Micro genetic algorithm with spatial crossover and correction schemes for constrained three-dimensional reader network planning

• We formulate a constrained 3-dimensional reader network planning (C3DRNP) problem.
• We propose a micro genetic algorithm (mGA) to solve the C3DRNP problem.
• The proposed mGA consists of novel spatial crossover and correction schemes.
• The obtained solution is guaranteed to achieve 100% tag coverage.
• The proposed mGA outperforms the particle swarm optimization method and conventional GA.

Due to the fast growing electronic commerce, the constrained three-dimensional reader network planning (C3DRNP) of the radio frequency identification (RFID) system for large warehouses is a subject that is worthy of study. A micro genetic algorithm (mGA) with novel spatial crossover and correction schemes is proposed to cope with this C3DRNP problem. The proposed algorithm is computationally efficient, which allows a frequent replacement of the RFID readers in the network to account for the fast turnaround time of the stored objects in the warehouse, and guarantees 100% tag coverage to avoid missing the records of the objects.The proposed algorithm is tested and compared with the existing methods such as the particle swarm optimization (PSO) method and the conventional GA (CGA) on solving several C3DRNP problems with various network sizes. The comparison results demonstrate the computational efficiency of the mGA and the effectiveness of the novel spatial crossover and correction schemes in searching the solution.