAB—support concise, modular and scalable model development: their built-in documentation and visualization features can be put to good use also in test model selection and analysis. ITCEs support the flexible inclusion of both new models and general-purpose solver engines for future studies. Within this broad context, in this article we review a collection of global optimization problems coded in Mathematica, and present illustrative and summarized numerical results obtained using the MathOptimizer Professional software package.
Metadata only
Benchmarking regression algorithms for income prediction modeling
(IEEE, 2015) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, Azamat
This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.
Metadata only
Benchmarking regression algorithms for income prediction modeling
(Elsevier, 2016) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, Azamat
This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.
Metadata only
A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture
(Springer Science+Business Media, 2013-11) Sherali, H. D.; Bae, K.-H.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
The airline’s ability to offer flight schedules that provide service to passengers at desired times in competitive markets, while matching demand with an aircraft fleet of suitable size and composition, can significantly impact its profits. In this spirit, optional flight legs can be considered to construct a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Examining itinerary-based demands as well as multiple fare-classes can effectively capture network effects and realistic demand patterns. In addition, allowing flexibility on the departure times of scheduled flight legs can increase connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs within the framework of an integrated model. Airlines can also capture an adequate market share by balancing flight schedules throughout the day, and recapture considerations can contribute to more realistic accepted demand realizations. We therefore propose in this paper a model that integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, path/itinerary-based demands, and multiple fare-classes. A polyhedral analysis is conducted to generate several classes of valid inequalities, which are used along with suitable separation routines to tighten the model representation. Solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented using real data obtained from United Airlines to demonstrate the efficacy of the proposed procedures.
Metadata only
Bin packing problem with conflicts and item fragmentation
(Elsevier, 2021-02) Ekici, Ali; Industrial Engineering; EKİCİ, Ali
In this paper, we study the Bin Packing Problem with Conflicts and Item Fragmentation (BPPC-IF) which has applications in the delivery and storage of items that cannot be packed together. Given a set of items each with a certain size, the goal in BPPC-IF is to pack these items into a minimum number of fixed-capacity bins while not packing fragments of conflicting items into the same bin. We assume a size-preserving fragmentation, i.e., the total size of fragments of an item packed into the bins has to be equal to the item's original size. We first prove that BPPC-IF is still NP-hard even though items can be fragmented. Unlike the Bin Packing Problem with Item Fragmentation (BPPIF), we show that BPPC-IF does not necessarily admit optimal solutions with a special structure. Moreover, we show that preprocessing an instance with oversized items (items with size greater than bin capacity) by packing a fragment of such items with size equal to bin capacity to a single bin does not necessarily yield an optimal solution. Using this observation, we develop a lower bounding procedure. Finally, we propose a heuristic algorithm which sequentially packs items into the bins using the observation about the oversized items. Through an extensive computational study, we demonstrate the superior performance of the proposed solution approach over the existing algorithms in the literature.
Metadata only
Blood supply chain management and future research opportunities
(Springer, 2018) Ekici, Ali; Özener, Okan Örsan; Göktürk, Elvin Çoban; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; GÖKTÜRK, Elvin Çoban
In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.
Metadata only
Bounding strategies for the hybrid flow shop scheduling problem
(Elsevier, 2011-07-01) Hidri, L.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
In this paper, we investigate new lower and upper bounds for the multiple-center hybrid flow shop scheduling problem. We propose a family of center-based lower bounds as well as a destructive lower bound that is based on the concept of revised energetic reasoning. Also, we describe an optimization-based heuristic that requires iteratively solving a sequence of parallel machine problems with heads and tails. We present the results of extensive computational experiments that provide evidence that the proposed bounding procedures consistently improve the best existing ones.
Metadata only
A branch-and-cut algorithm for solving the non-preemptive capacitated swapping problem
(Elsevier, 2010-08-06) Erdoğan, Güneş; Cordeau, J.-F.; Laporte, G.; Industrial Engineering; ERDOĞAN, Güneş
This paper models and solves a capacitated version of the Non-Preemptive Swapping Problem. This problem is defined on a complete digraph , at every vertex of which there may be one unit of supply of an item, one unit of demand, or both. The objective is to determine a minimum cost capacitated vehicle route for transporting the items in such a way that all demands are satisfied. The vehicle can carry more than one item at a time. Three mathematical programming formulations of the problem are provided. Several classes of valid inequalities are derived and incorporated within abranch-and-cut algorithm, and extensive computational experiments are performed on instances adapted from TSPLIB.

Browse

Browsing by Title