Planification réactive et robuste au sein d'une chaîne logistique
This work focuses on the supply chain tactical planning problem in an uncertain and disrupted environment. As part of point-to-point relationships, we propose an optimal and reactive tactical planning approach of a supply chain link in the presence of uncertain parameters and disturbances. Our approach is based on a two-level decision structure. The first level performs an aggregate planning which minimizes the overall production cost. It establishes "a guiding plan" which is transmitted to the detailed level. This latter performs its planning by following a guiding plan and by taking into account detailed constraints and data. The detailed level adopts a dynamic planning process with a rolling horizon. It updates its data at every planning step to ensure a reactive decision making. We characterize explicitly the inertia of a decision making system by distinguishing two decision phases: the anticipation phase and the realization phase. Each phase is described by a time delay. Thus, we propose an original model of decision making process in which every decision is modeled by three variables. The detailed level is formulated according to a linear program. At the aggregate level, a view of the detailed decisional process is integrated by work-in-progress constraints. We propose an aggregate model whose originality is to consider the specifics of the detailed decision process. The coupling between the two levels is provided by the guiding plan. According to aggregated data included in this plan, the aggregate level gives a specific autonomy to the detailed level which conditions the reactivity and the robustness of the detailed planning. In our work, we consider three types of guidance: two "global" budget guidings and a more "precise" subcontracting aggregate guiding. Our approach is evaluated by simulation under uncertain demand. For this we develop two simulation tools and a set of performance indicators. The experiments carried out confirm the performance of our approach over conventional approaches and highlight the influence of the guiding and the detailed demand profile on the reactivity and robustness of the solutions.