Rolling-Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling Problem

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.

1 INTRODUCTION

Efficient bioprocess industries can play a crucial role in feeding the world population in a sustainable way. Production sites for the process industries are often multipurpose, highly flexible systems. The number of different products using (partially) the same machines in their production process has been increased due to market pressure. Given long-horizon demand up to a full production year, factory operators want to optimally use their resources, while ensuring that deadlines for customer orders are met. Using these deadlines as hard constraints makes the scheduling too restrictive; instead a lateness objective can be defined which sums the lateness of all customer orders. Efficiency of a schedule can be evaluated by measuring the makespan. Intuitively, makespan minimization also contributes to reducing lateness of customer orders. On the other hand, not considering deadlines can potentially lead to schedules with lower makespan values. To illustrate this; ignoring deadlines allows clustering products in such a way that shared resources are used in a more efficient manner. For example, the same products for different customers can sometimes be produced in a single batch, or when subsequent batches are for the same or similar products, machine set-up and cleaning times can be shorter. The trade-off between the lateness and makespan objective makes the optimization therefore challenging.