| Title | Scheduling and process planning for the dismantling shop with flexible disassembly mode and recovery level |
|---|---|
| ID_Doc | 10742 |
| Authors | Ehm, F |
| Title | Scheduling and process planning for the dismantling shop with flexible disassembly mode and recovery level |
| Year | 2024 |
| Published | |
| Abstract | The challenges of climate change and resource scarcity have led policymakers around the globe to strengthen the concept of circular economy. Legislation towards extended producer responsibility means that recovery of parts and material from end-of-life (EOL) products is increasingly imposed as a mandatory task for manufacturers across various industries. Consequently, EOL decision-making has emerged as a relevant topic in management science. Recent research has addressed the environmental impact of EOL operations by considering energy usage or carbon emissions from variable disassembly sequences and recovery options. This paper deals with selective disassembly planning and scheduling for multiple products in a dismantling shop and considers the environmental impact that results from the process planning stage. It extends previous formulations by considering flexible disassembly levels and modes. In the multi-objective optimization problem, there is a trade -off between potential time savings, the ecological impact of process-related emissions, and the environmental penalty associated with the recovery of damaged parts, unseparated modules, or unpurified materials. A mathematical formulation is presented and demonstrated using a case study from end-of-life engine disassembly. Next, a multi-objective genetic algorithm (MOGA) is developed and tested using synthetic disassembly problem data. As shown in computational experiments, MOGA outperforms the exact model in more complex problem settings and produces competitive results in a fraction of the time required by the commercial solver. |
| https://doi.org/10.1016/j.cie.2024.109927 |
Similar Articles
| ID | Score | Article |
|---|---|---|
28032
|
Mamaghani, EJ; Boucher, X End-of-Life Product Recovery Optimization of Disassembled Parts Based on Collaborative Decision-Making(2021) | |
| 17226
|
Mao, J; Hong, D; Chen, Z; Ma, CH; Li, WW; Wang, J Disassembly sequence planning of waste auto parts(2021)Journal Of The Air & Waste Management Association, 71, 5 | |
| 9229
|
Hartono, N; Ramirez, FJ; Pham, DT Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots(2023)Automation, 4.0, 4 | |
| 9599
|
Lu, Q; Ren, YP; Jin, HY; Meng, LL; Li, L; Zhang, CY; Sutherland, JW A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem(2020) | |
| 13560
|
Gao, YC; Lou, SH; Zheng, H; Tan, JR A data-driven method of selective disassembly planning at end-of-life under uncertainty(2023)Journal Of Intelligent Manufacturing, 34, 2 | |
| 30028
|
Shabanpour, N; Colledani, M Integrated workstation design and buffer allocation in disassembly systems for remanufacturing(2018) | |
| 7330
|
Chand, M; Ravi, C A state-of-the-art literature survey on artificial intelligence techniques for disassembly sequence planning(2023) | |
| 9382
|
Mustajib, MI; Ciptomulyono, U; Kurniati, N A Model for Remanufacturing Process Planning Under Quality Loss and Emissions Constraint with Multi-graded of Incoming Cores(2024) | |
| 1793
|
Cong, L; Zhao, F; Sutherland, JW Integration of dismantling operations into a value recovery plan for circular economy(2017) | |
| 16822
|
Cong, L; Zhao, F; Sutherland, JW Value recovery from end-of-use products facilitated by automated dismantling planning(2017)Clean Technologies And Environmental Policy, 19, 7 |