TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment

Abstract

The refurbishment market is rich in opportunities, the global refurbished smartphones market alone will be $38.9 billion by 2025. Refurbishing a product involves disassembling it to test the key parts and replacing those that are defective or worn. This restores the product to like-new conditions, so that it can be put on the market again at a lower price. Making this process quick and efficient is crucial. This paper presents a novel formulation of parallel disassembly problem that maximizes the degree of parallelism, the level of ergonomics, and how the workers' workload is balanced, while minimizing the disassembly time and the number of times the product has to be rotated. The problem is solved using the Tensorial Memetic Algorithm (TeMA), a novel two-stage many-objective (MaO) algorithm which encodes parallel disassembly plans by using third-order tensors. TeMA first splits the objectives into primary and secondary on the basis of a decision maker's preferences, and then finds Pareto-optimal compromises (seeds) of the primary objectives. In the second stage, TeMA performs a fine-grained local search that explores the objective space regions around the seeds, to improve the secondary objectives. TeMA was tested on two real-world refurbishment processes involving a smartphone and a washing machine. The experiments showed that, on average, TeMA is statistically more accurate than various efficient MaO algorithms in the decision maker's area of preference.

A new remanufacturing system scheduling model with diversified reprocessing routes using a hybrid meta-heuristic algorithm

“…Considering the characteristics of EOL products and improvement of the RMS efficiency, this study extends the configuration of Kim et al (2017) through parallel disassembly workstations and job-shop-type reprocessing shops. Parallel disassembly is becoming of interest, as it employs several workstations to disassemble products simultaneously, thus making the remanufacturing process effectively (Pistolesi and Lazzerini, 2019). Because EOL products experience varying operation conditions and usage environments during their service life, a batch of EOL products commonly involves defective components with different types and degrees of damage, which lead to various reprocessing routes.…”

Interference probability matrix for disassembly sequence planning under uncertain interference

“…A layered candidate set including both components and subassemblies can be obtained from the IM, as shown in Eq. (5).…”

“…Much research has been carried out on disassembly sequence planning [4,5,6]. It requires using a mathematical representation to describe the spatial relationships of a product's components.…”

Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

“…In [73], X. Han et al formulate active object detection in industrial settings as a sequential action decision process, and apply a deep reinforcement learning framework, the deep Q-network (DQN) with dueling architecture to solve this formulation, by learning an optimal action policy. A deep neural network based two-stage automated approach for estimating the remaining useful life (RUL) of bearings in industrial machinery is proposed in [74], and in [75], authors model disassembly sequence planning as an NP-hard (non-deterministic polynomial-time hardness) many-objective problem, and solve this using the tensorial memetic algorithm that combines genetic computations with local search. In [76], authors formulate long-term and short-term utility of stakeholders in manufacturing service sharing for Industrial Internet platforms as a multi-objective optimization problem, and solve this using an improved non-dominated sorting genetic algorithm that combines Tabu search (an improvement over local neighbourhood searching) and K-means (which segments n observations into k clusters).…”