Biomanufacturing involves the large-scale production of bio-based products, for example, food ingredients. In fermentation-based factories, living organisms are used to produce the active components of these bio-based products through fermentation in large bioreactors. Modern biomanufacturing plants are highly complex, with many tanks and interconnected pieces of equipment. They are typically designed to make multiple products (in parallel), each with its own recipe and production process. As a result, it is not possible to repeat the same weekly schedule—customer demand changes over time, and different customer orders require different products and thus production schedules.
A feasible schedule should consider several scheduling rules essential for biomanufacturing. For example, in the food industry, it is crucial that tanks are cleaned before, after, or between specific production operations. The production of a single product involves multiple unit operations, for example, starting from fermentation, followed by several filtration steps. Between the different stages, there are rules about how long an intermediate product can wait at a single tank; otherwise, a product risks expiring. Due to the biological nature of fermentation processes, process durations are uncertain and can vary from batch to batch. Due to this complexity, limited resource capacities, and various sources of uncertainty, it poses a significant challenge for factory managers and production planners to fulfill all customer orders on time.....

This study investigates scheduling strategies for the stochastic resource-constrained project scheduling problem with maximal time lags (SRCPSP/max). Recent advances in Constraint Programming (CP) and Temporal Networks have re-invoked interest in evaluating the advantages and drawbacks of various proactive and reactive scheduling methods. First, we present a new, CP-based fully proactive method. Second, we show how a reactive approach can be constructed using an online rescheduling procedure. A third contribution is based on partial order schedules and uses Simple Temporal Networks with Uncertainty (STNUs). Our statistical analysis shows that the STNU-based algorithm performs best in terms of solution quality, while also showing good relative offline and online computation time.

When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed. Historical realizations of the stochastic processing times are available. We show how existing decision-focused learning techniques based on stochastic smoothing can be adapted to this scheduling problem. We include an extensive experimental evaluation to investigate in which situations decision-focused learning outperforms the state of the art, i.e., scenario-based stochastic optimization.

We study a highly complex scheduling problem that requires the generation and optimization of production schedules for a multi-product biomanufacturing system with continuous and batch processes. There are two main objectives here; makespan and lateness, which are combined into a cost function that is a weighted sum. An additional complexity comes from long horizons considered (up to a full year), yielding problem instances with more than 200 jobs, each consisting of multiple tasks that must be executed in the factory. We investigate whether a rolling-horizon principle is more efficient than a global strategy. We evaluate how cost function weights for makespan and lateness should be set in a rolling-horizon approach where deadlines are used for subproblem definition. We show that the rolling-horizon strategy outperforms a global search, evaluated on problem instances of a real biomanufacturing system, and we show that this result generalizes to problem instances of a synthetic factory.