Modèles et méthodes pour la planification de la récolte forestière
Harvest planning has different levels according to the time horizon of the problem and the nature of the decisions to be taken. Initially, we are interested in an annual harvest scheduling problem, halfway between tactical and operational planning. This problem applied in Qu\'ebec, is motivated by a need from the industry for an integrated tool that provides annual schedules to harvest teams. The integration is to determine demand driven assignments of teams to cutblocks and to manage transportation and inventory accordingly.   Several MIP models have been formulated, and three solution approaches have been developed according to the structure of each model. The rolling horizon approach performs better than the other two, by improving significantly from the traditional harvest plan, especially by reducing by half non delivered volumes or by dividing between 2 and 6 times volumes in storage when demands decrease.  Another contribution of the thesis is the creation of an interface to systematize solution process and to allow other users. This is the object of a transfer project between academics and industry.  The second problem is a Chilean tactical harvest planning. Harvesting decisions are driven by stochastic demands and prices of final products. The stochastic problem is solved using a heuristic based on a scenario decomposition technique. The number of scenarios considered is between 10 and 100 scenarios.   For each scenario, when the deterministic solution is feasible, it is compared with the stochastic solution for the current scenario.  The deterministic solution is only feasible for 10% of the scenarios, and induces losses of 9% in average.
harvest scheduling value chain optimization tactical harvest planning rolling horizons column generation stochastic programming progressive hedging