José M. Laínez-Aguirre

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.

Scenario-based dynamic negotiation for the coordination of multi-enterprise supply chains under uncertainty

Abstract A novel scenario-based dynamic negotiation approach is proposed for the coordination of decentralized supply chains under uncertainty. The relations between the involved organizations (client, provider and third parties) and their respective conflicting objectives are captured through a non-zero-sum and non-symmetric roles SBDN negotiation. The client (leader) designs coordination agreements considering the uncertain reaction of the provider (follower) resulting from the uncertain nature of the third parties, which is modeled as a probability of acceptance function. Different negotiation scenarios are studied: (i) cooperative, and (ii) non-cooperative and (iii) standalone cases. The use of the resulting models is illustrated through a case study with different vendors around a “leader” (client) in a decentralized scenario. Although the usual cooperation hypothesis will allow higher overall profit expectations, using the proposed approach it is possible to identify non-cooperative scenarios with high individual profit expectations which are more likely to be accepted by all individual partners.

Knowledge Management to Support the Integration of Scheduling and Supply Chain Planning using Lagrangean Decomposition

The complexity of integrated planning and scheduling models can be tackled with decomposition techniques based on duality and information flows between a master and a set of subproblems. Hence, the information sharing and communication of information from the industrial environments requires flexible structures, facilitating the use of analytic tools and providing higher flexibility for model building in industrial environments. In this work, an ontological framework is proposed to allow the virtualization of systems and processes and to implement a novel Lagrangean decomposition scheme based on hierarchical level decomposition. Indeed, the scheduling and planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.

Strategic Planning of Biomass Supply Chain Networks for Co-combustion Plants

Abstract An optimization-based supply chain design-planning approach is applied to process seasonal and highly distributed biomass waste to fulfill the demand of an existing park of coal power plants. Economic and environmental criteria are integrated in the resulting mixed integer linear program mathematical model: the net present value, measured in €2010, and life cycle assessment (LCA) through the Impact 2002+ method. Input data comprise suppliers geographically distributed and facility locations, the alternatives for technological equipment (e.g., pretreatment technologies), characterization of biomass properties, and models granularity. The model suggests the location and connectivity between providers and consumers, and pretreatment units capacities. The size of the optimization problem requires large computational time. To reduce the calculation time, a novel Lagrangian decomposition method is applied. A large retrofit case study is solved using the aforementioned decomposition scheme to demonstrate the capabilities of the proposed approach.

A stochastic programming approach for the Bayesian experimental design of nonlinear systems

Abstract Several approaches for the Bayesian design of experiments have been proposed in the literature (e.g., D-optimal, E-optimal, A-optimal designs). Most of these approaches assume that the available prior knowledge is represented by a normal probability distribution. In addition, most nonlinear design approaches involve assuming normality of the posterior distribution and approximate its variance using the expected Fisher information matrix. In order to be able to relax these assumptions, we address and generalize the problem by using a stochastic programming formulation. Specifically, the optimal Bayesian experimental design is mathematically posed as a three-stage stochastic program, which is then discretized using a scenario based approach. Given the prior probability distribution, a Smolyak rule (sparse-grids) is used for the selection of scenarios. Two retrospective case studies related to population pharmacokinetics are presented. The benefits and limitations of the proposed approach are demonstrated by comparing the numerical results to those obtained by implementing a more exhaustive experimentation and the D-optimal design.

Management Coordination for Multi-Participant Supply Chains Under Uncertainty

A game decision support tool is developed to suggest the best conditions for the coordination contract between different stakeholders with conflictive objectives in a multi-participant Supply Chain (SC). On the base of dynamic games, the interaction between the involved stakeholders is modeled as a non-cooperative non-zero-sum Stackelberg’s game under the leading role of one of the partners. The leader designs the first game move (price offered) based on its optimal conditions and taking into consideration the uncertain conditions of the follower. Consequently, the follower responds by designing the second move (quantity offered at this price) based on its best current/uncertain conditions, until the Stackelbergs payoff matrix is built. The expected follower payoffs are obtained taking into consideration the risks associated with the uncertain nature of the 3rd party suppliers. Results are verified on a case study consisting of different providers SC around a client SC in a global decentralized scenario. The results show improvements in the current/expected individual profits in the SCs of both leader and follower when compared with their standalone cases.

Operations Research Ontology for the Integration of Analytic Methods and Transactional Data

The solution of process systems engineering problems involves their formal representation and application of algorithms and strategies related to several scientific disciplines, such as computer science or operations research. In this work, the domain of operations research is modelled within a semantic representation in order to systematize the application of the available methods and tools to the decision-making processes within organizations. As a result, operations research ontology is created. Such ontology is embedded in a wider framework that contains two additional ontologies, namely, the enterprise ontology project and a mathematical representation, and additionally it communicates with optimization algorithms. The new ontology provides a means for automating the creation of mathematical models based on operations research principles.

Decision-Support System for Integration of Transactional Systems and Analytical Models in the Pharmaceutical Industry

Pharmaceutical industry is a highly competitive and global business, which requires sophisticate tools efficient decision-making. Decision-support tools rely on both robust information systems and accurate analytical tools capable of capturing and solving engineering and management problems. This paper presents a decision-support framework that aims to bridge the gap between transactional and analytical systems for the pharmaceutical industry. Specifically, the developed framework allows creating information quality from existing information systems, to automatically deliver it to the optimization models, and to provide optimization results for final implementation.

Enterprise-wide scheduling framework supported by knowledge management

The scheduling function determines the amount of each product to produce, the allocation of equipment and resources to tasks, as well as the sequencing and timing of such tasks, in order to fulfill plan production objectives. A knowledge-based model has been created relying on rigorous analysis of existing process scheduling models. Likewise, a reasoning tool has been developed for matching the system and scheduling task features to previously existing mathematical models. As a result, a model selection can be obtained based on features matching.