The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.
We present a novel optimisation approach for incentive contract design within a project setting. the structure of the remuneration is one of the key challenges faced by the project owner when (s)he decides to hire a contractor. The proposed technique builds on the recently proposed contract design methodology by Kerkhove and Vanhoucke (Omega, 2015). Specifically, a novel multi-objective scatter search heuristic is proposed and implemented using parallelisation. Both single- and multi-population implementations of this heuristic are compared to the original full-factorial approach as well as commercial optimisation software. The results of the computational experiments indicate that the single-population parallel scatter search procedure significantly outperforms the other solution strategies in terms of both speed and solution quality.
Modeling electricity storage to address challenges and opportunities of its applications for smart grids requires inter-temporal equalities to keep track of energy content over time. Prevalently, these constraints present crucial modeling elements as to what extent energy storage applications can enhance future electric power systems' sustainability, reliability, and efficiency. This paper presents a novel and improved mixed-integer linear problem (MILP) formulation for energy storage of plug-in (hybrid) electric vehicles (PEVs) for reserves in power system models. It is based on insights from the field of System Dynamics, in which complex interactions between different elements are studied by means of feedback loops as well as stocks, flows and co-flows. Generalized to a multi-bus system, this formulation includes improvements in the energy balance and surpasses shortcomings in the way existing literature deals with reserve constraints. Tested on the IEEE 14-bus system with realistic PEV mobility patterns, the deterministic results show changes in the scheduling of the units, often referred to as unit commitment (UC).
Erden, Zeynep; Ben-Menahem, Shiko; von Krogh, Georg; Schneider, Andreas; Koch, Guido; Widmer, Hans (R J Communications & Media World Ltd, 2019)
Drug discovery teams combine specialists with in-depth knowledge from a variety of scientific disciplines. Such diversity in thought worlds poses a challenging exercise in cross-disciplinary collaboration and project coordination. Based on a longitudinal field study of five projects in a leading pharmaceutical company we present a framework outlining the conditions for effective cross-disciplinary collaboration in drug discovery teams. We show that knowledge creation in multidisciplinary teams relies on a combination of formal team structures and informal co-ordination practices. Formal team structures set the boundary conditions for cross-disciplinary co-ordination. Within their boundaries self-managed sub-teams draw on informal co-ordination practices involving cross-disciplinary anticipation, synchronization and triangulation to overcome knowledge boundaries and high uncertainty. We identify five key insights and two questions which are important for managers to consider for fostering multidisciplinary collaboration in drug discovery.
Purpose:The purpose of this study was to investigate the effect of generational, contextual, and individual influences on Millennials’ career expectations. Design:methodology:approach:Two matched samples of Millennials graduating in 2006 (n = 787) and 2009 (n = 825) filled out a questionnaire regarding their psychological contract expectations, career strategy, and optimism about the labor market in completely different socioeconomic contexts. Findings:Recession is related to lower levels of optimism. During times of recession, Millennials lower their expectations regarding the work-life balance and social atmosphere. However, their expectations regarding job content, training, career development, and financial rewards remain high, suggesting that these expectations are largely embedded within the generation. Moreover, Millennials’ expectations are significantly influenced by individual variables, careerism, and optimism. Implications:This study suggests that managers need to focus their limited resources during times of recession on meeting Millennials’ high expectations regarding their development and careers. Because violating these high expectations can have detrimental effects on a number of outcomes, organizations are encouraged to discover creative and inexpensive ways to provide Millennials with meaningful work, plenty of learning opportunities and career development.:Originality value:By comparing two matched samples of Millennials in two different situations, this study was able to disentangle generational, contextual, and individual influences on Millennials’ psychological contract expectations.
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