Miguel Zamarripa

Improving supply chain planning in a competitive environment

Abstract This work extends the use of a Mixed Integer Linear Programming (MILP) model, devised to optimize the Supply Chain planning problem, for decision making in cooperative and/or competitive scenarios, by integrating these models with the use of the Game Theory. The system developed is tested in a case study based in previously proposed Supply Chain, adapted to consider the operation of two different Supply Chains (multi-product production plants, storage centers, and distribution to the final consumers); two different optimization criteria are used to model both the Supply Chains benefits and the customer preferences, so both cooperative and non-cooperative way of working between both Supply Chains can be considered.

Toward integrated production and distribution management in multi-echelon supply chains

Abstract The effective management of multi-site systems involves the proper coordination of activities performed in multiple factories, distribution centers (DCs), retailers and end-users located in many different cities, countries and/or continents. To optimally manage numerous production and transportation decisions, a novel monolithic continuous-time MILP-based framework is developed to determine the best short-term operational planning to meet all customer requests at minimum total cost. The formulation lies on the unit-specific general precedence concept for the production scheduling problem whereas the immediate precedence notion is used for transportation decisions. To illustrate the applicability and potential benefits of the model, a challenging example corresponding to a supply chain comprising several locations geographically spread in six European countries has been solved to optimality with modest CPU times. Several scenarios with different logistics features were addressed in order to remark the significant advantages of using the integrated approach.

Optimal integration of third-parties in a coordinated supply chain management environment

A generic tactical model is developed considering third party price policies for the optimization of coordinated and centralized multi-product Supply Chains (SCs). To allow a more realistic assessment of these policies in each marketing situation, different price approximation models to estimate these policies are proposed, which are based on the demand elasticity theory, and result in different model implementations (LP, NLP, and MINLP). The consequences of using the proposed models on the SCs coordination, regarding not only their practical impact on the tactical decisions, but also the additional mathematical difficulties to be solved, are verified through a case study in which the coordination of a production–distribution SC and its energy generation SC is analyzed. The results show how the selection of the price approximation model affects the tactical decisions. The average price approximation leads to the worst decisions with a significant difference in the real total cost in comparison with the best piecewise approximation.

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.

Detailed Operation Scheduling and Control for Renewable Energy Powered Microgrids

Abstract The distributed paradigm is gaining popularity at the expense of the centralized production models inspired by the economy of scale. This includes energy transformation, storage and use, for which microgrids emerge as the most practical solution to interconnect and interoperate a network of energy dealers (typically known as producers and consumers, despite the conservation principle). Accordingly, the need for efficient management arises as a key issue not only in a specific planning or control level, but for all the hierarchy of decision levels. This work investigates the elements of a coordinated planning and control system for energy management in a microgrid.

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.

Application of Pricing Policies for Coordinated Management of Supply Chains

Abstract An optimization approach is proposed to coordinate multi-site multi-product SC networks taking into account the cooperation between suppliers and production/distribution SCs. For this purpose, all the interacting entities are integrated into the optimization model as full SCs, so any production/distribution echelon/SC can work as supplier for any other echelon/SC and so on. Financial issues based on price elasticity of demand, usually considered in these models just at the final SC echelon (end product), are incorporated in the proposed model at all interacting levels, so cost is subject to the trade-off between the price and the quantity demanded. Different approximations to model this demand elasticity have been tested, and the resulting NLP/MINLP models have been applied to a case study where the coordination of service (energy generation) SCs and production/distribution SCs is proposed. The results prove that pricing policies management add to PSE an additional instrument towards improving decision making.

Integration of Mathematical Programming and Game Theory for Supply Chain Planning Optimization in Multi-objective competitive scenarios

Abstract This work develops a multi-objective MILP (Mixed Integer Linear Programming) model, devised to optimize the planning of supply chains using Game Theory optimization for decision making in cooperative and/or competitive scenarios. Three different optimization criteria are considered (total cost, tardiness and expenses of the buyers for the competitive problem). The multi objective problem has been solved using the Pareto frontier solutions, and both cooperative and non cooperative scenarios between supply chains are considered, so multiple optimization tools/techniques have been combined to analyze the different trade-offs associated to the resulting decision making: Game Theory, MILP based approach and Pareto frontiers. The resulting model is tested in a case study, based on the operation of two different supply chains in both competitive and cooperative situations.

Integrated production and distribution management with cross docking in supply chains

Abstract In the current context of a global and very competitive economy, multiple production and distribution activities must be properly coordinated in order to satisfy strict market requirements at the right time and with minimum cost. In typical multi-site systems, products are usually manufactured in one or more factories, moved to warehouses for intermediate storage, and subsequently shipped to retailers or final consumers. In turn, cross-docking platforms may be also used to consolidate multi-product customer demands without storing at intermediate depots. Consequently, the effective operation of complex production and distribution networks involves the management of activities performed in multiple factories, distribution centers (DCs), retailers and end users, which are usually geographical spread in many different cities, countries and/or continents. To optimally manage such complex multi-site systems, an integrated MILP-based framework for production and distribution scheduling with cross-docking in supply chains is proposed. In order to illustrate the applicability and effectiveness of the proposed method, a complex example taken from literature is solved to optimality with modest CPU times.