V Maniezzo - Promotion planning optimization
by Announcements of talks＠IDSIA
Speaker: Prof Vittorio Maniezzo, University of Bologna, Italy
Title: Promotion planning optimization
Abstract:
Retail trades greatly benefit from price promotions (promos), i.e., temporary price reductions. The marketing literature on the topic is vast, mainly under the heading “Trade Promotion Optimization”, but not much has been produced on the optimization of the schedule of promotions of brands or items on long time horizons. There is a rich offer of commercial packages to support these decisions, despite the rather poor coverage in the optimization literature of the many, intertwining operational constraints that characterize actual applications.
This work proposes a model for retailer chains, focused beyond transactional trade promotion management. It considers both manufacturers, who provide products to sell, and retailers, who are responsible for sales to the consumers. Input data to the model are derived from statistic analytics based on historical data, and yields the expected baseline and the uplift (ratio between the average sales volume with and without a promotion) for each promo in each time period, together with the different contributions to the uplift: cannibalization, halo, promotional dip, forward buying, etc. Building on this, we propose a mathematical model of the effectiveness of a promotion plan in the horizon of interest.
Scheduling a promotional plan consists in establishing the calendar, the discounts, and the mechanics of promotions over the time horizon, where the mechanics are possible means to enhance the simple price reduction, such as leaflets, hostesses, gifts, extra-display, etc.
A solution of the promotion scheduling problem determines the number of promotions in the plan, and for each promotion the starting and ending period, the sell-in and sell-out discounts, and its mechanics. A promo calendar specifies or permits to compute: sell in dates, sell in estimated volume, sell in promo price, promo mechanics, sell out dates, sell out estimated volume, retailer promo cost, retail price and retail margin.
The main constraints we included in the model are: minimum distance between promotions, maximum duration of each promotion, maximum number of promotions, minimum distance between promotions with the same discount or mechanics, maximum number of promotions with the same discount or mechanics, cost of the promotions in relation to the allocated budget, forbidden promotions in some periods.
We developed a full mathematical model and we compared different solution methods to solve instances of the promotion planning problem of real world complexity and size, including MIP, Dynamic Programming, Constraint Logic Programming and a matheuristic algorithm, namely a F&B heuristic, which is essentially an extension of beam search.
(Joint work with Marco Antonio Boschetti)
Date: October 3rd 2018, 16:00-16:30
Location: Manno, Galleria 1, 2nd floor, IDSIA meeting room
6 years, 3 months
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PIDSIA Seminar by Chris Schwiegelshohn (2nd time)
by Announcements of talks＠IDSIA
Speaker: Chris Schwiegelshohn
Title: On Coresets for Logistic Regression
Abstract:
Coresets are one of the central methods to facilitate the analysis of large
data sets. We continue a recent line of research applying the theory of
coresets to logistic regression.
First, we show a negative result, namely, that no strongly sublinear sized
coresets exist for logistic regression.
To deal with intractable worst-case instances, we introduce a complexity
measure $\mu(X)$, which quantifies the hardness of compressing a data set
for logistic regression. $\mu(X)$ has an intuitive statistical
interpretation that may be of independent interest.
For data sets with bounded $\mu(X)$-complexity, we show that a novel
sensitivity sampling scheme produces the first provably sublinear
$(1\pm\eps)$-coreset.
Our algorithms are viable in practise, comparing favorably to uniform
sampling as well as to state of the art methods in the area.
Joint work with Alexander Munteanu, Christian Sohler, and David Woodruff.
To appear at NIPS 2018.
Bio:
Chris Schwiegelshohn is currently a Researcher in Sapienza, University
of Rome. He did his Phd in Dortmund with a thesis on "Algorithms for
Large-Scale Graph and Clustering Problems". Chris' research interests
include streaming and approximation algorithms as well as machine
learning.
Date: 26th of September 2018, 12:00-13:00
Location: Manno, Galleria 1, 2nd floor, room G1-204
Registration: Pizza and drinks will be offered at the end of the talk.
If you plan to attend, please register in a timely fashion at the
following link so that we will have no shortage of food:
https://doodle.com/poll/sqa6idxyhf83ugba
6 years, 3 months
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