The Vlerick Repository is a searchable open-access publication database, containing the complete archive of research output written by Vlerick Business School faculty and researchers.
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Item Metadata only Automated design of heuristics for resource-constrained project scheduling problem via regression algorithms(Springer Nature, 2026)The resource-constrained project scheduling problem (RCPSP) is a complex optimization problem aiming to construct feasible schedules that minimize the project makespan while satisfying the precedence and renewable resource constraints. Priority rule heuristics are prevalent approaches for solving the RCPSP, particularly in practical applications. However, these rules are problem-specific, and no rule can consistently outperform others across different projects. Designing priority rules through manual methods requires substantial expertise, time, and computational effort. This has led researchers to propose automated techniques for this purpose. Most existing research in this area focuses on unsupervised learning techniques like genetic programming hyper-heuristics (GPHH), while the investigation of supervised learning algorithms remains limited. To address this gap, this research explores the potential of supervised learning algorithms, specifically regression-based methods, for the automated design of new priority rule heuristics for RCPSP. Nine widely used regression algorithms were evaluated, and the top-performing three were further enhanced using ensemble techniques to augment their effectiveness. Computational experiments show that regression-based heuristics can outperform traditional priority rules across all primary test datasets and, in some cases, even surpass priority rules designed through GPHH. To further validate the reliability of our results, we also tested the regression-based heuristics on various supplementary datasets, including project instances with more than 1,000 activities and empirical projects. Their performance highlights the robust generalization of regression-based heuristics.Item Metadata only A counterfactual and risk temporal knowledge graph framework for interpretable project risk management(Elsevier, 2026-07)Effective project risk management (PRM) necessitates accurate prediction and actionable insights. Machine learning (ML) models improve risk assessment by uncovering complex patterns; however, their black-box nature limits interpretability, making it difficult for stakeholders to trust predictions. Traditional explainable artificial intelligence (XAI) methods highlight influential risk factors yet often fail to provide actionable recommendations, focusing on model behavior rather than practical interventions. Counterfactual explanations (CEs) aim to bridge this gap by suggesting modifications to risk factors or project conditions that could alter outcomes. However, existing CE methods in PRM often lack domain specificity, overlook interdependencies, and ignore temporal constraints, producing recommendations that are unrealistic or infeasible. To address these limitations, we propose Counterfactual Reasoning with Risk Temporal Knowledge Graph (CR-RTKG), a framework that integrates counterfactual reasoning with a Risk Temporal Knowledge Graph (RTKG) to improve interpretability and actionability of risk mitigation. The RTKG encodes domain knowledge, models causal dependencies and cascading effects, and classifies risks by temporal horizon, supporting prioritization based on urgency and systemic influence. By embedding stakeholder-defined constraints into a multi-objective optimization process, CR-RTKG generates context-sensitive and feasible counterfactuals. Unlike conventional methods, it aligns recommendations with real-world project constraints. Experimental results show that CR-RTKG achieves higher plausibility (96%) and feasibility (93%), outperforming baselines including Diverse Counterfactual Explanations (DiCE) and Flow-based Counterfactual Explanation (CeFlow).Item Metadata only Stakeholder Perceptions of Policy Tools in Support of Sustainable Food Consumption in Europe: Policy Implications(MDPI, 2020)Transitioning agri-food systems towards increased sustainability and resilience requires that attention be paid to sustainable food consumption policies. Policy-making processes often require the engagement and acceptance of key stakeholders. This study analyses stakeholders’ solutions for creating sustainable agri-food systems, through interviews with a broad range of stakeholders including food value chain actors, non-governmental organizations, governmental institutions, research institutions and academic experts. The study draws on 38 in-depth, semi-structured interviews conducted in four European countries: France, Iceland, Italy and the UK, as well as three interviews with high-level EU experts. The interviewees’ solutions were analysed according to a five-category typology of policy tools, encompassing direct activity regulations, and market-based, knowledge-based, governance and strategic policy tools. Most of the identified solutions were located in the strategic tools category, reflecting shared recognition of the need to integrate food policy to achieve long-term goals. Emerging solutions—those which were most commonly identified among the different national contexts—were then used to derive empirically-grounded and more universally applicable recommendations for the advancement of sustainable food consumption policies.Item Metadata only Comprehensive Cost Estimation of an Enhanced Recovery Pathway in Hip and Knee Arthroplasty Using an Expanded Time-Driven Activity-Based Costing Framework(Elsevier, 2026-04)BACKGROUND: Total hip (THA) and total knee arthroplasty (TKA) are among the most frequently performed surgeries worldwide, creating a substantial financial burden. Enhanced Recovery After Surgery (ERAS) pathways aim to improve outcomes while reducing costs, aligning with the principles of Value-Based Healthcare (VBHC). Time-Driven Activity-Based Costing (TDABC) is recommended for VBHC cost calculation, yet its application to ERAS is rarely reported. This study calculated total and ERAS-related costs of a comprehensive ERAS pathway for THA and TKA using TDABC. METHODS: A retrospective single-center cohort study (February to September 2023) was performed in Belgium for 99 THA and 120 TKA patients following an uncomplicated ERAS pathway. Using the TDABC method and the "TDABC in Healthcare Consortium" framework, costs from first surgical consultation until the 3-month follow-up were divided into five categories. Direct costs (medication and material) and TDABC-allocated costs (personnel, infrastructure, and departmental overheads) were aggregated to determine total and ERAS-related costs. All costs are reported in euros (EUR) and converted to United States dollars (USD) using the average exchange rate (1 EUR = 1.08 USD) for 2023. RESULTS: The mean total costs were €6,682.3 ($7,216.9) for THA and €8,091.3 ($8,738.6) for TKA. Material and personnel accounted for approximately 90% of total pathway costs. The ERAS-related costs represented approximately 10% of total pathway costs (€673.7/ $727.6 THA; €744.2/ $803.7 TKA). The highest ERAS costs were noted in the preoperative phase, whereas postoperative elements (e.g., early mobilization) had relatively low costs. CONCLUSION: The TDABC revealed that ERAS costs represented only 10% of the total pathway cost and highlighted key (ERAS) cost components for further pathway improvement. This study offers a TDABC framework to standardize cost evaluation and support ERAS implementation, optimization, and sustainability within VBHC strategies.Item Metadata only How Downsizing a Typical Product at a Stable Unit Price Recalibrates Price Perceptions: A Contrast–Assimilation Account of Perceived Category and New‐Product Expensiveness(Wiley, 2026)ABSTRACT Managing perceived expensiveness is critical in competitive retail environments, especially amid inflation and heightened price sensitivity. We demonstrate that fast‐moving consumer goods (FMCG) retailers can influence these perceptions by reducing the volume of a typical product in an assortment while keeping the unit price stable (i.e., product downsizing with a proportional price adjustment). This tactic lowers the product's absolute price tag, serving as a salient cue that decreases the perceived expensiveness of the entire product category assortment. As such, retailers avoid margin sacrifices typical of alternative steep discounting tactics used to induce perceptions of inexpensiveness. Moreover, employing product downsizing with a proportional price adjustment may entail limited risk of consumer backlash, as the quantity–price ratio remains stable. However, we also observe that this tactic simultaneously increases perceived expensiveness of a newly introduced product within the assortment. We explain these opposing effects through a psychological mechanism involving generalization–assimilation and contrast processes and reflect on managerial implications. We provide empirical evidence from three experimental studies ( N = 447, N = 476, N = 294), employing visual scenario‐based online surveys and using FMCG goods (i.e., chocolate and soft drinks) as stimuli. The studies identify assortment size as a moderator, demonstrate that unit price disclosure attenuates several process effects, and highlight the need for research using multi‐brand and/or multi‐category assortment depictions. Theoretically, our work builds on reference price research, signpost roles of products caused by underlying price anchoring phenomena, and consumers' asymmetric sensitivities to price versus quantity changes. The expensiveness management tactic proposed seems especially suited for FMCG environments, because such products typically elicit low elaboration likelihood, leading consumers to rely on salient cues, and are commonly offered in varying quantities.