TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment
| Autor: | Beatrice Lazzerini, Francesco Pistolesi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
many-objective (MaO) optimization
Mathematical optimization product refurbishment Linear programming Computer science Process (engineering) 02 engineering and technology Evolutionary computation Genetic algorithm 0202 electrical engineering electronic engineering information engineering Local search (optimization) Electrical and Electronic Engineering business.industry 020208 electrical & electronic engineering Disassembly evolutionary computation memetic algorithm (MA) tensor Computer Science Applications Control and Systems Engineering Product (mathematics) Key (cryptography) Memetic algorithm business Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 15:3743-3753 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2019.2904631 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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