Summary of A Mathematical Model For Simultaneous Personnel Shift Planning and Unrelated Parallel Machine Scheduling, by Maziyar Khadivi et al.
A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
by Maziyar Khadivi, Mostafa Abbasi, Todd Charter, Homayoun Najjaran
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Discrete Mathematics (cs.DM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model optimizes production plans across a multi-period scheduling horizon, taking into account variations in personnel shift hours within each time period. It assumes shared personnel among machines, with one person required per machine for setup and supervision during job processing. The model minimizes total production time considering machine-dependent processing times and sequence-dependent setup times, while handling practical scenarios like machine eligibility constraints and production time windows. A Mixed Integer Linear Programming (MILP) model is introduced to formulate the problem, which is enhanced by a two-step solution approach that first maximizes accepted jobs and then minimizes production time. The model’s performance is validated with synthetic problem instances and a real industrial case study of a food processing plant. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps companies make better decisions about when and how to do tasks in their factories. It’s like planning a schedule for multiple machines that need people to work on them, but the people only have certain hours available each day. The goal is to get all the work done as quickly as possible while also following rules about what machines can be used at the same time. The researchers created a special kind of math problem to solve this scheduling challenge and tested it with made-up scenarios and real data from a food factory. |
