Grey Wolf Optimization Algorithm for Single Mobile Robot Scheduling

Development of reliable and efficient material transport system is one of the basic requirements for creating an intelligent manufacturing environment. Nowadays, intelligent mobile robots have been widely used as one of the components to satisfy this requirement. In this paper, a methodology based on Grey Wolf Optimization (GWO) algorithm is proposed in order to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) single mobile robot scheduling problem. The performance criterion is to minimize total transportation time of the mobile robot while it performs internal transport of raw materials, goods, and parts in manufacturing system. The scheduling plans are obtained in Matlab environment and tested by Khepera II mobile robot system within a static laboratory model of manufacturing environment. Experimental results show the applicability and effectiveness of the developed intelligent approach in real world conditions

Mathematical Formulation for Mobile Robot Scheduling Problem in a Manufacturing Cell

Part 1: Production ProcessInternational audienceThis paper deals with the problem of finding optimal feeding sequence in a manufacturing cell with feeders fed by a mobile robot with manipulation arm. The performance criterion is to minimize total traveling time of the robot in a given planning horizon. Besides, the robot has to be scheduled in order to keep production lines within the cell working without any shortage of parts fed from feeders. A mixed-integer programming (MIP) model is developed to find the optimal solution for the problem. In the MIP formulation, a method based on the (s, Q) inventory system is applied to define time windows for multiple-part feeding tasks. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed MIP model