Abstract
Circular supply chain (CSC) networks improve sustainability and create socially responsible enterprises through recycling, harvesting, and refurbishing. This study develops a Lagrangian relaxation (LR) algorithm for solving location-inventory-routing (LIR) problems with heterogeneous vehicles in multi-period and multi-product sustainable CSC networks. The proposed Artificial Internet of Things (AIoT) enabled sustainable CSC is designed to increase network performance and create a secure and traceable environment. For the first time, an LR algorithm is proposed to solve the LIR problems in an AIoT-enabled CSC network with storage, backorder shortage, split-delivery, and time window potentials. Sixteen small- and medium-size simulated problems were produced to assess the performance of the proposed algorithm relative to the GAMS software. The results show the proposed algorithm can solve the small- and medium-size problems as effectively as GAMS software but faster and more efficiently. In addition, eight large-size simulation problems were produced and solved by the algorithm. While the GAMS software failed to solve the large-size problems, the LR algorithm solved them efficiently and successfully.
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Afra, A. P., & Behnamian, J. (2021). Lagrangian heuristic algorithm for green multi-product production routing problem with reverse logistics and remanufacturing. Journal of Manufacturing Systems, 58, 33–43.
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308, 7–39.
Amini, H., & Kianfar, K. (2022). A variable neighborhood search based algorithm and game theory models for green supply chain design. Applied Soft Computing, 119, 108615.
Bairagi, B. (2022). A novel MCDM model for warehouse location selection in supply chain management. Decision Making: Applications in Management and Engineering, 5(1), 194–207.
Benyoucef, L., Xie, X., & Tanonkou, G. A. (2013). Supply chain network design with unreliable suppliers: A Lagrangian relaxation-based approach. International Journal of Production Research, 51(21), 6435–6454.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260, 120842.
Bouzid, M. C., Haddadene, H. A., & Salhi, S. (2017). An integration of Lagrangian split and VNS: The case of the capacitated vehicle routing problem. Computers & Operations Research, 78, 513–525.
Chao, C., Zhihui, T., & Baozhen, Y. (2019). Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network. Annals of Operations Research, 273(1–2), 111–134.
Chen, J. I. Z. (2021). The implementation to intelligent linkage service over AIoT hierarchical for material flow management. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2207–2219.
Diabat, A., Battaïa, O., & Nazzal, D. (2015). An improved Lagrangian relaxation-based heuristic for a joint location-inventory problem. Computers & Operations Research, 61, 170–178.
Fang, C., Liu, X., Pardalos, P. M., & Pei, J. (2016). Optimization for a three-stage production system in the Internet of Things: Procurement, production and product recovery, and acquisition. The International Journal of Advanced Manufacturing Technology, 83(5), 689–710.
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2018). Multi-objective stochastic closed-loop supply chain network design with social considerations. Applied Soft Computing, 71, 505–525.
Fisher, M. L. (2004). The Lagrangian relaxation method for solving integer programming problems. Management science, 50(12_supplement), 1861–1871.
Forouzanfar, F., Tavakkoli-Moghaddam, R., Bashiri, M., Baboli, A., & Molana, S. H. (2018). New mathematical modeling for a location–routing–inventory problem in a multi-period closed-loop supply chain in a car industry. Journal of Industrial Engineering International, 14(3), 537–553.
Fu, Y. M., & Diabat, A. (2015). A Lagrangian relaxation approach for solving the integrated quay crane assignment and scheduling problem. Applied Mathematical Modelling, 39(3–4), 1194–1201.
Ghasemi, P., Goodarzian, F., & Abraham, A. (2022). A new humanitarian relief logistic network for multi-objective optimization under stochastic programming. Applied Intelligence, 52, 13729–13762. https://doi.org/10.1007/s10489-022-03776-x
Ghorbani, A., & Jokar, M. R. A. (2016). A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Computers & Industrial Engineering, 101, 116–127.
Gomes, P., Magaia, N., & Neves, N. (2020). Industrial and artificial internet of things with augmented reality. In G. Mastorakis, C. Mavromoustakis, J. Batalla, & E. Pallis (Eds.), Convergence of Artificial Intelligence and the Internet of Things. Internet of Things. Springer. https://doi.org/10.1007/978-3-030-44907-0_13
Goodarzian, F., Navaei, A., Ehsani, B., Ghasemi, P., & Muñuzuri, J. (2022). Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: Artificial intelligence-based solutions. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04713-4
Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production, 242, 118317.
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: Insights from the review of academic literature and social media discussions. Annals of Operations Research, 308, 177–213.
Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308, 215–274.
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part e: Logistics and Transportation Review, 134, 101764.
Hasanzadeh, H., & Bashiri, M. (2016). An efficient network for disaster management: Model and solution. Applied Mathematical Modelling, 40(5–6), 3688–3702.
Hassannayebi, E., Zegordi, S. H., & Yaghini, M. (2016). Train timetabling for an urban rail transit line using a Lagrangian relaxation approach. Applied Mathematical Modelling, 40(23–24), 9892–9913.
Hassija, V., Chamola, V., Gupta, V., Jain, S., & Guizani, N. (2020). A survey on supply chain security: Application areas, security threats, and solution architectures. IEEE Internet of Things Journal, 8(8), 6222–6246.
Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95–105.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93–103.
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 178–191.
Kannan, D., Mina, H., Nosrati-Abarghooee, S., & Khosrojerdi, G. (2020). Sustainable circular supplier selection: A novel hybrid approach. The Science of the Total Environment, 722, 137936–137936.
Karakostas, P., Sifaleras, A., & Georgiadis, M. C. (2018). Basic VNS algorithms for solving the pollution location inventory routing problem. In International Conference on Variable Neighborhood Search, (pp. 64–76). Springer.
Karakostas, P., Sifaleras, A., & Georgiadis, M. C. (2019). A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing. Computers & Chemical Engineering, 126, 263–279.
Karakostas, P., Sifaleras, A., & Georgiadis, M. C. (2020). Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem. Optimization Letters. https://doi.org/10.1007/s11590-020-01630-y
Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93–103.
Keshavarz, T., Savelsbergh, M., & Salmasi, N. (2015). A branch-and-bound algorithm for the single machine sequence-dependent group scheduling problem with earliness and tardiness penalties. Applied Mathematical Modelling, 39(20), 6410–6424.
Keshavarz-Ghorbani, F., & Pasandideh, S. H. R. (2021). A Lagrangian relaxation algorithm for optimizing a bi-objective agro-supply chain model considering CO2 emissions. Annals of Operations Research, 314(2), 497–527.
Kheirabadi, M., Naderi, B., Arshadikhamseh, A., & Roshanaei, V. (2019). A mixed-integer program and a Lagrangian-based decomposition algorithm for the supply chain network design with quantity discount and transportation modes. Expert Systems with Applications, 137, 504–516.
Kunnumkal, S., & Talluri, K. (2019). A strong Lagrangian relaxation for general discrete-choice network revenue management. Computational Optimization and Applications, 73(1), 275–310.
Lee, Y., Hu, J., & Lim, M. K. (2020). Maximising the circular economy and sustainability outcomes: An end-of-life tyre recycling outlets selection model. International Journal of Production Economics, 232, 107965.
Long, Y., & Liao, H. (2021). A social participatory allocation network method with partial relations of alternatives and its application in sustainable food supply chain selection. Applied Soft Computing, 109, 107550.
Ma, H., & Li, X. (2018). Closed-loop supply chain network design for hazardous products with uncertain demands and returns. Applied Soft Computing, 68, 889–899.
Magaia, N., Fonseca, R., Muhammad, K., Segundo, A. H. F. N., Neto, A. V. L., & de Albuquerque, V. H. C. (2020). Industrial internet-of-things security enhanced with deep learning approaches for smart cities. IEEE Internet of Things Journal, 8(8), 6393–6405.
Mardan, E., Govindan, K., Mina, H., & Gholami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. Journal of Cleaner Production, 235, 1499–1514.
Mina, H., Kannan, D., Gholami-Zanjani, S. M., & Biuki, M. (2021). Transition towards circular supplier selection in petrochemical industry: A hybrid approach to achieve sustainable development goals. Journal of Cleaner Production, 286, 125273.
Miranda, P. A., & Garrido, R. A. (2008). Valid inequalities for Lagrangian relaxation in an inventory location problem with stochastic capacity. Transportation Research Part e: Logistics and Transportation Review, 44(1), 47–65.
Nair, A.K., John, C., Sahoo, J. (2022). Implementation of Intelligent IoT. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (Eds) AI and IoT for Sustainable Development in Emerging Countries Lecture Notes on Data Engineering and Communications Technologies, Vol. 105, Springer. https://doi.org/10.1007/978-3-030-90618-4_2
Nasr, A. K., Tavana, M., Alavi, B., & Mina, H. (2021). A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. Journal of Cleaner Production, 287, 124994.
Niu, H., Zhou, X., & Tian, X. (2018). Coordinating assignment and routing decisions in transit vehicle schedules: A variable-splitting Lagrangian decomposition approach for solution symmetry breaking. Transportation Research Part b: Methodological, 107, 70–101.
Özer, A. H. (2021). A fair, preference-based posted price resale e-market model and clearing heuristics for circular economy. Applied Soft Computing, 106, 107308.
Rafie-Majd, Z., Pasandideh, S. H. R., & Naderi, B. (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & Chemical Engineering, 109, 9–22.
Rayat, F., Musavi, M., & Bozorgi-Amiri, A. (2017). Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach. Applied Soft Computing, 59, 622–643.
Reimann, M., Xiong, Y., & Zhou, Y. (2019). Managing a closed-loop supply chain with process innovation for remanufacturing. European Journal of Operational Research, 276(2), 510–518.
Samanta, B., & Giri, B. C. (2021). A two-echelon supply chain model with price and warranty dependent demand and pro-rata warranty policy under cost sharing contract. Decision Making: Applications in Management and Engineering, 4(2), 47–75.
Saragih, N. I., Bahagia, N., & Syabri, I. (2019). A heuristic method for location-inventory-routing problem in a three-echelon supply chain system. Computers & Industrial Engineering, 127, 875–886.
Shambayati, H., Nikabadi, M. S., Firouzabadi, S. M. A. K., Rahmanimanesh, M., & Saberi, S. (2022). Optimization of virtual closed-loop supply chain under uncertainty: Application of IoT. Kybernetes. https://doi.org/10.1108/K-06-2021-0487
Torkaman, S., Ghomi, S. F., & Karimi, B. (2018). Hybrid simulated annealing and genetic approach for solving a multi-stage production planning with sequence-dependent setups in a closed-loop supply chain. Applied Soft Computing, 71, 1085–1104.
Vahdani, B., Veysmoradi, D., Noori, F., & Mansour, F. (2018). Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International Journal of Disaster Risk Reduction, 27, 290–306.
Wu, W., Zhou, W., Lin, Y., Xie, Y., & Jin, W. (2021). A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption. Expert Systems with Applications, 166, 114034.
Yan, R. (2017). Optimization approach for increasing revenue of perishable product supply chain with the Internet of Things. Industrial Management & Data Systems, 117(4), 729–741.
Yang, S., Ning, L., Shang, P., & Tong, L. C. (2020). Augmented Lagrangian relaxation approach for logistics vehicle routing problem with mixed backhauls and time windows. Transportation Research Part e: Logistics and Transportation Review, 135, 101891.
Yavari, M., Enjavi, H., & Geraeli, M. (2020). Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products. Research in Transportation Business & Management, 37, 100552.
Zandkarimkhani, S., Mina, H., Biuki, M., & Govindan, K. (2020). A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03677-7
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part e: Logistics and Transportation Review, 89, 182–214.
Zhang, C., Gao, Y., Yang, L., Gao, Z., & Qi, J. (2020). Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation. Transportation Research Part b: Methodological, 134, 64–92.
Zhang, G., Zhang, Y., Xu, X., & Zhong, R. Y. (2018). An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services. Journal of Manufacturing Systems, 48, 122–133.
Zhang, Y., Diabat, A., & Zhang, Z. H. (2021a). Reliable closed-loop supply chain design problem under facility-type-dependent probabilistic disruptions. Transportation Research Part b: Methodological, 146, 180–209.
Zhang, Y., Ramanathan, L., & Maheswari, M. (2021b). A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04412-6
Zhang, Z. H., Jiang, H., & Pan, X. (2012). A Lagrangian relaxation based approach for the capacitated lot sizing problem in closed-loop supply chain. International Journal of Production Economics, 140(1), 249–255.
Zhang, Z. H., & Unnikrishnan, A. (2016). A coordinated location-inventory problem in closed-loop supply chain. Transportation Research Part b: Methodological, 89, 127–148.
Zhao, J., & Ke, G. Y. (2017). Incorporating inventory risks in location-routing models for explosive waste management. International Journal of Production Economics, 193, 123–136.
Zheng, M. M., Li, W., Liu, Y., & Liu, X. (2020). A Lagrangian heuristic algorithm for sustainable supply chain network considering CO2 emission. Journal of Cleaner Production, 270, 122409.
Zheng, X., Yin, M., & Zhang, Y. (2019). Integrated optimization of location, inventory and routing in supply chain network design. Transportation Research Part b: Methodological, 121, 1–20.
Acknowledgements
Dr. Santos-Arteaga is grateful for the support received from the María Zambrano contract of the Universidad Complutense de Madrid financed by the Ministerio de Universidades with funding from the European Union Next Generation program.
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Appendix
Appendix
1.1 Proposed algorithm for data simulation
Parameters | Probabilistic distribution function |
---|---|
\(FSP_{it}\) | \(10^{3} \times round(uniform(3 \times 10^{3} ,4 \times 10^{3} ));\) |
\(FMN_{bjc}\) | \(\begin{gathered} loop(j, \hfill \\ \quad \quad \quad loop(b, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad loop(c, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad if(ord(c) < = 1, \hfill \\ FMN(b,j,c) = 10^{6} \times round(uniform(1.4 \times 10^{2} ,2.1 \times 10^{2} )); \hfill \\ else \hfill \\ FMN(b,j,c) = 10^{6} \times round(1.1 \times FMN(b,j,c - 1) \times 10^{ - 6} ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(FDST_{l}\) | \(10^{5} \times round(uniform(1.5 \times 10^{3} ,2.2 \times 10^{3} ));\) |
\(FCL_{f}\) | \(10^{4} \times round(uniform(2.5 \times 10^{3} ,3.0 \times 10^{3} ));\) |
\(FRC_{bdc}\) | \(\begin{gathered} loop(b, \hfill \\ \quad \quad \quad \quad loop(d, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad loop(c, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad if(ord(c) < = 1, \hfill \\ FRC(b,d,c) = 10^{6} \times round(uniform(1.0 \times 10^{2} ,1.4 \times 10^{2} )); \hfill \\ else \hfill \\ FRC(b,d,c) = 10^{6} \times round(1.1 \times FRC(b,d,c - 1) \times 10^{ - 6} ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(FDS_{r}\) | \(10^{4} \times round(uniform(4.1 \times 10^{2} ,5.0 \times 10^{2} ));\) |
\(\begin{gathered} FSP_{i}^{IoT} \hfill \\ FMN_{j}^{IoT} \hfill \\ FDST_{l}^{IoT} \hfill \\ FCL_{f}^{IoT} \hfill \\ FRC_{d}^{IoT} \hfill \\ \end{gathered}\) | \(10^{3} \times round(uniform(2 \times 10^{2} ,3.0 \times 10^{2} ));\) |
\(FVH_{h}\) | \(10^{5} \times round(uniform(7.5 \times 10^{2} ,8.0 \times 10^{2} ));\) |
\(DCS_{bmt}\) | \(\begin{gathered} loop(b, \hfill \\ \quad \quad \quad loop(t, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad loop(m, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad if(ord(m) < = 1, \hfill \\ DCS(b,m,t) = 0; \hfill \\ else \hfill \\ DCS(b,m,t) = 10^{2} \times round(uniform(9.0 \times 10^{2} ,1.0 \times 10^{3} )); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad ); \hfill \\ \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(\alpha_{bmt}\) | \(uniform(5.0 \times 10^{ - 2} ,9.0 \times 10^{ - 2} );\) |
\(\omega_{ab}\) | \(uniform(6.0 \times 10^{ - 1} ,7.5 \times 10^{ - 1} );\) |
\(\theta_{b}\) | \(uniform(2.0 \times 10^{ - 2} ,3.0 \times 10^{ - 2} );\) |
\(EN^{IoT}\) | \(uniform(0.02,0.03)\) |
\(P_{t}^{tag}\) | \(round(uniform(4.5,7.5));\) |
\(PEN\) | \(round(uniform(5.5,6.5));\) |
\(CPSP_{ait}\) | \(round(uniform(1.5,2.5) \times \frac{sum((b,m),DCS(b,m,t) \times \omega (a,b))}{{card(a) \times card(i)}})\) |
\(\begin{gathered} CPMN_{bjc}^{L} \hfill \\ CPRC_{bdc}^{L} \hfill \\ \end{gathered}\) | \(0\) |
\(CPMN_{bjc}^{U}\) | \(\begin{gathered} loop(j, \hfill \\ \quad \quad \quad loop(b, \hfill \\ \quad \quad \quad \quad \quad \quad \quad loop(c, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad if(ord(c) < = 1, \hfill \\ CPMNU(b,j,c) = round(uniform(1.5,2.0) \times \frac{sum((m,t),DCS(b,m,t))}{{card(j) \times card(t)}}); \hfill \\ else \hfill \\ CPMNU(b,j,c) = round(1.1 \times CPMNU(b,j,c - 1)); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(CPRC_{bdc}^{U}\) | \(\begin{gathered} loop(d, \hfill \\ \quad \quad \quad loop(b, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad loop(c, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad if(ord(c) < = 1, \hfill \\ CPRCU(b,d,c) {=} round(uniform(7.5 {\times} 10^{ - 1} ,8.5 {\times} 10^{ - 1} ) \times \frac{sum((f,t),CPCL(b,f,t))}{{card(d) \times card(t)}}); \hfill \\ else \hfill \\ CPRCU(b,d,c) = round(1.1 \times CPRCU(b,d,c - 1)); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(CPDST_{blt}\) | \(round(uniform(1.5,2.0) \times \frac{sum(m,DCS(b,m,t))}{{card(l)}});\) |
\(CPCL_{bft}\) | \(round(uniform(1.5,2.0) \times \frac{sum(m,DCS(b,m,t) \times \alpha (b,m,t))}{{card(f)}});\) |
\(CPDS_{rt}\) | \(round(uniform(2.0 \times 10^{ - 1} ,3.0 \times 10^{ - 1} ) \times \frac{sum((b,f),CPCL(b,f,t) \times \theta (b))}{{card(r)}});\) |
\(CPVH_{h}\) | \(round(uniform(1.5,1.9) \times \frac{sum((b,m,t),DCS(b,m,t) \times \theta (b))}{{card(h) \times card(t)}});\) |
\(PDS_{at}^{RW}\) | \(round(uniform(8.0 \times 10^{1} ,1.0 \times 10^{2} ));\) |
\(PDS_{bt}^{PRD}\) | \(round(uniform(1.1 \times 10^{2} ,1.3 \times 10^{2} ));\) |
\(CSP_{ait}\) | \(round(uniform(3.0 \times 10^{3} ,3.5 \times 10^{3} ));\) |
\(CMN_{bjt}\) \(CDST_{blt}\) \(CCL_{bft}\) \(CRC_{bdt}\) \(CDS_{brt}\) | \(round(uniform(2.5 \times 10^{2} ,3.5 \times 10^{2} ));\) |
\(DMD_{jl}\) \(DCC_{mf}\) \(DCR_{fd}\) \(DCD_{fr}\) \(DRM_{dj}\) | \(round(uniform(2.0 \times 10^{1} ,5.0 \times 10^{1} ));\) |
\(CHL_{bmt}\) | \(\begin{gathered} loop(b, \hfill \\ \quad \quad \quad \quad loop(t, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad loop(m, \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad if(ord(m) < = 1, \hfill \\ CHL(b,m,t) = 0; \hfill \\ else \hfill \\ CHL(b,m,t) = round(uniform(2.5 \times 10^{2} ,3.5 \times 10^{2} ))); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad \quad \quad \quad \quad ); \hfill \\ \quad \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(CSH_{bmt}\) | \(10^{2} \times CHL(b,m,t);\) |
\(DSM_{ij}\) | \(round(uniform(3.0 \times 10^{2} ,5.0 \times 10^{2} ));\) |
\(DDC_{lm}\) | \(\begin{gathered} loop(l, \hfill \\ \quad \quad \quad \quad loop(m\$ (ord(m) > 1), \hfill \\ DDC(l,m) = round(uniform(2.0 \times 10^{1} ,5.0 \times 10^{1} )); \hfill \\ \quad \quad \quad ); \hfill \\ ); \hfill \\ \end{gathered}\) |
\(TDC_{hlm}\) | \(round(uniform(5.0,7.0) \times DDC(l,m));\) |
\(TC_{hmn}\) | \(round(uniform(5.0,7.0) \times DC(m,n));\) |
\(\varpi_{bft}\) | \(uniform(0.75,0.85);\) |
\(\mu_{h}\) | \(uniform(0.15,0.25);\) |
\(PH\) | \(3\) |
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Tavana, M., Khalili Nasr, A., Santos-Arteaga, F.J. et al. An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05219-3
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DOI: https://doi.org/10.1007/s10479-023-05219-3