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We address the problem of scheduling a single batching machine to minimize the maximum lateness with a constraint restricting the batch size. A solution for this NP-hard problem is defined by a selection of jobs for each batch and an ordering of those batches. As an alternative, we choose to represent a solution as a sequence of jobs. This approach is justified by our development of a dynamic program to find a schedule that minimizes the maximum lateness while preserving the underlying job order. Given this solution representation, we are able to define and evaluate various job-insert and job-swap neighborhood searches. Furthermore we introduce a new neighborhood, named split-merge, that allows multiple job inserts in a single move. The split-merge neighborhood is of exponential size, but can be searched in polynomial time by dynamic programming. Computational results with an iterated descent algorithm that employs the split-merge neighborhood show that it compares favorably with corresponding iterated descent algorithms based on the job-insert and job-swap neighborhoods.

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