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A buffer allocation evolutionary algorithm for resource-constrained projects with activity clusters
No full textWe propose a novel approach for sizing the activity buffers in the project by clustering similar activities and allocating the buffers using a unique attribute in each cluster. Since the number of clusters as well as the assignment of attributes to these clusters has an impact on the buffer sizing, the problem is solved using an adapted multifactorial evolutionary algorithm (aMFEA) in which multiple buffer allocation problems (BAPs) are solved simultaneously. Several decoding schemes are compared to improve the synergies between the different BAPs and the evolutionary operators. The results show the added value of the evolutionary components of the aMFEA and show that the proposed approach is superior to existing benchmarking procedures. Furthermore, the solution quality improves with an increasing number of clusters, while the solution quality goes down again as the number of clusters becomes too large. From a practical perspective, this study highlights the need to identify good activity attributes that are linked to the buffer sizing decisions and the importance of activity clustering in order to reduce the time and effort needed for better buffer sizing decisions.(Research Foundation - Flanders|12A4222N, China Scholarship Council|202106280089, National Natural Science Foundation of China|72371195
Linguistics of the heart and mind: Negotiating in one's native language is comfortable but not efficient
No full textWhen negotiating with partners from abroad, which language should we choose: a native or a foreign one? To answer this question, we leverage dual‐process theory to investigate how using a native versus foreign language affects negotiation strategies and outcomes and explore the moderating role of emotions. Across three studies that use dual‐language speakers of four of the five most common languages in the world (English, Chinese, Spanish and French), our findings consistently show that, while the native language is the preferred option for negotiation (Study 1), the consequences are more emotional expression, more passive strategies and worse outcomes (Studies 2 and 3). Anger in a native versus foreign language makes negotiators compromise more, which results in worse outcomes (Study 3). Our contribution is threefold: We are the first to explore the effects of language (foreign vs. native) in an empirical negotiation setting; we separate the intrapersonal from the interpersonal effects of language by using the Actor‐Partner Interdependence Model; and we establish that the language effects are independent of culture. Our results suggest that managers should use their native language with caution when negotiating, since they might unconsciously display higher levels of emotion and use more passive negotiation strategies.(Federación Española de Enfermedades Raras, Ministry of Economy, Industry and Competitiveness
Valuation for insolvency practitioners
No full textValuations of private companies can be required for a variety of purposes, including insolvency proceedings. The process for valuing private companies is deceptively complex. This is notably because there is no consensus on the size and determinants of two important valuation discounts that may need to be applied: the discount for lack of control (DLOC) and the discount for lack of marketability (DLOM). This book chapter zeroes in on the (often overlooked) determinants of the DLOC and the DLOM by examining US court cases. We make two important contributions to the existing literature. First, we expand the arsenal of empirical methods to estimate the DLOC and the DLOM by tapping into a hitherto underutilised source of information that can provide rich context and novel insights. Second, we illustrate that there is a link between these two discounts. This is contrary to conventional wisdom and brings clarity to the scarce literature and anecdotal evidence that suggest a potential interplay between control and marketability
When and How Developmental Rewards and Expected Contributions Relate to Emotional Exhaustion Through Work Engagement: The Multilevel Moderating Role of the Leader’s Work Pressure
Full text linkThis study focuses on public secondary schools to examine the extent to which leader-level job demands impact the relationship between employees’ job resources, job demands, and well-being. Specifically, we investigate (1) how teachers’ developmental rewards and expected contributions relate to their work engagement and emotional exhaustion and (2) the role of school principals’ work pressure in this relationship. Building on recent developments in job demands-resources (JD-R) theory, we argue a leaders’ work pressure can trickle down to the employee level. Hierarchical linear analyses reveal that principals’ work pressure moderates the relationship between teachers’ expected contributions and emotional exhaustion. We thus add to JD-R theory by suggesting that employee work outcomes are also shaped by job demands at the leader level. Policies aimed at improving employee well-being should therefore be based on a comprehensive image of the organization that also takes the leader’s job demands into account
A genetic algorithm with resource buffers for the resource-constrained multi-project scheduling problem
No full textA novel metaheuristic solution procedure for the RCMPSP is presented.
Two variants of resource-buffered scheduling are embedded within the procedure.
The algorithm is benchmarked against 10 existing metaheuristic algorithms.
New best-known solutions are generated for 20% of the instances in the dataset.
A new schedule metric is proposed to analyse the structure of the solutions.In this study, we compose a new metaheuristic algorithm for solving the resource-constrained multi-project scheduling problem. Our approach is based on a general metaheuristic strategy which incorporates two resource-buffered scheduling tactics. We build on the most effective evolutionary operators and other well-known scheduling methods to create a novel genetic algorithm with resource buffers. We test our algorithm on a large benchmark dataset and compare its performance to ten existing metaheuristic algorithms. Our results show that our algorithm can generate new best-known solutions for about 20% of the test instances, depending on the optimisation criterion and due date. In some cases, our algorithm outperforms all other available methods combined. Finally, we introduce a new schedule metric that can quantitatively measure the dominant structure of a solution, and use it to analyse the differences between the best solutions for different objectives, due dates, and instance parameters.We acknowledge the support provided by the Bijzonder Onderzoeksfonds (BOF), Belgium with contract number 24J046-17 and the Research Foundation – Flanders (FWO), Belgium for the project, under contract number 3G012616. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputing Center), funded by Ghent University, Belgium, FWO and the Flemish Government department EWI
Supervised learning for integrated forecasting and inventory control
No full textWe explore the use of supervised learning with custom loss functions for multi-period inventory control with feature-driven demand. This method directly considers feature information such as promotions and trends to make periodic order decisions, does not require distributional assumptions on demand, and is sample efficient. The application of supervised learning in inventory control has thus far been limited to problems for which the optimal policy structure is known and takes the form of a simple decision rule, such as the newsvendor problem. We present an approximation approach to expand its use to inventory problems where the optimal policy structure is unknown. We test our approach on lost sales, perishable goods, and dual-sourcing inventory problems. It performs on par with state-of-the-art heuristics under stationary demand. It outperforms them for non-stationary perishable goods settings where demand is driven by features, and for non-stationary lost sales and dual-sourcing settings where demand is smooth and feature-driven
Understanding ageism in the workplace
No full textAn ageing society and workforce present both challenges and opportunities. The presence of multiple generations in the workplace can lead to age-related tensions, with some workers considered “too young” and others “too old”. How different age groups view and behave toward each other can have important consequences for workplace relationships, attitudes and performance, as well as for the wellbeing of employees. In this research study, we have examined predictors and consequences of ageism aimed at both younger and older workers. The results from a range of qualitative and quantitative studies, including a representative sample of one thousand Portuguese workers, show that ageism can have important, mostly negative, consequences for those targeted, as well as for endorsers of ageism. Based on our findings, we make recommendations for actions that can be taken to reduce bidirectional ageism in the workplace
Year Report 2023 (research in the energy sector)
No full textThe 2023 Year Report for the PhD project between an energy business partner and Vlerick Business School highlights the research activities executed in 2023. The year began with an in-depth examination of inconsistencies in emotion labeling across multiple datasets, leading to the exploration of external knowledge-based frameworks for dataset enrichment. Machine learning models were assessed for their ability to generalize across datasets, highlighting the need for standardization in emotion classification.
Mid-year, the research shifted toward predictive modeling, integrating service interaction data with customer feedback to explore customer experience metrics. A cloud-based machine learning infrastructure was implemented to improve scalability, and transfer learning approaches were tested to enhance model robustness. Later in the year, comparative studies were conducted to evaluate different emotion classification methodologies and their applicability across diverse datasets. The findings underscored key challenges in emotion recognition and provided insights into improving predictive accuracy. Future research will refine these approaches and expand the analysis of customer interactions in service environments
European Executive Reward Insights – 2023. An in-depth study of executive remuneration based on Stoxx Europe 600’s FY 2022 annual reports
No full textHow much are senior executives being paid across Europe today? What are their remuneration packages made up of? And is executive remuneration becoming too complicated? This white paper answers these questions and more. It’s the result of an ongoing study by Vlerick’s Executive Remuneration Research Centre. Since 2014, the centre has been compiling a comprehensive database of CEO remuneration. The work is painstaking, and involves extracting information from remuneration reports published by Stoxx Europe 600 companies. And the results are illuminating