Parallel Batch Scheduling With Incompatible Job Families via Constraint Programming

This paper addresses the incompatible case of parallel batch scheduling, where compatible jobs belong to the same family, and jobs from different families cannot be processed together in the same batch. The state-of-the-art constraint programming (CP) model for this problem relies on specific functions and global constraints only available in a well established commercial CP solver. This paper expands the literature around this problem by proposing four new CP models that can be implemented in commercial and open-source solvers: a new model that relies on automaton constraints, and three alternative models that integrate assignment and scheduling decisions with different strategies and global constraints. Extensive computational experiments on standard test cases under multiple objectives and multiple solvers demonstrate the implementation flexibility and competitive performance of the proposed models.