A bi-objective job-shop scheduling problem considering worker fatigue and productivity in cobotic order picking systems

Master thesis (2023)

Authors

B.L. Vermin Mechanical Engineering

Contributors

David Abbink Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (mentor)
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (mentor)
O. Oviedo-Trespalacios (graduation committee member)
C.M. Schmidt Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2023 Berry Vermin
More Info
expand_more

Published Date

10-03-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical Engineering

Abstract

The increase in online retail demand has stimulated automation in order picking systems, leading to new challenges and opportunities in task assignment and scheduling. In partially automated order picking systems, such challenges and opportunities exist regarding human factors implementation in the job-shop scheduling problem, an optimisation problem essential in operations. Workplace fatigue is a human factor often overlooked in scheduling research and application, despite hurting employees’ well-being and costing U.S. employers up to €127 billion annually. With the opportunities that automation offers, cobotic order picking systems could actively consider human fatigue development, mitigating its negative effects in operation.
This thesis investigates the possibility and potential benefits of fatigue consideration in the job-shop scheduling problem for a partially automated order picking system. We present a new bi-objective mixed integer nonlinear programming problem formulation to represent system constraints and a predictive fatigue model while considering worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model predictions and fatigue mitigation capabilities of the scheduling approach using heart rate measurements and qualitative fatigue ratings. These experiments occur with employees in a real-life partially automated order picking system.
Our mathematical model can find solutions that the conventional single-objective optimisation approach cannot, allowing fractional energy expenditure distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. This is a promising result for fatigue mitigation in operations only by altering operational decision-making. However, the validation experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements while also showing a low correlation. When assessing 10 minute intervals, a time span that fits a scheduling scope, the estimations improve slightly (avg. deviation of -1.85 kcal/min, avg. correlation of 0.17) but still underestimate the measured values. The experiments also show no significant differences in experienced fatigue between existing schedules and those with fatigue mitigation measures applied.
We conclude that the current scheduling formulation is not yet fit for application with a predictive fatigue model. However, real-life operations can benefit from energy expenditure estimation via heart rate measurements and a different approach for implementation is proposed. Research opportunities lie in further fatigue model development and validation, extension to indirect fatigue effects and other human factors, and further development of the mathematical formulation.

Files

Thesis_Final.pdf
(pdf | 29.7 Mb)
Unknown license