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Automatic detection of the best performing priority rule for the resource-constrained project scheduling problemPriority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.
Pertinent insights from Europe on executive compensationThroughout this article, it became clear that there is no “European way” of executive compensation as a number of geographical differences were found to be present. However, the research shows that the variance is explained by determinants such as business size, the industry in which the company operates and the share ownership structure. The article also looked at the underlying key performance indicators used for incentive systems. Not surprisingly, financial indicators were found to be most prevalent, determining on average 70% of the bonus while company size was the main driver of CEO compensation. We found that CEO compensation policies in the best performing companies, over a longer period of time, are characterized by modesty. This applies to compensation levels, the weight of incentives in the total package, and the spread between target and maximum bonus.
Least-cost distribution network tariff design in theory and practiceIn this paper a game-theoretical model with self-interest pursuing consumers is introduced in order to assess how to design a least-cost distribution tariff under two constraints that regulators typically face. The first constraint is related to difficulties regarding the implementation of cost-reflective tariffs. In practice, so-called cost-reflective tariffs are only a proxy for the actual cost driver(s) in distribution grids. The second constraint has to do with fairness. There is a fear that active consumers investing in distributed energy resources (DER) might benefit at the expense of passive consumers. We find that both constraints have a significant impact on the least-cost network tariff design, and the results depend on the state of the grid. If most of the grid investments still have to be made, passive and active consumers can both benefit from cost-reflective tariffs, while this is not the case for passive consumers if the costs are mostly sunk.
Venture capital, credit, and fintech start-up formation: A cross-country studyGrowing FinTech entrepreneurship is a recent global phenomenon. Drawing on the national innovation systems framework, we examine how countries’ venture capital (VC) and credit markets differently affect FinTech entrepreneurship across countries. We argue that with their established and globally diffused norms and practices, VC investors—but not banks—require a critical mass of FinTech entrepreneurship in a country to more positively influence FinTech entrepreneurship. Moreover, we argue that VC and credit markets are substitutes, especially in countries with more FinTech entrepreneurship. Using quantile regressions on data from 53 countries, we find support for our hypotheses.
2020 M&A Monitor: Shedding light on M&A in BelgiumThe Belgian mergers and acquisitions market experienced a good and stable year in 2019, with slight growth in the smaller transactions segment. However, as a result of the Covid-19 pandemic, merger and acquisition activities are expected to decline by more than 30% in 2020. In addition, 60% of Belgian experts also expect a price drop of more than 10%. Liquidity problems in many companies could also cause the balance of power to shift, mainly putting buyers in a strong bargaining position. Finally, for the first time a question was asked about the expected impact of an acquisition on future employment. This impact appears to be positive across the board. These are the main conclusions of the seventh edition of the M&A Monitor, an annual survey of around 110 Belgian merger and acquisition specialists including corporate finance advisers, private equity investors, brokers, bankers and lawyers. The survey asks about their experiences in the M&A market and the deals they were involved in during 2019 on the one hand, and their expectations for 2020 on the other. In order to estimate these expectations correctly, an additional survey was sent out in the second half of March which specifically asked about the expected impact of the Covid-19 pandemic. The study was conducted by professor Mathieu Luypaert and researcher Gianni Spolverato of the Centre for Mergers, Acquisitions & Buyouts at Vlerick Business School in collaboration with Bank J.Van Breda & Co., BDO, NautaDutilh and Sowaccess.