Optimal production planning is one of the most time-consuming and challenging tasks for many production experts, especially in diverse manufacturing environments where humans, robots, and machines work independently or collaboratively. Therefore, in this Industry 4.0 era, it is crucial to equip manufacturers with a digital decision support tool to plan their production efficiently. This project aims to develop an optimization tool to assist production planners in determining the optimal production sequence and assigning tasks and resources to workstations.

The PREFER project will significantly impact the production process by enhancing resource efficiency, robust and efficient production, and smart production planning. The outcomes of the project are expected to contribute to the competitiveness of the manufacturing industry in four main ways:

1. Better balance at workstations which leads to an increase in production or decrease in time/cost
2. Optimal usage of machines and robots which leads to energy saving
3. Swift production planning with the use of a digital tool
4. Sustainable production through improvements in energy consumption and lead time.

The project results will contribute to improving three important KPIs: throughput, energy consumption, and production planning lead time.

Planned approach and implementation

The project consortium includes three partners: (i) the University of Skövde as the project coordinator and the academic partner of the project (ii) Volvo Trucks and Daloc AB as the industrial partners that will support the project by providing use cases and involvement in various project activities.

The project has been planned for over three years and consists of five work packages, three technical and two general work packages. The technical work packages are dependent and need to be completed in sequence. The two other work packages are concerned with project coordination and dissemination. The solution and tools developed within the project will be tested and verified by industrial demonstrators.

Publications

2023

Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling

Amir Nourmohammadi, Amos H. C. Ng, Masood Fathi, Janneke Vollebregt, Lars Hanson

CIRP - Journal of Manufacturing Science and Technology

2023. Article. https://doi.org/10.1016/j.cirpj.2023.09.002

Project Leader

Associate Professor of Production Engineering

Masood Fathi

School of Engineering Science

Email address masood.fathi@his.se

Telephone number 0500-448575