publications

2024

1. 

   Reverse Auctions with Transportation and Convex Costs

   Benjamin Heymann, and Alejandro Jofré

   Computational Management Science, Feb 2024

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   We discuss a procurement problem with transportation losses and piecewise linear production costs. We first provide an algorithm based on Knaster-Tarski’s fixed point theorem to solve the allocation problem in the quadratic losses case. We then identify a monotony condition on the types distribution under which the Bayesian cost minimizing mechanism takes a simple form.

   @article{heymannReverseAuctionsTransportation2024,
     dimensions = {true},
     title = {Reverse Auctions with Transportation and Convex Costs},
     author = {Heymann, Benjamin and Jofr{\'e}, Alejandro},
     year = {2024},
     month = feb,
     journal = {Computational Management Science},
     volume = {21},
     number = {1},
     pages = {20},
     issn = {1619-6988},
     doi = {10.1007/s10287-023-00498-4}
   }

2023

1. 

   Maximizing the Success Probability of Policy Allocations in Online Systems

   Artem Betlei, Mariia Vladimirova, Mehdi Sebbar, and 3 more authors

   Feb 2023

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   The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the industrial contexts such as online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.

   @article{betlei2023maximizing,
     dimensions = {true},
     title = {Maximizing the Success Probability of Policy Allocations in Online Systems},
     author = {Betlei, Artem and Vladimirova, Mariia and Sebbar, Mehdi and Urien, Nicolas and Rahier, Thibaud and Heymann, Benjamin},
     year = {2023},
     eprint = {2312.16267},
     archiveprefix = {arXiv},
     primaryclass = {cs.IR}
   }

2022

1. Kuhn’s equivalence theorem for games in product form

   Benjamin Heymann, Michel De Lara, and Jean-Philippe Chancelier

   Games and Economic Behavior, Feb 2022

2021

1. Causal models for real time bidding with repeated user interactions

   Martin Bompaire, Alexandre Gilotte, and Benjamin Heymann

   In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Feb 2021

2020

1. How to bid in unified second-price auctions when requests are duplicated

   Benjamin Heymann

   Operations Research Letters, Feb 2020

2018

1. Continuous optimal control approaches to microgrid energy management

   Benjamin Heymann, J Frédéric Bonnans, Pierre Martinon, and 3 more authors

   Energy Systems, Feb 2018
2. Optimal battery aging: an adaptive weights dynamic programming algorithm

   Benjamin Heymann, and Pierre Martinon

   Journal of Optimization Theory and Applications, Feb 2018

2017

1. Mechanism Design and Auctions for Electricity Networks

   Benjamin Heymann, and Alejandro Jofré

   Generalized Nash Equilibrium Problems, Bilevel Programming and MPEC, Feb 2017

2016

1. A stochastic continuous time model for microgrid energy management

   Benjamin Heymann, J Frédéric Bonnans, Francisco Silva, and 1 more author

   In 2016 European Control Conference (ECC), Feb 2016