Industrial Engineering

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

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Now showing 1 - 20 of 216

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Detecting credit card fraud by modified Fisher discriminant analysis
(Elsevier, 01.04.2015) Mahmoudi, Nader; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Mahmoudi, Nader
In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.
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Energetic reasoning revisited: application to parallel machine scheduling
(Springer Science+Business Media, 2008-08) Hidri, L.; Gharbi, A.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
We consider the problem of minimizing makespan on identical parallel machines subject to release dates and delivery times. We present several new feasibility tests and adjustment techniques that consistently improve theclassical energetic reasoning approach. Computational results carried out on a set of hard instances provide strong evidence that the performance of a state-of-the-art exact branch-and-bound algorithm is substantially improved through embedding the proposed enhanced energetic reasoning.
Open Access
MathOptimizer: a nonlinear optimization package for mathematica users
(2009) Kampas, F. J.; Pinter, Janos D.; Industrial Engineering; PINTER, Janos
Mathematica is an advanced software system that enables symbolic computing, numerics, program code development, model visualization and professional documentation in a unified framework. Our MathOptimizer software package serves to solve global and local optimization models developed using Mathematica. We introduce MathOptimizer’s key features and discuss its usage options that support a range of operational modes. The numerical capabilities of the package are illustrated by simple and more advanced examples, pointing towards a broad range of potential applications.
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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.
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Heuristics for the variable sized bin-packing problem
(Elsevier, 2009-10) Haouari, Mohamed; Serairi, M.; Industrial Engineering; HAOUARI, Mohamed
We investigate the one-dimensional variable-sized bin-packing problem. This problem requires packing a set of items into a minimum-cost set of bins of unequal sizes and costs. Six optimization-based heuristics for this problem are presented and compared. We analyze their empirical performance on a large set of randomly generated test instances with up to 2000 items and seven bin types. The first contribution of this paper is to provide evidence that a set covering heuristic proves to be highly effective and capable of delivering very-high quality solutions within short CPU times. In addition, we found that a simple subset-sum problem-based heuristic consistently outperforms heuristics from the literature while requir- ing extremely short CPU times.
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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.
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Integrated airline schedule design and fleet assignment: polyhedral analysis and benders’ decomposition approach
(Informs, 2010-01) Sherali, H. D.; Bae, K.-H.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
The main airline operations consist of schedule planning, fleet assignment, aircraft routing, and crew scheduling. To improve profitability, we present in this paper an integrated fleet assignment model with schedule planning by simultaneously considering optional flight legs to select along with the assignment of aircraft types to all scheduled legs. In addition, we consider itinerary-based demands for multiple fare classes. A polyhedral analysis is conducted of the proposed mixed-integer programming model to tighten its representation via several classes of valid inequalities. Solution approaches are developed by applying Benders' decomposition method to the resulting lifted model, and computational results are presented using real data obtained from a major U.S. airline to demonstrate the efficacy of the proposed procedures.
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Discrete time/cost trade-off problem: a decomposition-based solutionalgorithm for the budget version
(Elsevier, 2010-04) Hazır, Ö.; Haouari, Mohamed; Erel, E.; Industrial Engineering; HAOUARI, Mohamed
This paper investigates the budget variant of the discrete time/cost trade-off problem (DTCTP). This multi-mode project scheduling problem requires assigning modes to the activities of a project so that the total completion time is minimized and the budget and the precedence constraints are satisfied. This problem is often encountered in practice as timely completion of the projects without exceeding the budget is crucial. The contribution of this paper to the literatures is to describe an effective Benders Decomposition-based exact algorithm to solve the DTCTP instances of realistic sizes. Although Benders Decomposition often exhibits a very slow convergence, we have included several algorithmic features to enhance the performance of the proposed tailored approach. Computational results attest to the efficacy of the proposed algorithm, which can solve large-scale instances to optimality.
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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.
Open Access
Calibrating artificial neural networks by global optimization
(2010-07) Pinter, Janos D.; Industrial Engineering; PINTER, Janos
An artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply such a generic procedure to actual decision problems, a key requirement isANN training to minimize the discrepancy between modeled and measured system output. In this work, we consider ANN training as a (potentially) multi-modal optimization problem. To address this issue, we introduce a global optimization (GO) framework and corresponding GO software. The practical viability of the GO based approach is illustrated by finding close numerical approximations of (one-dimensional, but non-trivial) functions.
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The traveling salesman problem with pickups, deliveries, and handling costs
(Informs, 2010-08) Battarra, M.; Erdoğan, Güneş; Laporte, G.; Vigo, D.; Industrial Engineering; ERDOĞAN, Güneş
This paper introduces a new variant of the one-to-many-to-one single vehicle pickup and delivery problems (SVPDP) that incorporates the handling cost incurred when rearranging the load at the customer locations. The simultaneous optimization of routing and handling costs is difficult, and the resulting loading patterns are hard to implement in practice. However, this option makes economical sense in contexts where the routing cost dominates the handling cost. We have proposed some simplified policies applicable to such contexts. The first is a two-phase heuristic in which the tour having minimum routing cost is initially determined by optimally solving an SVPDP, and the optimal handling policy is then determined for that tour. In addition, branch-and-cutalgorithms based on integer linear programming formulations are proposed, in which routing and handling decisions are simultaneously optimized, but the handling decisions are restricted to three simplified policies. The formulations are strengthened by means of problem specific valid inequalities. The proposed methods have been extensively tested on instances involving up to 25 customers and hundreds of items. Our results show the impact of the handling aspect on the customer sequencing and indicate that the simplified handling policies favorably compare with the optimal one.
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An exact algorithm for the Steiner tree problem with delays
(Elsevier, 2010-08-01) Leggieri, V.; Haouari, Mohamed; Triki, C.; Industrial Engineering; HAOUARI, Mohamed
The Steiner Tree Problem with Delays (STPD) is a variant of the well-known Steiner Tree Problem in which the delay on each path between a source node and a terminal node is limited by a given maximum value. We propose a Branch-and-Cut algorithm for solving this problem using a formulation based on lifted Miller-Tucker-Zemlin subtour elimination constraints. The effectiveness of the proposed algorithm is assessed through computational experiments carried out on dense benchmark instances.
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Exact method for robotic cell problem
(Elsevier, 2010-08-01) Kharbeche, M.; Carlier, J.; Haouari, Mohamed; Moukrim, A.; Industrial Engineering; HAOUARI, Mohamed
This study investigates an exact method for the Robotic Cell Problem. We present an exact branch and bound algorithm which is the first exact procedure specifically designed for this strongly NP-hard problem. In this paper, we propose a new mathematical formulation and we describe a new lower bound for the RCP. In addition, we propose a genetic algorithm. We report that the branch and bound algorithm is more effective than the proposed mathematical formulation which can solve small sized problem. Also, computational study provides evidence that the genetic algorithm delivers reasonably good solutions while requiring significantly shorter CPU times to solve this problem.
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Strength of three MIP formulations for the prize collecting steiner tree problem with a quota constraint
(Elsevier, 2010-08-01) Haouari, Mohamed; Layeb, S. B.; Sherali, H. D.; Industrial Engineering; HAOUARI, Mohamed
This paper investigates the quota version of the Prize Collecting Steiner Tree Problem (PCSTP) on a graph as a generalization of the well-known Steiner tree problem. For this challenging network design problem that arises in telecommunication settings, we present three MIP formulations: (a) the first one is a compact Miller-Tucker-Zemlin (MTZ-) based formulation, (b) the second one is derived through lifting the MTZ constraints, and (c) the third one is based on the RLT technique. We report the results of extensive computational experiments on large PCSTP instances, having up to 2500 nodes using a general-purpose MIP solver.
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A computational study of lower bounds for the two dimensional bin packing problem
(Elsevier, 2010-08-01) Serairi, M.; Haouari, Mohamed; Industrial Engineering; HAOUARI, Mohamed
We survey lower bounds for the variant of the two-dimensional bin packing problem where items cannot be rotated. We prove that the dominance relation claimed by Carlier et al. between their lower bounds and those of Boschetti and Mingozzi is not valid. We analyze the performance of lower bounds from the literature and we provide the results of a computational experiment.
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Robust aircraft routing and flight retiming
(Elsevier, 2010-08-01) Aloulou, M. A.; Haouari, Mohamed; Mansour, F. Z.; Industrial Engineering; HAOUARI, Mohamed
In this paper, we propose an integrated model for the robust aircraft routing and flight retiming problem. The model optimizes a slack-based robustness measure that explicitly takes heed of passengers in connection and aircraft as well. We provide empirical evidence of the relevance of the proposed approach using computational experiments that were carried out on real-data, provided by Amadeus, SAS.
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IP-based energetic reasoning for the resource constrained project scheduling problem
(Elsevier, 2010-08-01) Kooli, A.; Haouari, Mohamed; Hidri, L.; Neron, E.; Industrial Engineering; HAOUARI, Mohamed
In this paper, we consider the Resource Constrained Project Scheduling Problem (RCPSP). New feasibility tests for the energetic reasoning are introduced based on new integer programming (IP) formulations. Experimental results are presented based on PSPLIB instances.
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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.
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The one-warehouse multiretailer problem with an order-up-to level inventory policy
(Wiley, 2010-10) Solyalı, O.; Süral, H.; Denizel, Meltem; Industrial Engineering; DENİZEL, Meltem
We consider a two-level system in which a warehouse manages the inventories of multiple retailers. Each retailer employs an order-up-to level inventory policy over T periods and faces an external demand which is dynamic and known. A retailer's inventory should be raised to its maximum limit when replenished. The problem is to jointly decide on replenishment times and quantities of warehouse and retailers so as to minimize the total costs in the system. Unlike the case in the single level lot-sizing problem, we cannot assume that the initial inventory will be zero without loss of generality. We propose a strong mixed integer program formulation for the problem with zero and nonzero initial inventories at the warehouse. The strong formulation for the zero initial inventory case has only T binary variables and represents the convex hull of the feasible region of the problem when there is only one retailer. Computational results with a state-of-the art solver reveal that our formulations are very effective in solving large-size instances to optimality.
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Discrepancy search for the flexible job shop scheduling problem
(Elsevier, 2010-12) Hmida, A. B.; Haouari, Mohamed; Huguet, M.-J.; Lopez, P.; Industrial Engineering; HAOUARI, Mohamed
The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop problem in which each operation must be processed on a given machine chosen among a finite subset of candidate machines. The aim is to find an allocation for each operation and to define the sequence of operations on each machine, so that the resulting schedule has a minimal completion time. We propose a variant of the climbing discrepancy search approach for solving this problem. We also present various neighborhood structures related to assignment and sequencing problems. We report the results of extensive computational experiments carried out on well-known benchmarks for flexible job shop scheduling. The results demonstrate that the proposed approach outperforms the best-known algorithms for the FJSP on some types of benchmarks and remains comparable with them on other ones.

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