Abstract
This research combines between two different manufacturing concepts. On the one hand, flow shop scheduling is a well-known problem in production systems. The problem appears when a group of jobs shares the same processing sequence on two or more machines sequentially. Flow shop scheduling tries to find the appropriate solution to optimize the sequence order of this group of jobs over the existing machines. The goal of flow shop scheduling is to obtain the continuity of the flow of the jobs over the machines. This can be obtained by minimizing the delays between two consequent jobs, therefore the overall makespan can be minimized. On the other hand, collaborative robotics is a relatively recent approach in production where a collaborative robot (cobot) is capable of a close proximity cooperation with the human worker to increase the manufacturing agility and flexibility. The simplest case-study of a collaborative workcell is one cobot in cooperation with one worker. This collaborative workcell can be seen as a special case of the shop flow scheduling problem, where the required time from the worker to perform a specific job is unknown and variable. Therefore, during this research, we implement an intelligent control solution which can optimize the flow shop scheduling problem over the previously mentioned case-study.
Highlights
Introduction and Related ResearchCollaborative robotics is a novel successful trend in manufacturing which involves a safe cobot
The idea of the collaborative robotics is to gather the advantages of the industrial robot and the human worker together in the same work environment [1]
Since the manufacturing system is a matter of a continuous fluctuation in production demands such as the customization level and quantity [2], the presence of the human worker can be considered a very big advantage
Summary
Collaborative robotics is a novel successful trend in manufacturing which involves a safe cobot. The cobot can adapt to the pick and place operations based on previously programmed positions of all the product parts In other words, it is much easier for the worker to adapt to manufacturing operations which require human experience such as assembly. The research does not put into consideration the adaption to the fluctuation in the number of the operational resources as a new dimension of configurability in the cooperative workcell. The solution methodology suggested to estimate the worker task execution time This estimation was based on an offline analysis of the worker and the industrial robot tasks in a simulation environment. The idea of analyzing the worker task execution time in a cooperative manufacturing scenario seems to be useful for scheduling, and for the physical safety.
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