A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling

• URL: http://arxiv.org/abs/2402.15670v1
• Date: Sat, 24 Feb 2024 01:04:04 GMT
• Title: A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
• Authors: Maziyar Khadivi, Mostafa Abbasi, Todd Charter, Homayoun Najjaran
• Abstract summary: This paper addresses a production scheduling problem derived from an industrial use case. It focuses on unrelated parallel machine scheduling with the personnel availability constraint. It assumes shared personnel among machines, with one personnel required per machine for setup and supervision during job processing.
• Score: 3.0477617036157136
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper addresses a production scheduling problem derived from an industrial use case, focusing on unrelated parallel machine scheduling with the personnel availability constraint. The proposed model optimizes the production plan over a multi-period scheduling horizon, accommodating variations in personnel shift hours within each time period. It assumes shared personnel among machines, with one personnel required per machine for setup and supervision during job processing. Available personnel are fewer than the machines, thus limiting the number of machines that can operate in parallel. The model aims to minimize the total production time considering machine-dependent processing times and sequence-dependent setup times. The model handles practical scenarios like machine eligibility constraints and production time windows. A Mixed Integer Linear Programming (MILP) model is introduced to formulate the problem, taking into account both continuous and district variables. A two-step solution approach enhances computational speed, first maximizing accepted jobs and then minimizing production time. Validation with synthetic problem instances and a real industrial case study of a food processing plant demonstrates the performance of the model and its usefulness in personnel shift planning. The findings offer valuable insights for practical managerial decision-making in the context of production scheduling.

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