סמינר המחלקה להנדסת תעשייה
A Deep Learning Model for Post Harvest Grape
Yotam Givati, M.Sc. student at the Department of Industrial Engineering at Tel Aviv University
The lecture will beheld on
Tuesday, October 19, 2021, at 14:00
Preventing food waste has always been major objective in food distribution and the food retail industry. Yet, it is
estimated that in the US about 40\% of the food supply or 36 million tons are being wasted. When it comes to
storage of fresh produce like fruits and vegetable, the leading distribution method is First In First Out (FIFO). In
FIFO the produce is marketed according to its storage time regardless of any other parameter at storage time.
However, recent advances in digitization, data collection and machine learning allow for improvements in
traditional strategic and planning methodologies.
In this research, we present a novel model for predicting table grapes quality prior to storage. The proposed model
predicts grapes quality based on different features that are measured right after the harvest such as rachis score,
berries firmness, sugar content level, weight, storage temperature, color, etc. Our model utilizes a neural network in
order to map these features into a latent dense representation that removes redundancies while preserving
information related to the future quality of the produce. Then, a multi-objective prediction task is perform in order
to estimate several criteria of grapes quality. Our evaluations showcases the superiority of the proposed model w.r.t
Yotam Givati is an MSc student at the department of Industrial Engineering at Tel-Aviv University, who is writing
a thesis under the supervision of Dr. Noam Keonigstein. Yotam holds a B.sc degree in Industrial Engineering and
management sciences from Tel Aviv University. Previously, Yotam worked as a BI Analyst at Google (Waze),
helped to manage data storage and preform campaigns and feature analysis, participated in designing and execute
An Optimal Policy for Managing Ad Campaign in a Social Network
Adam Bartash, M.Sc. student at the Department of Industrial Engineering at Tel Aviv University
The lecture will be held on
Tuesday, October 19, 2021, at 14:30
Marketing has been a part of humanity for centuries. Over the years it has developed and changed. Today, marketing has become an intrinsic part of every company. In the digital age, one of the means of marketing is advertising on the social media platforms such as Facebook, Google, Instagram, Pinterest, etc.
As advertising has always played an important role in any business, marketing analysts constantly try to figure out how to invest their marketing budget in order to maximize their KPI's. As literature shows, one of main ways to maximize KPI's is to have clear objectives prior to advertising. Having precise objectives will allow the analysts to allocate their advertising budget in a more efficient way when a target function is defined prior to advertising. In this research we will present 4 different scenarios of target functions and will analyze the algorithms that will determine budget allocation for each to maximize the target function. This heuristic will facilitate marketers to base their future budget allocation in a way that fits their business needs.
Adam Bartash is an MSc student at the department of Industrial Engineering at Tel-Aviv University, who is writing a thesis under the supervision of Prof. Eugene Khmelnitsky. Adam holds a B.sc degree in Industrial Engineering and management sciences from Tel Aviv University. Previously, Adam worked as a Marketing Analyst at Kendago, led high budget PPC Facebook campaigns and analyzed them, was responsible for marketing research and identified new tactics to improve conversions and maximize ROI. Today, Adam is an Analytical Lead at Facebook, and works closely with most strategic clients in the Entertainment and Media vertical. In addition, Adam is a teaching assistant of the Operational Innovation course, in the Industrial Engineering faculty in Shenkar.