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The scope of any Supply Chain management strategy usually takes into account the few single echelons directly linked to the process of interest (raw materials acquisition, market distribution,...) which are assumed to present a previously known behaviour (even this behaviour may include some uncertainty). Decisions based on this limited picture disregard the important information associated to the interaction among different cooperative SCs. This work aims to optimize the overall performance of several SC's in a cooperative scenario acting as an “entire SC”. Accordingly, the main features of this entire SC (raw materials SC, production-distribution SC, products and wastes) have been considered. The approach is demonstrated using a case study which integrates an energy poligeneration SC model (RM acquisition, and different production systems in a competitive situation) and the traditional production-distribution SC model (RM acquisition, distribution to production plants, production, and distribution to markets) in a mixed integer non-linear programming model.

The basis of decision-making in the enterprise consists of formally representing the system and its subsystems in a model which adequately captures those features which are necessary to reach consistent decisions. This works aims at integrating the elements of the enterprise (i.e., decisions, parameters, constraints, performance indicators) which are included in mathematical models to a semantic representation of the enterprise by the creation of an ontology which captures the meaning of the mathematical language. As a result, the integration of decisions in enterprise may be achieved. The purpose of this work is illustrated in a case study related to plant capacity in the supply chain and scheduling problems.

A new optimization model is presented for the short-term management of the energy supply and demand in smart grids. The detailed model includes a flexible demand profile in order to manage the energy requirements by incorporating penalizations in the economic objective function for delays in satisfying energy demand. The MILP model for the optimization of deterministic scenarios is reformulated in order to incorporate discrete and hybrid time representations. This approach allows considering a different granularity of the problem. Finally, the improved performance of the hybrid approach introduced is shown by comparing the performance of these two time representations.