Remanufacturing scheduling under uncertainty considering remanufacturability assessment with adaptive hybrid optimization algorithm

Abstract

Remanufacturing enables the values contained in end-of-life products to be developed and utilized to the maximum extent, which is greatly significant to economic and social development. The remanufacturing process is characterized by uncertainties such as the quality of end-of-life products and the required remanufacturing time. Some studies have focused on remanufacturing scheduling under uncertainty. However, these studies ignored the direct effects of uncertainties on the assessment of remanufacturability and the selection of remanufacturing lines. Therefore, this study proposed a new decision tree-based remanufacturing scheduling model under uncertainty considering remanufacturability assessment, which constructs decision trees and combines fuzzy numbers to assess remanufacturability and select appropriate remanufacturing lines. Experiments have shown that the proposed model increases the total profits by approximately 2.8 %. To solve this model effectively, an adaptive hybrid optimization algorithm is proposed, with a new solution representation scheme, an adaptive adjustment function and a new population updating strategy. Simulated comparison experiments with other baseline algorithms and a real case study demonstrate that, the proposed algorithm has better performance in solution exploration and has superior stability in solving the remanufacturing scheduling model proposed in this study. Specifically, for improving the efficiency of remanufacturing, the proposed algorithm performs 0.5 % better than the differential evolutionary algorithm, 3.3 % better than the teaching-learning-based optimization algorithm, 0.2 % better than the extended particle swarm optimization algorithm, 1.7 % better than the improved ant colony optimization algorithm, and 2.7 % better than the simulated annealing algorithm, approximately. Finally, a real case study demonstrates the superior performance of the proposed model and algorithm in real industrial applications.