Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm


Autoři: Yubang Liu aff001;  Shouwen Ji aff001;  Zengrong Su aff002;  Dong Guo aff003
Působiště autorů: School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China aff001;  Aviation Business Department, Beijing capital international airport Company Limited, Beijing, China aff002;  School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226161

Souhrn

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.

Klíčová slova:

Algorithms – Electricity – Entropy – Genetic algorithms – Mathematical models – Optimization – Population genetics – Species diversity


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PLOS One


2019 Číslo 12