A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing

Sijie LI, You SHANG

PDF(1315 KB)
PDF(1315 KB)
Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (5) : 155615. DOI: 10.1007/s11704-020-9175-0
RESEARCH ARTICLE

A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing

Author information +
History +

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

Cite this article

Download citation ▾
Sijie LI, You SHANG. A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing. Front. Comput. Sci., 2021, 15(5): 155615 https://doi.org/10.1007/s11704-020-9175-0

References

[1]
Jia D, Li S. Optimal decisions and distribution channel choice of closedloop supply chain when e-retailer offers online marketplace. Journal of Cleaner Production, 2020, 265: 121767
CrossRef Google scholar
[2]
Walle A H. Remanufacturing marketing strategy and developing countries. Journal of Global Marketing, 1988, 1(4): 75–90
CrossRef Google scholar
[3]
Chen W, Kucukyazici B, Verter V, Jesús Sáenz M. Supply chain design for unlocking the value of remanufacturing under uncertainty. European Journal of Operational Research, 2015, 247(3): 804–819
CrossRef Google scholar
[4]
Branstetter L G, Drev M, Kwon N. Get with the program: softwaredriven innovation in traditional manufacturing. Management Science, 2019, 65(2): 541–558
CrossRef Google scholar
[5]
Dweekat A J, Hwang G, Park J. A supply chain performance measurement approach using the internet of things: toward more practical SCPMS. Industrial Management & Data Systems, 2017, 117(2): 267–286
CrossRef Google scholar
[6]
Zhang Y, Liu S, Liu Y, Yang H, Li M, Huisingh D, Wang L. The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. Journal of Cleaner Production, 2018, 185: 562–575
CrossRef Google scholar
[7]
Jeihoonian M, Zanjani M K, Gendreau M. 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
CrossRef Google scholar
[8]
Jeihoonian M, Zanjani M K, Gendreau M. Closed-loop supply chain network design under uncertain quality status: case of durable products. International Journal of Production Economics, 2017, 183: 470–486
CrossRef Google scholar
[9]
Subulan K, Ta¸san A S, Baykasoˇglu A. 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
CrossRef Google scholar
[10]
Subulan K, Ta¸san A S, Baykasoˇglu A. 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
CrossRef Google scholar
[11]
Fang C, Liu X, Pei J, Fan W, Pardalos P M. 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
CrossRef Google scholar
[12]
Govindan K, Jha P C, Garg K. Product recovery optimization in closedloop supply chain to improve sustainability in manufacturing. International Journal of Production Research, 2015, 54(5): 1463–1486
CrossRef Google scholar
[13]
Gu F, Ma B, Guo J, Summers P A, Hall P. 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
CrossRef Google scholar
[14]
Chen Y T, Chan F T S, Chung S H. 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
CrossRef Google scholar
[15]
Rowshannahad M, Absi N, Dauzère-Pérès S, Cassini B. Multi-item bilevel supply chain planning with multiple remanufacturing of reusable by-products. International Journal of Production Economics, 2018, 198: 25–37
CrossRef Google scholar
[16]
Safaei A S, Roozbeh A, Paydar M M. A robust optimization model for the design of a cardboard closed-loop supply chain. Journal of Cleaner Production, 2017, 166: 1154–1168
CrossRef Google scholar
[17]
Doolun I S, Ponnambalam S G, Subramanian N, Kanagaraj G. 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
CrossRef Google scholar
[18]
Shankar R, Bhattacharyya S, Choudhary A. 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
CrossRef Google scholar
[19]
Radhi M, Zhang G. Optimal configuration of remanufacturing supply network with return quality decision. International Journal of Production Research, 2015, 54(5): 1487–1502
CrossRef Google scholar
[20]
Baptista S, Barbosa-Póvoa A P, Escudero L F, Gomes M I, Pizarro C. 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
CrossRef Google scholar
[21]
Guiras Z, Turki S, Rezg N, Dolgui A. Optimization of two-level disassembly/ remanufacturing/assembly system with an integrated maintenance strategy. Applied Sciences, 2018, 8(5): 666
CrossRef Google scholar
[22]
Kazancoglu Y, Ozkan-Ozen Y D. Sustainable disassembly line balancing model based on triple bottom line. International Journal of Production Research, 2020, 58(14): 4246–4266
CrossRef Google scholar
[23]
Masoudipour E, Amirian H, Sahraeian R. A novel closed-loop supply chain based on the quality of returned products. Journal of Cleaner Production, 2017, 151: 344–355
CrossRef Google scholar
[24]
Niknejad A, Petrovic D. Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 2014, 238(1): 143–154
CrossRef Google scholar
[25]
Ondemir O, Gupta S M. Quality management in product recovery using the Internet of Things: an optimization approach. Computers in Industry, 2014, 65(3): 491–504
CrossRef Google scholar
[26]
Al-Salem M, Diabat A, Dalalah D, Alrefaei M. A closed-loop supply chain management problem: reformulation and piecewise linearization. Journal of Manufacturing Systems, 2016, 40: 1–8
CrossRef Google scholar
[27]
Yu H, Solvang W D. 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
CrossRef Google scholar
[28]
Douzis K, Sotiriadis S, Petrakis E G M, Amza C. Modular and generic IoT management on the cloud. Future Generation Computer Systems, 2018, 78: 369–378
CrossRef Google scholar
[29]
Büyüközkan G, Göçer F. Digital supply chain: literature review and a proposed framework for future research. Computers in Industry, 2018, 97: 157–177
CrossRef Google scholar
[30]
Abdel-Basset M, Manogaran G, Mai M. 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
CrossRef Google scholar
[31]
Byun J, Woo S, Tolcha Y, Kim D. 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
CrossRef Google scholar
[32]
Främling K, Maharjan M. Standardized communication between intelligent products for the IoT. International Federation of Automatic Control Proceedings Volumes. 2013, 46(7): 157–162
CrossRef Google scholar
[33]
Liu Y, Han W, Zhang Y, Li L,Wang J, Zheng L. 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
CrossRef Google scholar
[34]
Kshetri N. 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 2018, 39: 80–89
CrossRef Google scholar
[35]
Zhang Y, Han Y, Wen J. SMER: a secure method of exchanging resources in heterogeneous internet of things. Frontiers of Computer Science, 2019, 13(6): 1198–1209
CrossRef Google scholar

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(1315 KB)

Accesses

Citations

Detail

Sections
Recommended

/