The ability to ensure an efficient inventory management, production schedules and distribution strategies allow companies to meet customer demand while minimizing costs and maximizing profitability. The work presented in this paper focuses on the dynamic inventory management from control theory perspective. The proposed approach employs the so-called "Model-Free Control (MFC)" concept, where the use of any supply chain model becomes useless. Application to a real case of multi-nodes petrochemical supply chain system as well as a comparative studies allow to demonstrate the relevance of the role of this control theory in the frame of supply chain management.

This paper investigates the controller design for a robust and efficient supply strategy for logistic constrained systems with perishable goods subject to uncertainties on the demand and the process delays. Here, instead of model-based control strategies, through a continuous, local identification of the level variations and forecasting via the setting of time series, an adaptive model-free control that does not need any prior knowledge of the supply chain behaviors is developed. The proposed approach takes into accounts not only the decay of goods stored in warehouse (perishing inventories) and prevents from exceeding the facilities capacities but also ensures full demand satisfaction irrespective of the value of delay and reacting significantly faster to the consumer's demand uncertainty. Quantitative analyzes prove a strong reduction of the storage cost, increase of the profit and bullwhip effect attenuation.

The aim of real-time Dynamic Trafic Routing (DTR) is to find the time-dependent split variables at the diversion point for routing the incoming traffic flow onto the alternate routes in order to reach a user equilibrium traffic pattern. This paper exploits the recent advances on model-free control and proposes a robust and easy implement able algorithm to deal with the DTR problem in the case of non-recurrent congestions. The provided simulations show the relevance of the developed approach.

In the frame of supply chain networks, several approaches stemming from control theory seem to be more adapted to deal with dynamic inventory management. Nevertheless, the used methods, until today, are model-based control strategies where supply chain models play a major role. Due to the increasing complexity of such systems, the modeling of supply chains becomes more difficult and fails to capture all the dynamic behavior of the supply chain networks. This paper proposes as an alternative to these approaches a model-free control method and its corresponding intelligent controllers for inventory control in supply chain. Several concrete numerical simulations and comparative studies mainly with the internal model control show the efficiency of the approach and promising future of the obtained results even in the presence of various disturbances.

Supply chain management and inventory control provide most exciting examples of control systems with delays. Here, Smith predictors, model-free control and new time series forecasting techniques are mixed in order to derive an efficient control synthesis. Perishable inventories are also taken into account. The most intriguing “bullwhip effect” is explained and attenuated, at least in some important situations. Numerous convincing computer simulations are presented and discussed.

Dynamic supply chain management is tackled, in this paper, using the concept of differential flatness. Such approach seems to be more adequate with supply chains, which are complex and meshed systems. The paper focuses on the operational activities of supply-chains, the main objective is to maintain the inventory level at a desired one according to the customer demand while synchronizing the flows within a set of physical boundaries and constraints. The main advantage of differential flatness is characterized by the fact that the whole system behavior is described by the trajectory of a so-called “flat output” and a number of its successive time derivatives. This leads to a simple design of the control algorithm, without integration of any differential equation. Convincing numerical simulations, using a case study of a multi-product polyethylene petrochemical plant, demonstrate the effectiveness of the proposed approach.

Hybrid traffic modeling and simulation provide an important way to represent and evaluate large-scale traffic networks at different levels of details. The first level, called "microscopic" allows the description of individual vehicles and their interactions as well as the study of driver's individual behavior. The second, based on the analogy with fluidic dynamic, is the "macroscopic" one and provides an efficient way to represent traffic flow behavior in large traffic infrastructures, using three aggregated variables: traffic density, mean speed and traffic volume. An intermediate level called "mesoscopic" considers a group of vehicles sharing common properties such as a same origin and destination. The work conducted in this paper presents a first step allowing simulation of wide area traffic network on the basis of dynamic hybrid modeling, where the representation associated to a network section can change at runtime. The proposed approach is implemented in a simulation platform, called Jam-free.