In de afgelopen jaren is veel aandacht besteed aan digitalisering van Cultureel erfgoed. Vrijwel alle musea en archieven digitaliseren, beschrijven, en classificeren objecten in collectiebeheersystemen. Belangrijkste doelen van digitalisering zijn duurzame archivering en ontsluiting via museumwebsites of via portals zoals Europeana. In vergelijking met Cultureel erfgoed is digitalisering in Industrieel erfgoed onderontwikkeld. Er zijn diverse initiatieven en organisaties die zich inzetten voor het behoud van Industrieel erfgoed, zoals de organisaties die zich hebben verenigd in de Federatie Industrieel Erfgoed Nederland. Traditioneel ligt hierbij een sterke nadruk op behoud van industriële gebouwen zoals fabrieken, molens, vuurtorens, etc. Ook wordt aandacht besteed aan machines en voertuigen, bijvoorbeeld door het Nationaal Register Mobiel Erfgoed. Methoden, technieken en tools voor beschrijven en ontsluiten zoals deze zijn ontwikkeld voor Cultureel erfgoed missen echter voor Industrieel erfgoed. De maakindustrie in Nederland is altijd in ontwikkeling. Door innovatie ontstaan nieuwe productietechnieken en verplaatsen bestaande productiemethoden zich naar lage-lonen landen of verdwijnen ze helemaal. Het gevolg is dat niet alleen machines verloren gaan die ooit vernieuwend waren, maar ook dat vakmanschap verschuift: van handmatig naar het gebruik van gereedschappen, en naar supervisie van een geautomatiseerd proces. Doel van het project is het ontwikkelen en implementeren van een systematiek om Industrieel erfgoed in brede zin (onroerende en roerende goederen, productiemethoden en technieken) te kunnen beschrijven, archiveren, en publiek ontsluiten.

Worker shortages and frequent employee turnover are often linked to insufficient employee well-being in the workplace. However, existing personnel scheduling optimization, which develops models and algorithms for work-rest and task allocation schedules, primarily emphasizes efficiency metrics. These highly efficient schedules often come at the cost of stressed employees and even productivity losses. This project proposes a novel approach to personnel scheduling that minimizes physical fatigue which is a large determinant of overall well-being. In doing so, this project will develop mathematical models and algorithms that have the potential to improve employee well-being in a variety of labor-intensive workplaces.

This project aims to bridge the gender gap in crowdfunding, specifically within the technology sector, where female entrepreneurs success rates lag significantly behind their male counterparts. By introducing an innovative framework that delineates between traditional marketing capabilities (MC) for static campaign features (text description) and dynamic capabilities (DC) for interactive features (comments section), we seek to identify the unique combination of these capabilities that fosters female entrepreneurs’ funding success. Employing a machine learning and configurational analysis, this study endeavors to reveal the specific configurations of MC and DC that empower female entrepreneurs, thereby creating a more inclusive entrepreneurial ecosystem.

Many practical decisions have to be made before key information is known. For example, network operators have to make investments in the electricity grid while future costs of capital and future supply in renewable energy are uncertain. Decision support is required for such problems, also in healthcare, logistics and engineering. However, this support is only available to a limited degree because of the high complexity of the underlying mathematical optimization problems. Such so-called stochastic mixed-integer optimization problems are extremely difficult to solve since they combine the difficulties of having integer decision variables (i.e., discrete or yes/no decisions) and uncertainty in the parameters of the problem. Traditional solution methods combine solution approaches from deterministic mixed-integer and stochastic continuous optimization, but are generally unable to solve practical problems of realistic sizes. Even simplified, deterministic versions of these problems are challenging since they are not convex, and thus efficient solution methods from the well-developed field of convex optimization cannot be used to solve them. Interestingly, however, my recent work has shown that stochastic mixed-integer optimization problems are (approximately) convex. Thus, from a “convex” perspective, these stochastic problems are easier to solve than their deterministic counterparts. In this sense, the introduction of uncertainty to mixed-integer optimization problems overcomes the difficulty of having integer decision variables. The aim of this project is to design fast solution methods for stochastic mixed-integer optimization problems, exploiting this new and exciting perspective and building on my previous work. The newly developed solution methodology will be validated by applying the developed methods to optimal investments in the electricity grid for a network operator. In conclusion, this research project will yield a new and efficient type of solution methodology for stochastic mixed-integer optimization problems and enable better decision support for many practical decisions under uncertainty.

An important mismatch between inventory control models and their practical use is that models are based on the assumption that the future demand distribution and all of its parameters are completely known, whereas in practice these have to be estimated. Neglecting or not fully incorporating this uncertainty leads to sub-optimal inventory decisions and, even worse, to not meeting service level targets. This research studies the degree of sub-optimality when using existing models, and develops alternative models that do take forecast uncertainty into account. Both uncertainty about demand parameters and uncertainty about the demand distribution class are accounted for.