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

TL;DR

This paper presents a MILP model for optimizing simultaneous personnel shift planning and unrelated parallel machine scheduling, addressing real-world constraints like personnel availability and machine eligibility.

Contribution

It introduces a novel integrated model combining personnel scheduling with machine scheduling, incorporating practical constraints and a two-step solution approach.

Findings

Model effectively handles personnel constraints and machine eligibility.

Validation shows improved scheduling efficiency in industrial case.

Provides managerial insights for production planning.

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.