Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Complex City Systems

Information and communications technology (ICT) is being exploited within cities to enable them to better compete in a global knowledge-based service-led economy. In the nineteenth and twentieth centuries, cities exploited large technical systems (LTSs) such as the telegraph, telephony, electrical networks, and other technologies to enhance their social and economic position. This paper examines how the LTS model applies to ICT deployments, including broadband network, municipal wireless, and related services, and how cities and city planners in the twenty-first century are using or planning to use these technologies. This paper also examines their motivations and expectations, the contribution to date, and the factors affecting outcomes. The findings extend the LTS model by proposing an increased role for organizations with respect to an individual agency. The findings show how organizations form themselves into networks that interact and influence the outcome of the system at the level of the city. The extension to LTS, in the context of city infrastructure, is referred to as the complex city system framework. This proposed framework integrates the role of these stakeholder networks, as well as that of the socioeconomic, technical, and spatial factors within a city, and shows how together they shape the technical system and its socioeconomic contribution. The CCS framework has been presented at Digital Cities Conferences in Eindhoven, Barcelona, Taiwan, London and at IBM’s Global Smart Cities Conference in Shanghai between 2010 and 2012. Its finding are timely in the context of major policy decisions on investments at regional, national and international level on ICT infrastructure and related service transformation, as well as the governance of such projects, their planning and their deployment

CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference

The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world

Agglomeration externalities, innovation and regional growth: Theoretical perspectives and meta-analysis

Technological change and innovation and are central to the quest for regional development. In the globally-connected knowledge-driven economy, the relevance of agglomeration forces that rely on proximity continues to increase, paradoxically despite declining real costs of information, communication and transportation. Globally, the proportion of the population living in cities continues to grow and sprawling cities remain the engines of regional economic transformation. The growth of cities results from a complex chain that starts with scale, density and geography, which then combine with industrial structure characterised by its extent of specialisation, competition and diversity, to yield innovation and productivity growth that encourages employment expansion, and further urban growth through inward migration. This paper revisits the central part of this virtuous circle, namely the Marshall-Arrow-Romer externalities (specialisation), Jacobs externalities (diversity) and Porter externalities (competition) that have provided alternative explanations for innovation and urban growth. The paper evaluates the statistical robustness of evidence for such externalities presented in 31 scientific articles, all building on the seminal work of Glaeser et al. (1992). We aim to explain variation in estimation results using study characteristics by means of ordered probit analysis. Among the results, we find that the impact of diversity depends on how it is measured and that diversity is important for the high-tech sector. High population density increases the chance of finding positive effects of specialisation on growth. More recent data find more positive results for both specialization and diversity, suggesting that agglomeration externalities become more important over time. Finally, primary study results depend on whether or not the externalities are considered jointly and on other features of the regression model specification

Business Process Management Education in Academia: Status, challenges, and Recommendations

In response to the growing proliferation of Business Process Management (BPM) in industry and the demand this creates for BPM expertise, universities across the globe are at various stages of incorporating knowledge and skills in their teaching offerings. However, there are still only a handful of institutions that offer specialized education in BPM in a systematic and in-depth manner. This article is based on a global educators’ panel discussion held at the 2009 European Conference on Information Systems in Verona, Italy. The article presents the BPM programs of five universities from Australia, Europe, Africa, and North America, describing the BPM content covered, program and course structures, and challenges and lessons learned. The article also provides a comparative content analysis of BPM education programs illustrating a heterogeneous view of BPM. The examples presented demonstrate how different courses and programs can be developed to meet the educational goals of a university department, program, or school. This article contributes insights on how best to continuously sustain and reshape BPM education to ensure it remains dynamic, responsive, and sustainable in light of the evolving and ever-changing marketplace demands for BPM expertise

Voluntary Turnover and Job Performance: Curvilinearity and the Moderating Influences of Salary Growth, Promotions, and Labor Demand

In this study we investigated the relation between job performance and voluntary employee turnover for 5,143 exempt employees in a single firm in the petroleum industry. As hypothesized, we found support for Jackofsky\u27s (1984) curvilinear hypothesis as turnover was higher for low and high performers than it was for average performers. Three potential moderators of this curvilinearity were examined in an attempt to explain conflicting results in the performance turnover literature and contradictory predictions from turnover models. As predicted, pay growth, promotions, and labor demand each differentially influenced the turnover patterns of low, average, and high performers. Most notably, paying high performers according to their performance predicted substantial decrements in turnover. A utility analysis indicated that the benefits of paying high performers according to their performance more than offset the costs and that such an approach was a superior strategy when compared to a more egalitarian pay growth policy