Multi-Objective Batching-Dispatching Framework for Parallel Machine Scheduling with Job Families
Under review · Computers and Industrial Engineering (C&IE), submitted 2025
Authors: Karen Seojin Kim†, Minju Jang†, Sang-Wook Lee, Jimin Park, and Hyun-Jung Kim († Co-first authors) Status: Under review in Computers and Industrial Engineering (C&IE), submitted in 2025
TL;DR
When jobs belong to families that share a common setup, processing them in batches can save a lot of machine time — but only if batching and machine assignment are decided together. We propose a framework that couples batching and dispatching for parallel machine scheduling and optimizes several objectives at once.
The problem
In parallel machine scheduling with job families, switching a machine from one family to another incurs a setup. Grouping same-family jobs into a batch avoids repeated setups, but larger batches can delay individual jobs. The scheduler must therefore trade off setup efficiency, throughput, and lateness — and do so across multiple machines simultaneously.
The approach
- Batching + dispatching, jointly — instead of forming batches first and assigning them later, the framework decides how to batch and where/when to dispatch in a coordinated way.
- Multi-objective — it balances competing goals (e.g. makespan, tardiness, and setup count) rather than optimizing a single metric.
Why it matters
Family setups dominate cost in many real lines (semiconductor, electronics, process industries). A coordinated batching–dispatching policy gives planners a practical way to cut setup time without sacrificing due-date performance.
My Role
I contributed to this work as a co-author.
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