Özdamar, Linet

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Profile URL
https://hdl.handle.net/20.500.14365/7175
Name Variants
Özdamar, L.
Oezdamar, Linet
Ozdamar, L.
Ozdamar, Linet
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Email Address
Main Affiliation
03.05. Logistics Management
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Former Staff
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ORCID ID
https://orcid.org/0000-0002-9276-7502
Scopus Author ID
7004162696
Turkish CoHE Profile ID
D3C430BA69D9399C
Google Scholar ID
turFZa0AAAAJ
WoS Researcher ID
GCV-5906-2022

Sustainable Development Goals

5

GENDER EQUALITY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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13

CLIMATE ACTION
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8

DECENT WORK AND ECONOMIC GROWTH
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14

LIFE BELOW WATER
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17

PARTNERSHIPS FOR THE GOALS
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NO POVERTY
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2

ZERO HUNGER
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4

QUALITY EDUCATION
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11

SUSTAINABLE CITIES AND COMMUNITIES
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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GOOD HEALTH AND WELL-BEING
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6

CLEAN WATER AND SANITATION
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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REDUCED INEQUALITIES
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15

LIFE ON LAND
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AFFORDABLE AND CLEAN ENERGY
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75

Citations

4694

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30

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Scholarly Output

7

Articles

5

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WoS Citation Count

523

Scopus Citation Count

708

WoS h-index

3

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3

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WoS Citations per Publication

74.71

Scopus Citations per Publication

101.14

Open Access Source

3

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JournalCount
European Journal of Operatıonal Research2
Advances in Metaheurıstıcs For Hard Optımızatıon1
Computers & Operatıons Research1
Journal of Global Optımızatıon1
Models And Algorıthms For Global Optımızatıon1
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Now showing 1 - 7 of 7
  • Thumbnail Image
    Article
    Citation - WoS: 474
    Citation - Scopus: 640
    A Dynamic Logistics Coordination Model for Evacuation and Support in Disaster Response Activities
    (Elsevier Science Bv, 2007) Yi, Wei; Ozdamar, Linet
    This paper describes an integrated location-distribution model for coordinating logistics support and evacuation operations in disaster response activities. Logistics planning in emergencies involves dispatching commodities (e.g., medical materials and personnel, specialised rescue equipment and rescue teams, food, etc.) to distribution centres in affected areas and evacuation and transfer of wounded people to emergency units. During the initial response time it is also necessary to set up temporary emergency centers and shelters in affected areas to speed up medical care for less heavily wounded survivors. In risk mitigation studies for natural disasters, possible sites where these units can be situated are specified according to risk based urban structural analysis. Logistics coordination in disasters involves the selection of sites that result in maximum coverage of medical need in affected areas. Another important issue that arises in such emergencies is that medical personnel who are on duty in nearby hospitals have to be re-shuffled to serve both temporary and permanent emergency units. Thus, an optimal medical personnel allocation must be determined among these units. The proposed model also considers this issue. The proposed model is a mixed integer multi-commodity network flow model that treats vehicles as integer commodity flows rather than binary variables. This results in a more compact formulation whose output is processed to extract a detailed vehicle route and load instruction sheet. Post processing is achieved by a simple routing algorithm that is pseudo-polynomial in the number of vehicles utilized, followed by the solution of a linear system of equations defined in a very restricted domain. The behavior and solvability of the model is illustrated on an earthquake scenario based on Istanbul's risk grid as well as larger size hypothetical disaster scenarios. (c) 2006 Elsevier B.V. All rights reserved.
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    Book Part
    Citation - WoS: 2
    Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization
    (Springer-Verlag Berlin, 2008) Pedamallu, Chandra Sekhar; Özdamar, Linet
    The continuous Constrained Optimization Problem (COP) often occurs in industrial applications. In this study, we compare three novel algorithms developed for solving the COP. The first approach consists of an Interval Partitioning Algorithm (IPA) that is exhaustive in covering the whole feasible space. IPA has the capability of discarding sub-spaces that are sub-optimal and/or infeasible, similar to available Branch and Bound techniques. The difference of IPA lies in its use of Interval Arithmetic rather than conventional bounding techniques described in the literature. The second approach tested here is the novel dual-sequence Simulated Annealing (SA) algorithm that eliminates the use of penalties for constraint handling. Here, we also introduce a hybrid algorithm that integrates SA in IPA (IPA-SA) and compare its performance with stand-alone SA and IPA algorithms. All three methods have a local COP solver, Feasible Sequential Quadratic Programming (FSQP) incorporated so as to identify feasible stationary points. The performances of these three methods are tested on a suite of COP benchmarks and the results are discussed.
  • Thumbnail Image
    Article
    Citation - Scopus: 3
    A Dual Sequence Simulated Annealing Algorithm for Constrained Optimization
    (2007) Ozdamar L.
    We propose a dual sequence Simulated Annealing algorithm, DSAC, for solving constrained optimization problems. This approach eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. We compare DSAC with a similar single sequence algorithm, PSAC, where the objective function is augmented by various penalty forms related to constraint infeasibilities. Numerical experiments show that DSAC is more effective than its counterpart PSAC in the worst and average case performances.
  • Thumbnail Image
    Article
    Citation - WoS: 43
    Citation - Scopus: 58
    Investigating a Hybrid Simulated Annealing and Local Search Algorithm for Constrained Optimization
    (Elsevier, 2008) Pedamallu, Chandra Sekhar; Ozdamar, Linet
    Constrained Optimization Problems (COP) often take place in many practical applications such as kinematics, chemical process optimization, power systems and so on. These problems are challenging in terms of identifying feasible solutions when constraints are non-linear and non-convex. Therefore, finding the location of the global optimum in the non-convex COP is more difficult as compared to non-convex bound-constrained global optimization problems. This paper proposes a Hybrid Simulated Annealing method (HSA), for solving the general COP. HSA has features that address both feasibility and optimality issues and here, it is supported by a local search procedure, Feasible Sequential Quadratic Programming (FSQP). We develop two versions of HSA. The first version (HSAP) incorporates penalty methods for constraint handling and the second one (HSAD) eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. Numerical experiments show that the second version is more reliable in the worst case performance. (C) 2006 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Book Part
    Citation - Scopus: 2
    An Interval Partitioning Approach for Continuous Constrained Optimization
    (Springer, 2007) Pedamallu, Chandra Sekhar; Ozdamar, Linet; Csendes, Tibor
    Constrained Optimization Problems (COP's) are encountered in many scientific fields concerned with industrial applications such as kinematics, chemical process optimization, molecular design, etc. When non-linear relationships among variables are defined by problem constraints resulting in non-convex feasible sets, the problem of identifying feasible solutions may become very hard. Consequently, finding the location of the global optimum in the COP is more difficult as compared to bound-constrained global optimization problems. This chapter proposes a new interval partitioning method for solving the COP. The proposed approach involves a new subdivision direction selection method as well as an adaptive search tree framework where nodes (boxes defining different variable domains) are explored using a restricted hybrid depth-first and best-first branching strategy. This hybrid approach is also used for activating local search in boxes with the aim of identifying different feasible stationary points. The proposed search tree management approach improves the convergence speed of the interval partitioning method that is also supported by the new parallel subdivision direction selection rule (used in selecting the variables to be partitioned in a given box). This rule targets directly the uncertainty degrees of constraints (with respect to feasibility) and the uncertainty degree of the objective function (with respect to optimality). Reducing these uncertainties as such results in the early and reliable detection of infeasible and sub-optimal boxes, thereby diminishing the number of boxes to be assessed. Consequently, chances of identifying local stationary points during the early stages of the search increase. The effectiveness of the proposed interval partitioning algorithm is illustrated on several practical application problems and compared with professional commercial local and global solvers. Empirical results show that the presented new approach is as good as available COP solvers.
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Symbolic Interval Inference Approach for Subdivision Direction Selection in Interval Partitioning Algorithms
    (Springer, 2007) Pedamallu, Chandra Sekhar; Özdamar, Linet; Csendes, Tibor
    In bound constrained global optimization problems, partitioning methods utilizing Interval Arithmetic are powerful techniques that produce reliable results. Subdivision direction selection is a major component of partitioning algorithms and it plays an important role in convergence speed. Here, we propose a new subdivision direction selection scheme that uses symbolic computing in interpreting interval arithmetic operations. We call this approach symbolic interval inference approach (SIIA). SIIA targets the reduction of interval bounds of pending boxes directly by identifying the major impact variables and re-partitioning them in the next iteration. This approach speeds up the interval partitioning algorithm (IPA) because it targets the pending status of sibling boxes produced. The proposed SIIA enables multi-section of two major impact variables at a time. The efficiency of SIIA is illustrated on well-known bound constrained test functions and compared with established subdivision direction selection methods from the literature.
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Efficient Interval Partitioning - Local Search Collaboration for Constraint Satisfaction
    (Pergamon-Elsevier Science Ltd, 2008) Pedamallu, Chandra Sekhar; Ozdamar, Linet; Ceberio, Martine
    In this article, a cooperative solution methodology that integrates interval partitioning (IP) algorithms with a local search, feasible sequential quadratic programming (FSQP), is presented as a technique to enhance the solving of continuous constraint satisfaction problems (continuous CSP). FSQP is invoked using a special search tree management system developed to increase search efficiency in finding feasible solutions. In this framework, we introduce a new symbolic method for selecting the subdivision directions that targets immediate reduction of the uncertainty related to constraint infeasibility in child boxes. This subdivision method is compared against two previously established partitioning rules (also parallelized in a similar manner) used in the interval literature and shown to improve the efficiency of IP. Further, the proposed tree management system is compared with tree management approaches that are classically used in IP. The whole method is compared with published results of established symbolic-numeric methods for solving CSP on a number of state-of-the-art benchmarks. (c) 2006 Elsevier Ltd. All rights reserved.