No abstract

This paper oers a review of univariate and multivariate Process Capability Indices (PCIs). PCIs are used in the industry to quantify how well a process can meet customer requirements. Univariate PCIs describe the capability of one single product characteristic. Multivariate PCIs deal with the multivariate case in which the measures of all multiple product characteristics must be within specication limits to be conforming. When analyzing the capability of processes, decision makers of the industry may choose one PCI among all the PCIs existing in the literature, depending on dierent capability criteria. The aim of the review is to describe, cluster and discuss univariate and multivariate PCIs. This review may help researchers and decision makers to identify univariate and multivariate PCIs that can be used when monitoring univariate and multivariate production processes. On the one hand, the authors of this article suggest using PCIs obtained through the alternative denition for the C pk index when analyzing the capability of production processes, in which the estimation of the proportion of nonconforming parts is rated as crucial. On the other hand, all other multivariate PCIs presented in the literature can be applicable in capability analysis of production processes in which a direct relation to the proportion of nonconforming parts is not needed.

No abstract

The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: To identify the part of a complex production process that presents capability problems; to detect worsening over the time in multivariate production processes; and to compare similar production processes. Some illustrative examples based on dierent kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.

The stochastic models of systems with reverse logistics usually assume that the quantity of products returned is\ud independent of sales. This hypothesis is obviously not true and can lead to suboptimal production policies. In\ud this paper a new sales-dependent returns model is described. In this model, the returns depend on the useful\ud life of the products sold and on the probability of an end-of-life product being returned. A Markov decision\ud problem is formulated in order to obtain the optimal manufacturing policy. A numerical example is provided\ud to illustrate the use of the defined model. An approximated Markov decision model is defined where the\ud optimal policy is easily obtained. The optimal policies of the original and the approximated models are\ud compared.Peer ReviewedPostprint (published version

In this paper an algorithm for solving the Optimal Power Flow problem for multi-terminal DC networks based on the gradient method is proposed. The aim is seeking the optimal point subject to voltage, current and power constraints. The algorithm is described by a continuous-time port-Hamiltonian model, and the inequality constrains are included by the use of barrier functions. The dynamics of the algorithm is studied and stability conditions are obtained. Finally, the method is used for the offshore wind integration grid in the North Sea and the interconnection with the network dynamics is tested by means of numerical simulations.

No abstract

Supply chains (SC) aim to provide products to the final customer at a certain service level. However, unforeseen events occur that impede supply chain objectives. SC Risk has been studied in the literature, providing frameworks and methodologies to manage SC failures. Nevertheless, more efforts are needed to prevent hazardous and disruptive risks and their consequences. These risks must be considered during the process of designing a supply chain. Some methodological contributions concerning risk in the supply chain network design (SCND) are conceptual frameworks for mitigating SC disruptions, which suggest strategies and measures for designing robust and resilient SCs. Although such contributions are valuable, they do not indicate how to cope with risk when designing a SC. The main objective of this research is to describe a methodology aimed at including risk considerations into the SCND. Our proposal aims to be, on the one hand, a comprehensive approach that includes a risk identification and assessment procedure in each of the stages of the SCND process and, on the other hand, a tool for decision-making in SC design or redesign processes when SC risks need to be considered. The methodology proposed is an extension of a SCND methodology including risk considerations in order to improve the performance of the supply chains. A case study illustrates how the proposed methodological works, achieving the identification of SC risks already observed in previous works.