[Defended thesis] Gabriel Volte: Interactive exact optimisation for numerical services to agriculture

Interactive exact optimisation for numerical services to agriculture

• Starting date: October 2018
• University: University of Montpellier
• PhD school: I2S – Information Structures Systèmes
• Scientific field: IT
• Thesis management: Rodolphe Giroudeau, Université de Montpellier, Lirmm et Olivier Naud, Inrae, Itap
• Funding: #DigitAg – Université de Montpellier
• #DigitAg : Cofunded PhD – Axe 6 – Challenge 5

Keywords: Operational research applied to agriculture, decision computing, exact methods, decision support, farm management services

Abstract: The trend towards a precise, numerical, and data intensive agriculture brings forward the need to integrate in a unique decision support methodology optimization techniques that are efficient, interactive, robust and adaptable. We propose to develop a decision calculus strategy for the management of agricultural activity that combines the efficiency and precision of optimisation methods based on linear integer programming and heuristics, and the flexibility and modularity of constraint programming methods. With the perspective that custom decision support services should be offered to farmers, we make the hypothesis that historics of data, as well as daily updates of informations such as meteorology, crop evolutions and traceability informations should be available. This hypothesis allows for studying strategies that combine off-line (back office) approaches for searching best production processes that meet farmer criteria, and on-line (front office) approaches to contextualize and adapt solutions. We also make the hypothesis that distributed computing platforms could collaborate on calculus and numerical studies will be conducted on this topic, using available data and web services. In fine, the methodology should integrate stochastic features because of the uncertainties of agricultural production. A first characterization and evaluation of solutions for the current decision period can be based on models built from data historics. In a second step, it is planned to make use of probability estimators linked to meteorological forecast to offer robustness oriented interactivity to the user of decision support tool and decision maker.

Jury compound:

Rapporteurs:

Anass Nagih, Pr LCOMS (Laboratoire de Conception, Optimisation et Modélisation des systèmes, Université de Lorraine : http://lcoms.univ-lorraine.fr/content/nagih

Christelle Gueret Pr, Laboratoire Angevin de Recherche en Ingénierie des Systèmes, Université d’Angers :  http://perso-laris.univ-angers.fr/~gueret/

Examiners:

Ruslan Sadykov: CR INRIA, HDR : Institut de Mathématiques de Bordeaux, *Marie-Jo  Huguet : Pr LAAS, Institut National des Sciences Appliquées : https://homepages.laas.fr/huguet/drupal/node/11

Thesis management:

*Rodolphe Giroudeau MCF/HDR LIRMM/MAORE

Guests:

E. Bourreau MCF (LIRMM/MAORE), O. Naud (INRAE), F. Hernandez Ecole de Technologie Supérieure, Université du Québec

Contact:  
rodolphe.giroudeau [AT] lirmm.fr
olivier.naud [AT] inrae.fr

Communications & Papers

Download the thesis manuscript

Papers in international conferences

• Gabriel Volte, Eric Bourreau, Rodolphe Giroudeau, Olivier Naud (2019) Differential Harvest Problem, 20ème congrès annuel de la société Française de Recherche Opérationnelle et d’Aide à la Décision (ROADEF 2019)

• Gabriel Volte, Eric Bourreau, Rodolphe Giroudeau, Olivier Naud (2019) Column generation approach for the Differential Harvest Problem a parametric study, 12th European Federation for Information Technology in Agriculture, Food and the Environment (EFITA), Rhodes island, Greece, 27-29 June, 2019.