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Algorithmic Handling of Time Expanded Networks

Quilliot Alain, Figueroa Jose-Luis, Toussaint Hélène, Wagler Annegret

Annals of Computer Science and Information Systems

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2023

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Vol. 35

667--676

EN

Time Expanded Networks, built by considering the nodes of a base network over some time space, are powerful tools for the formulation of problems involving synchronization mechanisms. Those mechanisms may for instance be related to the interaction between resource production and consumption or between routing and scheduling. Still, in most cases, deriving algorithms from those formulations is difficult, due to both the size of resulting network structure and the fact that reducing this size through rounding techniques tends to induce uncontrolled essor propagation. We address here this algorithmic issue, while proposing a generic decomposition scheme which works by first skipping the temporal dimension of the problem and next expanding resulitng projected solution into a full solution of the problem set on the time expanded network.

2

Surrogate estimators for complex bi-level energy management

Bendali Fatiha, Mailfert Jean, Gonzales Alejandro Olivas, Quilliot Alain, Toussaint Hélène, Kamga Eloise Mole

Annals of Computer Science and Information Systems

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2022

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Vol. 32

85--92

EN

We deal here with the routing of vehicles in charge of performing internal logistics tasks inside some protected area. Those vehicles are provided in energy by a local solar hydrogen production facility, with limited storage and time-dependent production capacities. In order to avoid importing energy from outside, one wants to synchronize energy production and consumption in order ot minimize both production and routing costs. Because of the complexity of resulting bi-level model, we deal with it by short-cutting the production scheduling level with the help of surrogate estimators, whose values are estimated through fast dynamic programming algorithms or through machine learning.

3

Algorithms for the Safe Management of Autonomous Vehicles

Baïou Mourad, Mombelli Aurelien, Quilliot Alain, Adouane Lounis, Zhu Zhengze

Annals of Computer Science and Information Systems

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2021

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Vol. 25

153--162

EN

We deal here with a fleet of autonomous vehicles, devoted to internal logistics inside some protected area. This fleet is ruled by a hierarchical supervision architecture, which, at the top level distributes and schedules the tasks, and, at the lowest level, ensures local safety. We focus here on the top level, while introducing a time dependent estimation of the risk induced by the traversal of any arc. We set a model, state structural results, and design a bi-level algorithm and a A* routing/scheduling algorithms which both aim at a well-fitted compromise between speed and risk and rely on reinforcement learning.

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Pipe-lining Dynamic Programming Processes in Order to Synchronize Energy Production and Consumption

Bendali Fariha, Quilliot Alain, Mole Kamga Eloise, Mailfert Jean, Toussaint Hélène

Annals of Computer Science and Information Systems

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2020

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Vol. 21

303--306

EN

Synchronizing heterogeneous processes remains a difficult issue in Scheduling area. Related ILP models are in trouble, because of large gaps induced by rational relaxation. We propose here a pipe-line decomposition of a dynamic programming process for energy production and consumption scheduling, and describe the way related sub-processes interact in order to achieve efficient synchronization.