Abstract
In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP) is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy information graph (DHIG). Then, combining the characteristic of the disassembly sequence, an entropy-based adaptive hybrid particle swarm optimization algorithm (AHPSO) is presented. In this algorithm, entropy is introduced to measure the changing tendency of population diversity, and the dimension learning, crossover and mutation operator are used to increase the probability of producing feasible disassembly solutions (FDS). Performance of the proposed methodology is tested on the primary problem instances available in the literature, and the results are compared with other evolutionary algorithms. The results show that the proposed algorithm is efficient to solve the complex DLBP.
Highlights
Line balancing problem (DLBP) is an efficient method for minimizing resources invested in disassembly and maximizing the automation level [1]
In order to show the motivation of the entropy, we make a comparison between the listed in Table 1 and both algorithms are executed 30 times
In terms of all objectives, the performance of the entropy-based AHPSO is better than the variable neighborhood search (VNS), especially about the objective 3 and objective 4
Summary
Line balancing problem (DLBP) is an efficient method for minimizing resources invested in disassembly and maximizing the automation level [1]. Some researchers obtain the disassembly sequence using the mathematical programming method in which a single object is considered [3,4], but it is to fall into the local optimal when the method is applied to solve the complex DLBP. A novel multi-objective ant colony optimization (MOACO) algorithm was used to solve DLBP and obtained good benefit in the practice of disassembly process [7]. The selection of the particle with good diversity, crossover rate and mutation rate are depended on the change of the entropy. This can increase the number of the optimal solutions and improve the speed of convergence.
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