Informace o publikaci
Scheduling of mobile robots for transportation and manufacturing tasks
| Autoři | |
|---|---|
| Rok publikování | 2019 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Journal of Heuristics |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | http://doi.org/10.1007/s10732-018-9391-z |
| Doi | http://dx.doi.org/10.1007/s10732-018-9391-z |
| Klíčová slova | Scheduling; Mobile robots; Hybrid heuristic; Genetic algorithm; Tabu search; MIP; Flexible manufacturing systems |
| Přiložené soubory | |
| Popis | Current manufacturing systems have a tendency to become more and more flexible to adapt to the needs of product diversification. Such a system, e.g. flexible manufacturing system, consists of a number of automatic machines, material handling devices such as automated guided vehicles (AGVs) or mobile robots, and a central control computer. Mobile robots, as well as AGVs, can move around in their working space to transport components among machines. The main novelty of this work is that mobile robots can also execute various value-added tasks without human intervention thanks to their manipulation arms which is not possible for AGVs. This new characteristic certainly makes the problem more complex and computationally expensive. To utilize these manufacturing systems in an efficient manner, it is necessary to schedule transportation of product components by mobile robots and to schedule processing of products on machines possibly by mobile robots. Consequently, the key innovation of this work lies in consideration of three interrelated sub-problems which must be solved. They include computing the sequence of operations on machines, the robot assignment for transportation, and the robot assignment for processing. To achieve this goal, a computationally efficient hybrid heuristic method combining genetic algorithm and tabu search is developed to solve the problem considering makespan minimization. A mixed-integer programming (MIP) model is formulated. Another combined method using results of the hybrid heuristic is proposed to speed up the solving of MIP model. The quality of hybrid heuristic’s solutions is compared and evaluated by using those of the MIP model as reference points. |