Javier Silvente

Tactical management for coordinated supply chains

Abstract Current supply chain (SC) optimization models deal with material and information flows along few echelons of the SC (“own SC”), minimizing the role of the complex behavior of third parties (raw materials and utilities suppliers, clients, waste and recovery systems, etc.) in the decision-making process of this SC of interest. Third parties are just represented by simplified parameters (capacity, cost, etc.) usually considered constant, but the decisions based on this picture are not adequate when the third parties’ behavior is significantly affected by these decisions or other circumstances, especially when global coordination is attained. In this work, the role of these third parties, which might face different objectives, has been integrated and a solution based on the full SC management problem is proposed. This results on a generic model which may be used to optimize the planning decisions of the multi-product multi-site SC of interest (production/distribution echelons), taking into account the production vs. demand coherence among this SC and the third parties. The features of the proposed model are illustrated using a case study which considers the coordination of a series of resource (energy) generation SCs linked to a production/distribution SC (“SC of interest”). The results show how the behavior of the considered SCs determines the best planning decisions of each organization, which will depend on the way used to coordinated them (e.g. toward less total or individual costs), adding to the PSE science a new point of view which allows all involved organizations to share responsibilities in the system.

Hybrid time representation for the scheduling of energy supply and demand in smart grids

Abstract 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.

Supply chain planning under uncertainty using genetic algorithms

Abstract The solution of a mixed integer linear programming (MILP) model describing the main characteristics of the basic Supply Chain Management (SCM) problem is attained using different procedures. The use of genetic algorithms is proposed as a computing efficient alternative to deal with the combinatorial explosion of alternatives associated to the consideration of different production scenarios, which is a requirement if, as usual, the basic planning information is just estimated with a significant degree of uncertainty.

Reactive Scheduling for the Coordination of Energy Supply and Demand Management in Microgrids

Abstract This work focuses on the development of scheduling strategies for the coordination of energy production and consumption tasks in microgrids through the management of flexible demand profiles. Delays in the nominal energy demands are allowed under associated penalty costs. The basic microgrid structure studied consists of renewable energy generators (photovoltaic panels, wind turbines) and energy storage units that alleviate the main drawback of such systems which is the mismatch between energy production and demand. A mathematical formulation is presented and used in a rolling horizon scheme that periodically updates input data information.

Use of a distributed simulation environment for training in Supply Chain decision making

Abstract Tactical and Operational decision making requires considering a large amount of data, which must be properly stored and interpreted. In this context, an on-line information system has been developed to allow the integration of real time reactive tools, which can constitute a useful operator support in the event of process and/or scenario disturbances. The use of this integrated system as a training tool over a simulated Supply Chain scenario has proved to help the Process Systems Engineering students to improve the information comprehension capabilities, as well as to enhance the way they apply their knowledge to plant and process optimization.

A promising OPC-based computer system applied to fault diagnosis

Abstract Fault detection and diagnosis is a challenging problem for plant economics and safety. In this context, a promising OPC-based modular architecture for a Fault Diagnosis System (FDS) is designed and implemented. This FDS has been validated by performing on-line real-time diagnosis on a simulated process. The modular architecture allows openly connecting a simulator or a real process via OPC. The Tennessee Eastman Process (TEP) is used as data generator and case study, so that several abnormal operation conditions can be diagnosed by the system. The proposed architecture is discussed regarding the integration of future modules for the timely adoption of appropriate corrective actions.

Simplified model for integrated Supply Chains Planning

Abstract 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 SCs 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.

Knowledge-based Approach for the Integration of the Planning and Scheduling Decision-making Levels

This work aims to integrate the tactical and operational decision making levels. A typical Scheduling mixed integer linear programming (MILP) model has been solved using several demand scenarios. The results have been analyzed and accordingly the operation cost vs. production levels nonlinear equations have been obtained. The aforementioned equations have been included as constraints of the SC planning problem. Hence, production, inventory and distribution variables along the complete SC have been optimized using a NLP model.

Optimal management of microgrids under uncertainty using scenario reduction

Abstract This work addresses the optimal management of a microgrid through a two-stage Mixed Integer Linear Programming (MILP) approach. This approach uses a scenario-based mathematical formulation to deal with uncertain information. Sufficient representation of uncertainty usually requires increased number of scenarios, which result in large-scale optimisation models. This paper investigates the use of scenario reduction techniques to reduce computational requirements while maintaining good quality of the final optimal solution. The proposed approach is then applied to optimally manage the heat demand uncertainty within a microgrid so as to minimise the operational cost while considering electricity and heat generation, purchases, sales, storage and demand profiles.