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

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

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

[2] Walle A H. Remanufacturing marketing strategy and developing countries. Journal of Global Marketing, 1988, 1(4): 75–90

[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

[4] Branstetter L G, Drev M, Kwon N. Get with the program: softwaredriven innovation in traditional manufacturing. Management Science, 2019, 65(2): 541–558

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[29] Büyüközkan G, Göçer F. Digital supply chain: literature review and a proposed framework for future research. Computers in Industry, 2018, 97: 157–177

[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

[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

[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

[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

[34] Kshetri N. 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 2018, 39: 80–89

[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

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