The Department of Industrial Engineering has seven research areas for students to choose from. Every research area is unique and has its own research theme and course work.
APPLIED STATISTICS & sTATISTICAL lEARNING
Research in applied statistics and statistical learning investigates technologies and methodologies in the area of data science. It helps drive business value, improve decision-making, understand human relationships and transform data into knowledge.
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Research in financial engineering integrates methods and knowledge from mathematics, statistics, economics, operations research, and computer science.
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Research in healthcare engineering integrates and develops operations research, management science, analytics, and computer science methodologies with applications to, and motivation from, problems in developing cost effective health and humanitarian systems.
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Research in optimization focuses on the design of algorithms and models for decision making, often under uncertainty.
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Research on management science focuses on the social and technical dynamics within formal and informal organizations.
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Logistics and operations
Research in Logistics and Operations focuses on supply chain management, product development, and service operations.
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sTochastic Analysis & simulation
Research in stochastic analysis & simulation derives new methods for the design, analysis, and optimization of simulation experiments.
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Reza Zanjirani Farahani | Masoud Hekmatfar | Alireza Boloori Arabani | Ehsan Nikbakhsh
Hub location problem (HLP) is a relatively new extension of classical facility location problems. Hubs are facilities that work as consolidation, connecting, and switching points for flows between stipulated origins and destinations. While there are few review papers on hub location problems, the most recent one (Alumur and Kara, 2008. Network hub location problems: The state of the art. European Journal of Operational Research, 190, 1-21) considers solely studies on network-type hub location models prior to early 2007. Therefore, this paper focuses on reviewing the most recent advances in HLP from 2007 up to now. In this paper, a review of all variants of HLPs (i.e., network, continuous, and discrete HLPs) is provided. In particular, mathematical models, solution methods, main specifications, and applications of HLPs are discussed. Furthermore, some case studies illustrating real-world applications of HLPs are briefly introduced. At the end, future research directions and trends will be presented. © 2013 Elsevier Ltd. All rights reserved.
Ehsan Valian | Saeed Tavakoli | Shahram Mohanna | Atiyeh Haghi
An efficient approach to solve engineering optimization problems is the cuckoo search algorithm. It is a recently developed meta-heuristic optimization algorithm. Normally, the parameters of the cuckoo search are kept constant. This may result in decreasing the efficiency of the algorithm. To cope with this issue, the cuckoo search parameters should be tuned properly. In this paper, an improved cuckoo search algorithm, enhancing the accuracy and convergence rate of the cuckoo search algorithm, is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are four well-known reliability optimization problems, a large-scale reliability optimization problem as well as a complex system, which is a 15-unit system reliability optimization problem. Finally, the results are compared with those given by several well-known methods. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2012 Elsevier Ltd. All rights reserved.
Harish Garg | S. P. Sharma
This paper considers the multi-objective reliability redundancy allocation problem of a series system where the reliability of the system and the corresponding designing cost are considered as two different objectives. Due to non-stochastic uncertain and conflicting factors it is difficult to reduce the cost of the system and improve the reliability of the system simultaneously. In such situations, the decision making is difficult, and the presence of multi-objectives gives rise to multi-objective optimization problem (MOOP), which leads to Pareto optimal solutions instead of a single optimal solution. However in order to make the model more flexible and adaptable to human decision process, the optimization model can be expressed as fuzzy nonlinear programming problems with fuzzy numbers. Thus in a fuzzy environment, a fuzzy multi-objective optimization problem (FMOOP) is formulated from the original crisp optimization problem. In order to solve the resultant problem, a crisp optimization problem is reformulated from FMOOP by taking into account the preference of decision maker regarding cost and reliability goals and then particle swarm optimization is applied to solve the resulting fuzzified MOOP under a number of constraints. The approach has been demonstrated through the case study of a pharmaceutical plant situated in the northern part of India. © 2012 Elsevier Ltd. All rights reserved.
Fatma Pinar Goksal | Ismail Karaoglan | Fulya Altiparmak
Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions. © 2012 Elsevier Ltd. All rights reserved.
G. Kanagaraj | S. G. Ponnambalam | N. Jawahar
Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS-GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms' performance. The computational results carried out on four classical reliability-redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS-GA algorithm proves to be extremely effective and efficient at locating optimal solutions. © 2013 Elsevier Ltd. All rights reserved.
Jairo R. Montoya-Torres | Julián López Franco | Santiago Nieto Isaza | Heriberto Felizzola Jiménez | Nilson Herazo-Padilla
© 2014 Elsevier Ltd. In this paper, we present a state-of-the-art survey on the vehicle routing problem with multiple depots (MDVRP). Our review considered papers published between 1988 and 2014, in which several variants of the model are studied: time windows, split delivery, heterogeneous fleet, periodic deliveries, and pickup and delivery. The review also classifies the approaches according to the single or multiple objectives that are optimized. Some lines for further research are presented as well.
Jenn Rong Lin | Ta Hui Yang | Yu Chung Chang
This study addresses a strategic design problem for bicycle sharing systems incorporating bicycle stock considerations. The problem is formulated as a hub location inventory model. The key design decisions considered are: the number and locations of bicycle stations in the system, the creation of bicycle lanes between bicycle stations, the selection of paths of users between origins and destinations, and the inventory levels of sharing bicycles to be held at the bicycle stations. The design decisions are made with consideration for both total cost and service levels (measured both by the availability rate for rental requests at the pick-up rental stations and coverage of the origins and destinations). The optimal design of this system requires an integrated view of the travel costs of users, bicycle inventory costs and facility costs of bicycle stations and bicycle lanes as well as service levels. The purpose of this study is to create a formal model that provides such an integrated view, and to develop methods for obtaining solutions for the design variables in practical situations. The complexity of the problem precludes the exact solution of the optimization problem for instances of realistic size, and so we propose a heuristic method for efficiently finding near-optimal solutions. In the test problem for which enumeration is possible, the heuristic solution is within 2% optimal. Finally, a numerical example is created to illustrate the model and proposed solution algorithm. © 2011 Elsevier Ltd. All rights reserved.
L. Aboueljinane | E. Sahin | Z. Jemai
Emergency medical services (EMS) are public safety systems responsible for the pre-hospital stabilization and transport of seriously injured patients. The goal of such systems is to respond adequately to population calls by providing first aid services and transferring patients, when needed, to the emergency department of the appropriate hospital. In order to achieve this goal, a variety of tools (e.g. simulation, mathematical programming and queuing theory models) have been used to improve the performance of EMS. This paper focuses specifically on computer simulation models used for the analysis and improvement of EMS. In particular, we give a critical overview of the existing international literature on simulation models for EMS by pinpointing the issues considered, the associated modeling assumptions as well as the results obtained. Such a contribution is lacking in the current literature. © 2013 Elsevier Ltd. All rights reserved.
Ping Shun Chen | Ming Tsung Wu
In the emerging supply chain environment, supply chain risk management plays a more important role than ever. Companies must focus not only on the efficiency of supply chain, but also on its risks. If an unanticipated event occurs, all of the supply chain members will be impacted, and the result will cause significant loss. Therefore, this research proposes a modified failure mode and effects analysis (MFMEA) method to select new suppliers from the supply chain risk's perspective and applies the analytic hierarchy process (AHP) method to determine the weight of each criterion and sub-criterion for supplier selection. An IC assembly company is then studied to validate this model. The result shows that the case company can categorize its suppliers more effectively and at the same time select a low-risk supply chain partner. Moreover, the case company can provide unsatisfactory suppliers with valuable feedback that will help them improve and become its partners in the future. © 2013 Elsevier Ltd. All rights reserved.
Diego Fernández-Francos | David Marténez-Rego | Oscar Fontenla-Romero | Amparo Alonso-Betanzos
Rolling-element bearings are among the most used elements in industrial machinery thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals an automatic method for bearing fault detection and diagnosis is presented in this work. Initially a one-class m-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time. ©2012 Elsevier Ltd. All rights reserved.
Zhiming Zhang | Chao Wang | Dazeng Tian | Kai Li
In this paper, we develop a series of induced generalized aggregation operators for hesitant fuzzy or interval-valued hesitant fuzzy information, including induced generalized hesitant fuzzy ordered weighted averaging (IGHFOWA) operators, induced generalized hesitant fuzzy ordered weighted geometric (IGHFOWG) operators, induced generalized interval-valued hesitant fuzzy ordered weighted averaging (IGIVHFOWA) operators, and induced generalized interval-valued hesitant fuzzy ordered weighted geometric (IGIVHFOWG) operators. Next, we investigate their various properties and some of their special cases. Furthermore, some approaches based on the proposed operators are developed to solve multiple attribute group decision making (MAGDM) problems with hesitant fuzzy or interval-valued hesitant fuzzy information. Finally, some numerical examples are provided to illustrate the developed approaches. © 2013 Elsevier Ltd. All rights reserved.
Zhi Ping Fan | Xiao Zhang | Fa Dong Chen | Yang Liu
In this paper, a method based on prospect theory is proposed to solve the multiple attribute decision making (MADM) problem considering aspiration-levels of attributes, where attribute values and aspiration-levels are represented in two different formats: crisp numbers and interval numbers. According to the idea of prospect theory, aspiration-levels are firstly regarded as the reference points, and the four possible types for comparing an attribute value with an aspiration-level are described. Then, for all possible cases of the four types, the calculation formulae of gains and losses of alternatives concerning attributes are given. By calculating gain and loss of each alternative, a gain matrix and a loss matrix are constructed, respectively. Further, using the value function proposed in prospect theory and the simple additive weighting m ethod, the overall prospect value of each alternative is calculated. Based on the obtained overall prospect values, a ranking of alternatives can be determined. Finally, a numerical example is used to illustrate the use of the proposed method. © 2013 Elsevier Ltd. All rights reserved.
Raúl Baños | Julio Ortega | Consolación Gil | Antonio L. Márquez | Francisco De Toro
The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtime s. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomon's benchmark problems show the good performance of this hybrid approach. © 2013 Elsevier Ltd. All rights reserved.
E. Ertugrul Karsak | Mehtap Dursun
© 2015 Elsevier Ltd. 2015 Elsevier Ltd. All rights reserved. A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.
Neda Manavizadeh | Nilufar Sadat Hosseini | Masoud Rabbani | Fariborz Jolai
This research deals with balancing a mixed-model U-line in a Just-In-Time (JIT) production system. The research intends to reduce the number of stations via balancing the workload and maximizing the weighted efficiency, which both are considered as the objectives of this research paper. After balancing the line and determining the number of stations, the labor assignment policy should be set. In this study, it was assumed that there are two types of operators: permanent and temporary. Both types can work in regular and overtime periods. Based on their skill levels, workers are classified into four types. The sign at each work station indicates types of workers allowed to work at that station. An alert system using the hybrid kanban systems was also considered. To solve this problem, a Simulated Annealing algorithm was applied in the following three stages. First, the balancing problem was solved and the number of stations was determined. Second, workers were assigned to the workstations in which they are qualified to work. Following that, an alert system based on the kanban system was designed to balance the work in the process inventory. This was achieved by defining control points based on the processing time and making control decisions to minimize the number of kanban cards. In the proposed SA algorithm, two methods for the temperature cooling schedule were considered and two methods were defined for determining the number of neighborhood search. The initial temperature was considered equal to the cost of the initial solution to reach the convergence situation as soon as possible. Five problems were solved in small size using the GAMS software. The results obtained from the GAMS software were compared with those obtained from the SA algorithm to determine the performance difference. The computational results demonstrated that the SA algorithm is more consistent with the answers obtained. Also seven large scale problems were solved. The results showed that the SA algorithm still have better reliability. To show the efficiency of the proposed SA algorithm, an axel assembly company was studied. To satisfy demands and reduce backlogging, a mixed model assembly line was designed for this case study. The results showed that the mixed model assembly line designed using the SA algorithm had good efficiency. © 2012 Elsevier Ltd. All rights reserved.
Hector Toro-Díaz | Maria E. Mayorga | Sunarin Chanta | Laura A. McLay
The main purpose of Emergency Medical Service systems is to save lives by providing quick response to emergencies. The performance of these systems is affected by the location of the ambulances and their allocation to the customers. Previous literature has suggested that simultaneously making location and dispatching decisions could potentially improve some performance measures, such as response times. We developed a mathematical formulation that combines an integer programming model representing location and dispatching decisions, with a hypercube model representing the queuing elements and congestion phenomena. Dispatching decisions are modeled as a fixed priority list for each customer. Due to the model's complexity, we developed an optimization framework based on Genetic Algorithms. Our results show that minimization of response time and maximization of coverage can be achieved by the commonly used closest dispatching rule. In addition, solutions with minimum response time also yield good values of expected coverage. The optimization framework was able to consistently obtain the best solutions (compared to enumeration procedures), making it suitable to attempt the optimization of alternative optimization criteria. We illustrate the potential benefit of the joint approach by using a fairness performance indicator. We conclude that the joint approach can give insights of the implicit trade-offs between several conflicting optimization criteria. © 2013 Elsevier B.V. All rights reserved.
Kuei Tang Fang | Bertrand M.T. Lin
Traditional research on machine scheduling focuses on job allocation and sequencing to optimize certain objective functions that are defined in terms of job completion times. With regard to environmental concerns, energy consumption becomes another critical issue in high-performance systems. This paper addresses a scheduling problem in a multiple-machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution. The CPU adjustment capability enables the flexibility for minimizing electricity cost from the energy saving aspect by sacrificing job completion times. The decision of the studied problem is to dispatch the jobs to the machines as well as to determine the job sequence and processing speed of each machine with the objective function comprising of the total weighted job tardiness and the power cost. We give a formal formulation, propose two heuristic algorithms, and develop a particle swarm optimization (PSO) algorithm to effectively tackle the problem. Since the existing solution representations do not befittingly encode the decisions involved in the studied problem into the PSO algorithm, we design a tailored encoding scheme which can embed all decisional information in a particle. A computational study is conducted to investigate the performances of the proposed heuristics and the PSO algorithm. © 2012 Elsevier Ltd. All rights reserved.
Reza Zanjirani Farahani | Masoud Hekmatfar | Behnam Fahimnia | Narges Kazemzadeh
The primary objective in a typical hierarchical facility location problem is to determine the location of facilities in a multi-level network in a way to serve the customers at the lowest level of hierarchy both efficiently (cost minimization objective) and effectively (service availability maximization objective). This paper presents a comprehensive review of over 40 years of hierarchical facility location modeling efforts. Published models are classified based on multiple characteristics including the type of flow pattern, service availability, spatial configuration, objective function, coverage, network levels, time element, parameters, facilities, capacity, and real world application. A second classification is also presented on the basis of solution methods adopted to solve various hierarchical facility location problems. The paper finally identifies the gaps in the current literature and suggests directions for future modeling efforts. © 2013 Elsevier Masson SAS. All rights reserved.
Jian Wu | Yujia Liu
This article proposes an approach to resolve multiple attribute group decision making (MAGDM) problems with interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs). We first introduce the cut set of IVITFNs and investigate the attitudinal score and accuracy expected functions for IVITFNs. Their novelty is that they allow the comparison of IVITFNs by taking into accounting of the experts' risk attitude. Based on these expected functions, a ranking method for IVITFNs is proposed and a ranking sensitivity analysis method with respect to the risk attitude is developed. To aggregate the information with IVITFNs, we study the desirable properties of the interval-valued intuitionistic trapezoidal fuzzy weighted geometric (IVITFWG) operator, the interval-valued intuitionistic trapezoidal fuzzy ordered weighted geometric (IVITFOWG) operator, and the interval-valued intuitionistic trapezoidal fuzzy hybrid geometric (IVITFHG) operator. It is worth noting that the aggregated value by using these operators is also an interval-valued intuitionistic trapezoidal fuzzy value. Then, based on these expected functions and aggregating operators, an approach is proposed to solve MAGDM problems in which the attribute values take the form of interval-valued intuitionistic fuzzy numbers and the expert weights take the form of real numbers. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness. © 2013 Elsevier Ltd.