A balanced-quantum inspired evolutionary algorithm for solving disassembly line balancing problem

Disassembly lines are the most efficient choice for recovering parts and components on a large scale from old products. The efficient utilization of disassembly line resources requires optimal selection and sequencing of disassembly tasks which constitutes the disassembly line balancing problem (DLBP). In this work, a novel Balanced Quantum-inspired evolutionary algorithm (Balanced-QEA) is proposed to optimize profit and workload balance for the DLBP. Quantum evolutionary algorithms (QEA) utilize stochastic solution representation in the form of q-bits to explore the solution space. The proposed approach differs from traditional QEA by strategically making multiple observations for a single quantum individual. This modification aims to address the weakness of traditional QEA by utilizing stochastic information in quantum solutions more effectively. The application of the proposed approach is illustrated numerically using an example of radio set to maximize profit and workload balance. For validating the utility of proposed modification, the performance of Balanced-QEA is compared with traditional QEA and five other popular evolutionary algorithms in the field of DLBP. The comparisons are done using benchmark disassembly instances of different scales. Results show that the proposed Balanced QEA is superior to QEA and other algorithms in terms of best solutions.