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Tarım, Şahap Armağan

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Profile URL
https://hdl.handle.net/20.500.12416/9422
Name Variants
Tarım, Ş. Armağan
Tarim, S. Armagan
Tarım, Ş. Armağan
Armagan Tarim, S.
Tarım, Ş. Armağan
Tarım, Ş. Armağan
Job Title
Prof. Dr.
Email Address
Main Affiliation
03.04. İşletme
03. İktisadi ve İdari Birimler Fakültesi
01. Çankaya Üniversitesi
03.04. İşletme
İşletme
03. İktisadi ve İdari Birimler Fakültesi
01. Çankaya Üniversitesi
Status
Former Staff
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Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
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0

Research Products

3

GOOD HEALTH AND WELL-BEING
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0

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

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6

CLEAN WATER AND SANITATION
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0

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14

LIFE BELOW WATER
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0

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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

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8

DECENT WORK AND ECONOMIC GROWTH
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0

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1

NO POVERTY
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0

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4

QUALITY EDUCATION
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5

GENDER EQUALITY
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0

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10

REDUCED INEQUALITIES
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

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15

LIFE ON LAND
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0

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7

AFFORDABLE AND CLEAN ENERGY
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13

CLIMATE ACTION
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17

PARTNERSHIPS FOR THE GOALS
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2

ZERO HUNGER
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This researcher does not have a Scopus ID.
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Scholarly Output

7

Articles

4

Views / Downloads

38/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

84

Scopus Citation Count

103

WoS h-index

5

Scopus h-index

5

Patents

0

Projects

0

WoS Citations per Publication

12.00

Scopus Citations per Publication

14.71

Open Access Source

5

Supervised Theses

0

JournalCount
European Journal of Operational Research2
18th Annual Conference of the French-Society-for-Operational-Research-and-Decision-Support (ROADEF) -- FEB 22-24, 2017 -- Metz, FRANCE1
31st International Conference on Logic Programming (ICLP) Co-located with the 21st International Conference on Principles and Practice of Constraint Programming (CP) -- AUG 31-SEP 04, 2015 -- Cork, IRELAND1
Artificial Intelligence1
INFORMS Journal on Computing1
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2015 - 20187
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Scholarly Output Search Results

Now showing 1 - 7 of 7
  • Thumbnail Image
    Article
    Citation - WoS: 22
    Citation - Scopus: 23
    An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem
    (informs, 2018) Tunc, Huseyin; Kilic, Onur A.; Tarim, S. Armagan; Rossi, Roberto
    We present an extended mixed-integer programming formulation of the stochastic lot-sizing problem for the static-dynamic uncertainty strategy. The proposed formulation is significantly more time efficient as compared to existing formulations in the literature and it can handle variants of the stochastic lot-sizing problem characterized by penalty costs and service level constraints, as well as backorders and lost sales. Also, besides being capable of working with a predefined piecewise linear approximation of the cost function-as is the case in earlier formulations-it has the functionality of finding an optimal cost solution with an arbitrary level of precision by means of a novel dynamic cut generation approach.
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    Article
    Citation - WoS: 21
    Citation - Scopus: 24
    Heuristic Policies for the Stochastic Economic Lot Sizing Problem With Remanufacturing Under Service Level Constraints
    (Elsevier Science Bv, 2018) Kilic, Onur A.; Tunc, Huseyin; Tarim, S. Armagan
    In this paper, we address the stochastic economic lot sizing problem with remanufacturing under service level constraints. The problem emerges in hybrid production systems where demand can be met via two alternative sources: manufacturing new products and remanufacturing returned products. The deterministic counterpart of this problem has been considered in the literature and it is shown to be NP-Hard. We focus on the case where period demands and returns are stochastic. The optimal solution to this problem is not a deterministic production schedule but a control policy, yet its structure has not yet been characterized. We propose two heuristic policies for the problem that make use of simple decision rules to control manufacturing and remanufacturing operations and present mathematical models thereof. (C) 2018 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 4
    Robust Principal Component Analysis by Reverse Iterative Linear Programming
    (Springer Verlag, 2016) Visentin, A.; Prestwich, S.; Armagan Tarim, S.
    Principal Components Analysis (PCA) is a data analysis technique widely used in dimensionality reduction. It extracts a small number of orthonormal vectors that explain most of the variation in a dataset, which are called the Principal Components. Conventional PCA is sensitive to outliers because it is based on the L2-norm, so to improve robustness several algorithms based on the L1-norm have been introduced in the literature. We present a new algorithm for robust L1- norm PCA that computes components iteratively in reverse, using a new heuristic based on Linear Programming. This solution is focused on finding the projection that minimizes the variance of the projected points. It has only one parameter to tune, making it simple to use. On common benchmarks it performs competitively compared to other methods. The data and software related to this paper are available at https://github. com/visentin-insight/L1-PCAhp. © Springer International Publishing AG 2016.
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    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Randomness as a Constraint
    (Springer-verlag Berlin, 2015) Prestwich, Steven D.; Rossi, Roberto; Tarim, S. Armagan
    Some optimisation problems require a random-looking solution with no apparent patterns, for reasons of fairness, anonymity, undetectability or unpredictability. Randomised search is not a good general approach because problem constraints and objective functions may lead to solutions that are far from random. We propose a constraint-based approach to finding pseudo-random solutions, inspired by the Kolmogorov complexity definition of randomness and by data compression methods. Our "entropy constraints" can be implemented in constraint programming systems using well-known global constraints. We apply them to a problem from experimental psychology and to a factory inspection problem.
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    Article
    Citation - WoS: 18
    Citation - Scopus: 23
    Computing Non-Stationary (S, S) Policies Using Mixed Integer Linear Programming
    (Elsevier Science Bv, 2018) Xiang, Mengyuan; Rossi, Roberto; Martin-Barragan, Belen; Tarim, S. Armagan
    This paper addresses the single-item single-stocking location non-stationary stochastic lot sizing problem under the (s, S) control policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal (s, S) policy parameters. To tackle larger instances, we then combine the previously introduced MINLP model and a binary search approach. These models can be reformulated as mixed integer linear programming (MILP) models which can be easily implemented and solved by using off-the-shelf optimization software. Computational experiments demonstrate that optimality gaps of these models are less than 0.3% of the optimal policy cost and computational times are reasonable. (C) 2018 Elsevier B.V. All rights reserved.
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    Article
    Citation - WoS: 9
    Citation - Scopus: 11
    Confidence-Based Reasoning in Stochastic Constraint Programming
    (Elsevier, 2015) Rossi, Roberto; Hnich, Brahim; Tarim, S. Armagan; Prestvvich, Steven; Prestwich, Steven
    In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochastic constraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach. (C) 2015 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - WoS: 13
    Citation - Scopus: 17
    An Overview of Revenue Management and Dynamic Pricing Models in Hotel Business
    (Edp Sciences S A, 2018) Bandalouski, Andrei M.; Kovalyov, Mikhail Y.; Pesch, Erwin; Tarim, S. Armagan
    Basic concepts and brief description of revenue management models and decision tools in the hotel business are presented. An overview of the relevant literature on dynamic pricing, forecasting methods and optimization models is provided. The main ideas of the authors' customized revenue management method for the hotel business are presented.