|
|
A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing |
Sijie LI(), You SHANG |
1School of Economics and Management, Southeast University, Nanjing 211189, China |
|
|
Abstract In this paper, a binary-extensible quality status encoding scheme, named IQSCT (IoT quality status code table), is proposed for the PCB-based product with available recovery options in remanufacturing. IQSCT is achieved by code evolution based on binary logic, in which the product flow and the quality information flow are integrated, and three key features of PCB-based product (PCB-module association, assemblydisassembly logic, and disassembly risk) are involved in production costing.With IQSCT, the manufacturer can have better decisions to reduce remanufacturing cost and improve resource utilization, which is verified by a case study based on the real data from BOM cost and corresponding estimation of Apple iPhone 11 series.
|
Keywords
Internet-of-Things
binary encoding scheme
binary logic bit operations
PCB-based products
remanufacturing
recovery option
|
Corresponding Author(s):
Sijie LI
|
Just Accepted Date: 16 July 2020
Issue Date: 13 July 2021
|
|
1 |
D Jia, S Li. Optimal decisions and distribution channel choice of closedloop supply chain when e-retailer offers online marketplace. Journal of Cleaner Production, 2020, 265: 121767
https://doi.org/10.1016/j.jclepro.2020.121767
|
2 |
A H Walle. Remanufacturing marketing strategy and developing countries. Journal of Global Marketing, 1988, 1(4): 75–90
https://doi.org/10.1300/J042v01n04_06
|
3 |
W Chen, B Kucukyazici, V Verter, M Jesús Sáenz. Supply chain design for unlocking the value of remanufacturing under uncertainty. European Journal of Operational Research, 2015, 247(3): 804–819
https://doi.org/10.1016/j.ejor.2015.06.062
|
4 |
L G Branstetter, M Drev, N Kwon. Get with the program: softwaredriven innovation in traditional manufacturing. Management Science, 2019, 65(2): 541–558
https://doi.org/10.1287/mnsc.2017.2960
|
5 |
A J Dweekat, G Hwang, J Park. A supply chain performance measurement approach using the internet of things: toward more practical SCPMS. Industrial Management & Data Systems, 2017, 117(2): 267–286
https://doi.org/10.1108/IMDS-03-2016-0096
|
6 |
Y Zhang, S Liu, Y Liu, H Yang, M Li, D Huisingh, L Wang. The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. Journal of Cleaner Production, 2018, 185: 562–575
https://doi.org/10.1016/j.jclepro.2018.02.061
|
7 |
M Jeihoonian, M K Zanjani, M Gendreau. Accelerating Benders decomposition for closed-loop supply chain network design: case of used durable products with different quality levels. European Journal of Operational Research, 2016, 251(3): 830–845
https://doi.org/10.1016/j.ejor.2015.12.052
|
8 |
M Jeihoonian, M K Zanjani, M Gendreau. Closed-loop supply chain network design under uncertain quality status: case of durable products. International Journal of Production Economics, 2017, 183: 470–486
https://doi.org/10.1016/j.ijpe.2016.07.023
|
9 |
K Subulan, A S Ta¸san, A Baykasoˇglu. A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 2015, 37: 243–264
https://doi.org/10.1016/j.jmsy.2014.09.001
|
10 |
K Subulan, A S Ta¸san, A Baykasoˇglu. Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming. Applied Mathematical Modelling, 2015, 39(9): 2661–2702
https://doi.org/10.1016/j.apm.2014.11.004
|
11 |
C Fang, X Liu, J Pei, W Fan, P M Pardalos. Optimal production planning in a hybrid manufacturing and recovering system based on the internet of things with closed loop supply chains. Operational Research, 2015, 16(3): 543–577
https://doi.org/10.1007/s12351-015-0213-x
|
12 |
K Govindan, P C Jha, K Garg. Product recovery optimization in closedloop supply chain to improve sustainability in manufacturing. International Journal of Production Research, 2015, 54(5): 1463–1486
https://doi.org/10.1080/00207543.2015.1083625
|
13 |
F Gu, B Ma, J Guo, P A Summers, P Hall. Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: an exploratory study. Waste Management, 2017, 68: 434–448
https://doi.org/10.1016/j.wasman.2017.07.037
|
14 |
Y T Chen, F T S Chan, S H Chung. An integrated closed-loop supply chain model with location allocation problem and product recycling decisions. International Journal of Production Research, 2014, 53(10): 3120–3140
https://doi.org/10.1080/00207543.2014.975849
|
15 |
M Rowshannahad, N Absi, S Dauzère-Pérès, B Cassini. Multi-item bilevel supply chain planning with multiple remanufacturing of reusable by-products. International Journal of Production Economics, 2018, 198: 25–37
https://doi.org/10.1016/j.ijpe.2018.01.014
|
16 |
A S Safaei, A Roozbeh, M M Paydar. A robust optimization model for the design of a cardboard closed-loop supply chain. Journal of Cleaner Production, 2017, 166: 1154–1168
https://doi.org/10.1016/j.jclepro.2017.08.085
|
17 |
I S Doolun, S G Ponnambalam, N Subramanian, G Kanagaraj. Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: automotive green supply chain empirical evidence. Computers & Operations Research, 2018, 98: 265–283
https://doi.org/10.1016/j.cor.2018.01.008
|
18 |
R Shankar, S Bhattacharyya, A Choudhary. A decision model for a strategic closed-loop supply chain to reclaim End-of-Life Vehicles. International Journal of Production Economics, 2018, 195: 273–286
https://doi.org/10.1016/j.ijpe.2017.10.005
|
19 |
M Radhi, G Zhang. Optimal configuration of remanufacturing supply network with return quality decision. International Journal of Production Research, 2015, 54(5): 1487–1502
https://doi.org/10.1080/00207543.2015.1086034
|
20 |
S Baptista, A P Barbosa-Póvoa, L F Escudero, M I Gomes, C Pizarro. On risk management of a two-stage stochastic mixed 0-1 model for the closed-loop supply chain design problem. European Journal of Operational Research, 2019, 274(1): 91–107
https://doi.org/10.1016/j.ejor.2018.09.041
|
21 |
Z Guiras, S Turki, N Rezg, A Dolgui. Optimization of two-level disassembly/ remanufacturing/assembly system with an integrated maintenance strategy. Applied Sciences, 2018, 8(5): 666
https://doi.org/10.3390/app8050666
|
22 |
Y Kazancoglu, Y D Ozkan-Ozen. Sustainable disassembly line balancing model based on triple bottom line. International Journal of Production Research, 2020, 58(14): 4246–4266
https://doi.org/10.1080/00207543.2019.1651456
|
23 |
E Masoudipour, H Amirian, R Sahraeian. A novel closed-loop supply chain based on the quality of returned products. Journal of Cleaner Production, 2017, 151: 344–355
https://doi.org/10.1016/j.jclepro.2017.03.067
|
24 |
A Niknejad, D Petrovic. Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 2014, 238(1): 143–154
https://doi.org/10.1016/j.ejor.2014.03.034
|
25 |
O Ondemir, S M Gupta. Quality management in product recovery using the Internet of Things: an optimization approach. Computers in Industry, 2014, 65(3): 491–504
https://doi.org/10.1016/j.compind.2013.11.006
|
26 |
M Al-Salem, A Diabat, D Dalalah, M Alrefaei. A closed-loop supply chain management problem: reformulation and piecewise linearization. Journal of Manufacturing Systems, 2016, 40: 1–8
https://doi.org/10.1016/j.jmsy.2016.04.001
|
27 |
H Yu, W D Solvang. Incorporating flexible capacity in the planning of a multi-product multi-echelon sustainable reverse logistics network under uncertainty. Journal of Cleaner Production, 2018, 198: 285–303
https://doi.org/10.1016/j.jclepro.2018.07.019
|
28 |
K Douzis, S Sotiriadis, E G M Petrakis, C Amza. Modular and generic IoT management on the cloud. Future Generation Computer Systems, 2018, 78: 369–378
https://doi.org/10.1016/j.future.2016.05.041
|
29 |
G Büyüközkan, F Göçer. Digital supply chain: literature review and a proposed framework for future research. Computers in Industry, 2018, 97: 157–177
https://doi.org/10.1016/j.compind.2018.02.010
|
30 |
M Abdel-Basset, G Manogaran, M Mai. Internet of Things (IoT) and its impact on supply chain: a framework for building smart, secure and efficient systems. Future Generation Computer Systems, 2018, 86: 614–628
https://doi.org/10.1016/j.future.2018.04.051
|
31 |
J Byun, S Woo, Y Tolcha, D Kim. Oliot EPCIS: engineering a web information system complying with EPC Information Services standard towards the Internet of Things. Computers in Industry, 2018, 94: 82–97
https://doi.org/10.1016/j.compind.2017.10.004
|
32 |
K Främling, M Maharjan. Standardized communication between intelligent products for the IoT. International Federation of Automatic Control Proceedings Volumes. 2013, 46(7): 157–162
https://doi.org/10.3182/20130522-3-BR-4036.00043
|
33 |
Y Liu, W Han, Y Zhang, L Li, J Wang, L Zheng. An Internet-of-Things solution for food safety and quality control: a pilot project in China. Journal of Industrial Information Integration, 2016, 3: 1–7
https://doi.org/10.1016/j.jii.2016.06.001
|
34 |
N Kshetri. 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 2018, 39: 80–89
https://doi.org/10.1016/j.ijinfomgt.2017.12.005
|
35 |
Y Zhang, Y Han, J Wen. SMER: a secure method of exchanging resources in heterogeneous internet of things. Frontiers of Computer Science, 2019, 13(6): 1198–1209
https://doi.org/10.1007/s11704-018-6524-3
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|