Optimal scheduling in a Collaborative robot environment and evaluating workforce dynamic performance

¹ Mechanical, Material and thermal Laboratory, National School of Mines In Rabat (ENIM), Rabat, Morocco
² Mechanical, Material and thermal Laboratory, National School of Mines In Rabat (ENIM), Rabat, Morocco
³ Artificial Intelligence Complex Systems Engineering Laboratory (LAICSE), National School of Arts and Crafts (ENSAM), Hassan II University, Casablanca, Morocco

^* Corresponding author: hajar.tj@gmail.com

Abstract

After the emergence of industry 4.0 and the continuous technological development, it became vital for industries to transfer mass production expertise into personalized products in small batches. Clients became more aware of their needs and start basing their decision on specific quality requirements, lower cost, and the shortest delivery date. This is where collaborative robots intervene, these structures can work hand in hand with operators and take charge of hard, long, or repetitive tasks in a fast, precise, and robust manner. Although these structures have great potential, they lack flexibility and adaptability, these aspects can only be found in humans. The workforce competencies and performance are the ultimate precursors to any proper industrial evolution. Performances and competencies workforce must go further than the standard definitions attributed to them. This paper addresses the scheduling problem, our proposition relies on the assumption that the final programs attributed to collaborative robots can be divided into standard sub- programs. Based on the similarities between sub-programs can help propose a schedule that reduces significantly wasted time developing new programs or going from one program to another. This paper will also address the dissociation between human and robots’ performances in a context where humans and robots work in very dependent proximity. Finally, we will also propose a new definition of workload performance while highlighting its dynamic aspect in terms of fatigue, motivation, and the correlation between repetition and the learning process.