Research at SyMDA Lab is involved with developing analytical models for solving decision making and planning
problems associated with large and complex systems.
Frenk, J.B.G., Javadi, S., Pourakbar M., Sezer S.O. An exact static solution approach for the
service parts end-of-life inventory problem. European Journal of Operational Research, Volume 272,
Issue 2, 16 January 2019, Pages 496-504. https://doi.org/10.1016/j.ejor.2018.06.041
Burak Kocuk & Gérard Cornuéjols (2019). Incorporating Black-Litterman Views in
Portfolio Construction when Stock Returns are a Mixture of Normals, Omega,
accepted. https://doi.org/10.1016/j.omega.2018.11.017
Burak Kocuk, Santanu S. Dey & X. Andy Sun (2018). Matrix Minor Reformulation and
SOCP-based Spatial Branch-and-Cut Method for the AC Optimal Power Flow Problem, Mathematical
Programming Computation,10 (4), 557-596. https://doi.org/10.1007/s12532-018-0150-9
Noyan, N., Rudolf, G. (2018). Optimization with stochastic preferences based on a general class
of scalarization functions, Operations Research, 66(2): 463-486.http://dx.doi.org/10.1287/opre.2017.1671
Atakan, S., Bülbül, K., Noyan, N. (2017). Minimizing Value-at-Risk in Single Machine Scheduling
Problems, Annals of Operations Research, 248(1), 25-73. http://dx.doi.org/10.1007/s10479-016-2251-z
Feyzioğlu, O., Noyan, N. (2017). Risk-averse Toll Pricing in a Stochastic Transportation
Network,European Journal of Industrial Engineering, 11 (2): 133-167. http://dx.doi.org/10.1504/EJIE.2017.083248
Noyan, N., B. Balçık, S. Atakan, 2016. A Stochastic Optimization Model for Designing Last Mile
Relief, Transportation Science, 50 (3), 1092-1113. https://dx.doi.org/10.1287/trsc.2015.0621
J.B.G.Frenk, Behrooz Pourghannad, Semih O.Sezer, A static model in single-leg flight airline
revenue management, Transportation Science 51(1), 214-232, 2017. https://doi.org/10.1287/trsc.2016.0695
Aliabadi D.E., Kaya M., Şahin G. (2017). An agent-based simulation of power generation company
behavior in electricity markets under different market-clearing mechanisms, Energy Policy
100:191-205. [https://doi.org/10.1016/j.enpol.2016.09.063]
Aliabadi D.E., Kaya M., Şahin G. (2017). Competition, risk and learning in electricity markets: An
agent-based simulation study, Applied Energy 195:1000-1011.[https://doi.org/10.1016/j.apenergy.2017.03.121]
Aliabadi D.E., Kaya M., Şahin G. (2016). Determining collusion opportunities in deregulated
electricity markets, Electric Power Systems Research 141:432-441. [https://doi.org/10.1016/j.epsr.2016.08.014]
Aliabadi, Danial Esmaeili (2016). Analysis of collusion and competition in electricity markets using
an agent-based approach (Supervisors: Güvenç Şahin and Murat Kaya).
On the End-of-life Inventory Problem
We consider the so-called End-of-Life inventory problem for a manufacturer of spare parts in
the final phase of the service life cycle. The final phase starts when the part production is
terminated and continues until the last service contract expires. One of the most popular
tactics to cope with this problem is to place a sufficient volume of spare parts at the
beginning of the final phase which is called the final order quantity. Then the
repair-replacement policy serves the costumers by repairing or replacing the defective items.
On the other hand, nowadays, a considerable price erosion happens for the products while
repair and service costs stay steady over time. If so, it is more cost effective to consider
an alternative policy to meet the service demands after some time. This policy may offer the
costumers a new product of similar type or a discount on a next generation product. In this
setup, the purpose is to find an optimal pair of final order quantity and switching time to an
alternative policy which minimizes the total expected discounted costs. We study this problem
under the static and dynamic approaches which require different mathematical techniques.
Estimating The Number of Product Failures: A Theoretical Approach.
In this thesis, we propose a stochastic process describing the total number of failed items
under warranty over time. This stochastic process consists of a sales process represented by a
stochastic point process and a process counting the total random number of repairs applied to
an arbitrary item of this product. Combining these two stochastic processes yields a
representation of the counting process of the total random number of failed items returned to
the manufacturer within theirwarranty period. To fit the proposed parametric model to a large
data set we need to estimate separately the intensity measure of both the failure and sales
process. To estimate the intensity measure of the cumulative sales process we use some well
known parametric functions and apply linear regression techniques. Also, under the assumption
that a repair does not change the age of the particular item of the product it can be shown
that the counting process of failures is a non-homogenous Poisson process and so we need to
estimate the cdf of the time to the first failure. Since our data set is censored we apply the
Maximum Likelihood principle for censored data and use as a parametric class the class
ofWeibull distributions. Our approach serves as an alternative to the time series based
approaches for cases where item tracking information is available.
On A Dynamic Pricing Model With A Possibility To Exit The Market
Taking pricing decisions over time is an important tool to maximize profit in revenue
management. In most of the literature with dynamic pricing and stochastic demand, costs are
considered as fixed components independent of the pricing policy. Due to the fact, exiting the
market is not included as an option in these models. Next to revenue through sales, in this
thesis we comprise inventory holding cost which leads staying in the market to be costly.
Therefore, we consider the possibility to exit the market before the season ends. In
particular, we deal with the problem of selling a seasonal product in a retail store over a
finite sales season. Initial order quantity is also a decision variable; hence, we consider
ordering cost per item. During the season, inventory replenishment or backlogging is not
allowed. In continuous time demand model which is our proposed model, Poisson sales process is
assumed with arrival rate function depending on both the time of arrival and the price of the
product. At predetermined decision moments known at the beginning, the supplier has to decide
either staying in the market and adjusting the price or exiting the market and selling the
leftover inventory at a certain salvage value. We formulate both our proposed model and
discrete time demand model by dynamic programming techniques. Static version of our proposed
model is also provided. For numerical experiments, we investigate the sensitivity of the
optimal pricing policy with respect to different problem parameters of a given base scenario.
Detailed information about Industrial Engineering Graduate Programs (MS and PhD) can be found at the
following website: iegrad.sabanciuniv.edu
We offer Full Scholarship (tuition waiver, stipend and dorm) to outstanding applicants.
In addition, we have Project Scholarship positions available for the following projects:
PhD Position: Strategic Planning for Transition to Next Generation Clean Energy Technologies using
Multi-Stage Stochastic Programming (PI: Burak Kocuk)
MS Position: Machine Learning Applications in Bundle Pricing Problem to Learn Product Valuations (PI: Ezgi Karabulut Türkseven)
Preventing Tacit Collusion In Deregulated Electricity Markets
The goal of deregulated electricity markets is to provide consumers with affordable electricity
prices by sustain competition among electricity generators. Although market- clearing mechanisms are
to attain perfect competition, electricity generators exploit the deficiencies of such mechanism in
order to decrease the level of competition in the market against public welfare. Independent system
manager and/or operator are responsible for administering the market; in the interest of public
welfare, they aim to prevent tacit collusion among generators that may decrease the level of
competition in the market and even create an oligopolistic environment. In this project, we aim to
study analytical optimization models to suggest changes in the market parameters so that system
manager and system operator prevent tacit collusion.
In the scope of this study, decision processes of system manager, system operator and electricity
generators are considered in an integrated manner. In order to determine the existence of tacit
collusion, strategic behavior of generators should be analyzed. We assume that generators’ actions in
the day-ahead market are a reflection of their strategies. System operator takes into account the
actions of generators to clear the market and determine electricity prices. System manager determines
the parameters and conditions of the market under which generators determine their bids.
In the first phase of our study, we study the problem to determine the existence of tacit collusion
in the market. The market’s decision process where generators determine their bids in order to
maximize their profit while the system operator allocates power and determine locational electricity
prices is represented as a bi-level optimization problem. The resulting optimization problem is a
bi-level multi-criteria problem with non-linear terms. In the second phase of our study, decisions of
the system manager to prevent collusion among generators are integrated as the top level decision
process. As a result, we obtain a tri- level optimization problem with a bi-level sub-problem which is
already complex and difficult to solve.
The ultimate goal of our study is to develop an algorithm with reasonable computational complexity to
solve the tri-level optimization problem in which the system manager aims to prevent tacit collusion.
Although our primary goal is to solve the problem to optimality, we may also study heuristic methods
for this problem. Our methodological approach requires firstly solving the bi-level problem in the
first phase of our study. In this respect, we plan to develop an exact solution approach for the
first-phase problem.
ABOUT
“SML Lab aims at facilitating and stimulating collaboration among faculty,
students, and practitioners to foster continuous learning and translating knowledge into innovative
solutions for making transport and logistics more efficient, smarter, greener, and safer.”
Transport and logistics planning is extremely important in particular for Turkey.
Current development plans highlight the need for modern and reliable transportation systems, and aim
at transforming Turkey to a global logistic hub, both for materials and energy flows, at the
crossroads of three continents. Moreover, the traffic volume in all sorts of transport modes has
increased rapidly, particularly in metropolitan areas like Istanbul. There have been a lot of
investments to improve the infrastructure. Thus, the efficient use and planning of the resources is an
important and challenging task. Moreover, developing effective plans is essentially a national
priority due to the high likelihood of serious natural disasters.
Smart Mobility and Logistics Lab (SML) in Sabancı University focuses on transport
logistics and mobility planning including urban transport, first-mile, long-distance and last-mile
pickup/delivery operations, humanitarian logistics, electro-mobility, and sustainable logistics
chains. SML team is equipped with extensive domain knowledge in logistics and transportation research
and experienced in addressing multifaceted problems through systematic modeling approaches and
effective solution methods using operations research tools and techniques. The Lab conducts research
projects particularly on urban mobility, humanitarian logistics, and sustainable transport planning
with a special emphasis on route optimization, electrification of logistics vehicles, battery
performance analysis.
