A dynamic scheduling approach for optimizing the material handling operations in a robotic cell. (November 2018)

• Record Type: Journal Article
• Authors: Yan, Pengyu; Liu, Shi Qiang; Sun, Tengfei; Ma, Kaiyuan
• Is Part Of: Computers & operations research. Volume 99(2018)
• Page Start: 166
• Page End: 177
• Publication Date: 2018-11
• Subjects: Dynamic scheduling -- Robotic cells -- Material handling -- Unexpected new jobs; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.05.009
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: We investigate a new real-time dynamic robotic scheduling problem with the consideration of newly arriving jobs, transportation operations of a robot and processing time windows. A strengthened mixed integer programming model is developed by adding a set of speed-up constraints. An iterative algorithm based on the characteristics of the problem is proposed to solve the problem in an efficient way. Computational results validate effectiveness and efficiency of the strengthened MIP model and the iterative algorithm. Abstract: This paper investigates a real-time dynamic job-shop scheduling problem in a robotic cell, in which multiple jobs enter into the cell with unexpected arriving rates. Different from classical flow-shop and job-shop scheduling problems, the jobs' transportation handled by a robot must be considered. Another characteristic is that the jobs' processing times are not constant values but confined in time-window constraints. To efficiently solve this problem in real time, the original schedule is restricted to zero changes. The problem is formulated as a sophisticated Mixed Integer Programming (MIP) model in which the new jobs' processing and transportation operations are inserted into the available time intervals of the original schedule. To strengthen the MIP model, speed-up constraints are added by taking advantage of specific relationships between the available time intervals arranged for a job's processing and transportation operations. Furthermore, an exact iterative algorithm is proposed, which starts with a relaxed solution of the MIP model and iteratively adds essential robot handling capacity constraints back to the relaxed MIP model until an optimal solution is found. Computational results validate effectiveness and efficiency of the strengthened MIP model and the iterative algorithm.