An assembly strategy scheduling method for human and robot coordinated cell manufacturing

Abstract

Purpose

The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High‐Mix, Low‐Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots.

Design/methodology/approach

A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and Multiple‐Objective Optimization (MOOP) method related Cost‐effectiveness analysis is adopted to select the optimal ones.

Findings

It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results.

Originality/value

The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors.

Citation

Chen, F., Sekiyama, K., Huang, J., Sun, B., Sasaki, H. and Fukuda, T. (2011), "An assembly strategy scheduling method for human and robot coordinated cell manufacturing", International Journal of Intelligent Computing and Cybernetics, Vol. 4 No. 4, pp. 487-510. https://doi.org/10.1108/17563781111186761