Abstract
In this paper, we deal with a single-commodity location-allocation problem for a two-echelon logistic network. We present novel models with a new reading of the problem. Indeed, some classical models in literature seem in certain cases simplistic and then generate gaps between theoretical formalisms and the reality. Concretely, these gaps arise when setting up and calibrating these models, thus the identification of robust parameter values could prove difficult. The reason is the aggregation process of data, since it is naturally used in modeling, lacks to provide sufficient accuracy. In a pragmatic framework, the issue of logistic network design is rich and presents several possibilities for data apprehension in terms of details to be modeled. This motivates to construct a more realistic modeling approach. In this context, we propose a way for integrating a maximum precision degree without leaving the context of modeling a strategic issue. Actually, the question of designing a logistic network is substituted by the need to compute a logistic master plan mixing the tactical with the strategic. This becomes a major concern of businesses and this observation fits the importance of location-allocation problems. Two mixed integer programming (MILP) are proposed. A derived linear programming (LP) is identified and used to describe a perspective of a cooperative hybrid resolution approach based on Genetic Algorithm. Experimental study provides a comparison of the models results.