From 2015 and 2020 the number of industrial robots installed per year has increased by 9\% on the average. In 2024 number of installations is expected to reach 500,000 units. It is estimated that industrial robots will consume 0.5%-0.8% of total U.S. electricity demand in 2025. Industrial robots will become a significant source of energy consumption in the future. This research considers finding energy efficient schedules for a robotic manufacturing cell with two parallel machines and a material handling robot. The problem is to schedule a set of parts on the machines and plan the activities of the robot which performs all handling and loading-unloading operations in the cell.

A robot’s activities and its speed during each activity can be planned via a human-machine interface software. So, in this study, we consider robot speed decisions in addition to machine-part assignment, part sequencing and robot activity planning decisions. Speeding up the robot improves makespan objective while leading to higher energy consumption which can be expressed as a convex function of the speed.

We consider minimizing makespan and energy consumption objectives at the same time. We propose a mathematical model and develop three heuristic search algorithms that find efficient solutions for the problem. We test the computational performance of proposed solution methods. We also show that significant energy saving can be achieved via robot speed control strategy. This is a joint work with Dr. Sinan Gürel