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A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models

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A 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 ...

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In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this ...

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Where is the money ? A decomposition of monetary flows behind fossil fuels

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Transition from fossils to renewables is leading to radical societal changes. Shifting the capital from fossils to renewables is commonly accompanied with political concerns, such as energy autonomy, domestic employment etc. Despite a decreasing trend in r ...

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Assortment optimization using an attraction model in an omnichannel environment

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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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Computational tool for stock-constrained design of structures

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Data reduction for mixed integer linear programming in complex energy systems

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The task of optimally designing and scheduling energy systems with a high share of renewable energies is complex and computationally demanding. A widespread method for tackling this task is to apply mixed integer linear programming (MILP). Even though the ...

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