A hybrid metaheuristic algorithm to optimise a real-world robotic cell

In this paper, a real-world robotic cell is investigated by transforming it into a special job shop with a set of stationary robots for manufacturing the parts of a product (i.e., operations of a job) at multiple operational stages. In addition, this robotic cell contains a particular mobile robot to transport the parts among stationary robots inside the cell as well as a depot (for initialising the production) and a stockpile (for stocking the complete products) outside the cell. Thus, a new scheduling problem called Blocking Job Shop Scheduling problem with Robotic Transportation (BJSSRT) is proposed. A numerical example is presented to illustrate the characteristics and complexity of BJSSRT. According to the problem properties, four types of robotic movements are defined for a mobile robot in an operation's execution: processing-purpose, depot-purpose, return-purpose and stocking-purpose. By satisfying complex feasibility conditions, an innovative graph-based constructive algorithm is developed to produce a good feasible BJSSRT schedule. Embedded with the constructive algorithm, a hybrid Tabu Search and Threshold Accepting metaheuristic algorithm is developed to find a near-optimal solution in an efficient way. The proposed BJSSRT methodology has practical benefits in modelling the automated production system using stationary and mobile robots, especially in manufacturing and mining industries. (C) 2016 Elsevier Ltd. All rights reserved.