The DOI matches its Crossref record.
Disassembly line balancing problem: a review of the state of the art and future directions
Open accessAbstract
The disassembly line balancing (DLB) problem assigns the set of tasks to each workstation for each product to be disassembled and aims at attaining several objectives, such as minimising the number of workstations, ensuring similar idle time at each workstation and removing hazardous parts/highly demanded components at the earliest moments possible. Over the past two decades, the DLB problem and its variants have grown ever more popular for researchers and practitioners of environmentally conscious manufacturing. Yet, the problem characteristics and assumptions vary widely and there is no literature review to classify the existing articles accordingly. Hence, a comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework of the past, and to shed light on future directions. To do so, 116 studies published in proceedings and journals since 1999 are selected and reviewed. The papers are then analysed and categorised to construct a useful foundation of past research. Finally, trends and gaps in the literature are identified to clarify and to suggest future research opportunities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Or, T., Gourley, S. W. D., Kaliyappan, K., Yu, A., & Chen, Z. (2020) · Carbon Energy · doi:10.1002/cey2.29 · Cited by 559
Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review
Ai, Z., Nee, A. Y. C., & Ong, S. K. (2024) · Automation · doi:10.3390/automation5040028 · Cited by 16
Metadata
- Authors
- Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2018-01-26
- Volume
- 57
- Issue
- 15-16
- Pages
- 4805-4827
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2789796155
- Cited by
- 220
- References
- 0
- FWCI
- 18.90
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Disassembly line balancing problem: a review of the state of the art and future directions. https://app.cochat.ai/papers/10.1080/00207543.2018.1428775
- The paper
- Özceylan, E., Kalayci, C. B., Güngör, A., & Gupta, S. M. (2019). Disassembly line balancing problem: a review of the state of the art and future directions. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1428775
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.