Concept
Cutting-plane method
Related publications (197)
Learning to Remove Cuts in Integer Linear Programming
Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis
Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...
2024Reinforcement Learning for Joint Design and Control of Battery-PV Systems
Christophe Ballif, Marine Dominique Cauz, Laure-Emmanuelle Perret Aebi
The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...
2023A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling
In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...
2023Graphical solutions to one-phase free boundary problems
Xavier Fernandez-Real Girona, Hui Yu
We study viscosity solutions to the classical one-phase problem and its thin counterpart. In low dimensions, we show that when the free boundary is the graph of a continuous function, the solution is the half-plane solution. This answers, in the salient di ...
Berlin2023Quantitative Methods for Omnichannel Decision-Making
Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...
EPFL2023A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models
Michel Bierlaire, Claudia Bongiovanni
In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced dis ...
2022Assortment optimization using an attraction model in an omnichannel environment
Ralf Seifert, Andrey Vasilyev, Sebastian Maier
Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for ...
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