Carbon-efficient closed-loop supply chain network: an integrated modeling approach under uncertainty

Soleimani, Hamed; Mohammadi, Mahsa; Fadaki, Masih; Al-e-hashem, S.M.J. Mirzapour

doi:10.1007/s11356-021-15100-0

Cited by 18 publications

(13 citation statements)

References 55 publications

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“…Early research focuses on developing deterministic single objective optimization models for either minimizing the system operating cost or maximizing the total profit (Govindan et al 2015). However, recent studies emphasize the balance among different sustainable indicators with multi-objective optimization models (Govindan et al 2016;Yu and Solvang 2018), the proper formulation and treatment of uncertainties (Fattahi and Govindan 2017;Soleimani et al 2021;Yu and Solvang 2016), the improvement of the models' computational efficiency (Afra and Behnamian 2021;Alshamsi and Diabat 2017), and the management of different stakeholders (Gu et al 2019).…”

Section: Reverse Logisticsmentioning

confidence: 99%

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

Sun

Solvang

2022

Environ Sci Pollut Res

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The recent advancement of digitalization and information and communication technology (ICT) has not only shifted the manufacturing paradigm towards the Fourth Industrial Revolution, namely Industry 4.0, but also provided opportunities for a smart logistics transformation. Despite studies have focused on improving the smartness, connectivity, and autonomy of isolated logistics operations with a primary focus on the forward channels, there is still a lack of a systematic conceptualization to guide the coming paradigm shift of reverse logistics, for instance, how “individualization” and “service innovation” should be interpreted in a smart reverse logistics context? To fill this gap, Reverse logistics 4.0 is defined, from a holistic perspective, in this paper to offer a systematic analysis of the technological impact of Industry 4.0 on reverse logistics. Based on the reported research and case studies from the literature, the conceptual framework of smart reverse logistics transformation is proposed to link Industry 4.0 enablers, smart service and operation transformation, and targeted sustainability goals. A smart reverse logistics architecture is also given to allow a high level of system integration enabled by intelligent devices and smart portals, autonomous robots, and advanced analytical tools, where the value of technological innovations can be exploited to solve various reverse logistics problems. Thus, the contribution of this research lies, through conceptual development, in presenting a clear roadmap and research agenda for the reverse logistics transformation in Industry 4.0.

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Section: Reverse Logisticsmentioning

confidence: 99%

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

Sun

Solvang

2022

Environ Sci Pollut Res

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“…[10] Foreign studies such as Soleimani et al (2021) applied the Augmented Weighted Chebyshev (AWT) method and the e-constraint method to address the multi-objectivity of the problem, while the robust optimization method was used to deal with the uncertainty of demand, which provided effective solutions for designing a green closed-loop supply chain network. [11] Choudhary et al (2020) used a combined approach of Best-Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to identify "Total Quality Management" and "Cleaner Production Technology" as the most important causal dimensions of environmentally sustainable supply chain management. [12] Ramanathan et al (2020) used case study observations from three global companies to develop a conceptual model and conducted a questionnaire survey to test the conceptual model, and the results indicated that the environmental positioning (green objectives) of the companies would enhance their business and financial performances.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We first analyzed the status of four indicators of financial indicators, resource indicators, operational indicators and environmental indicators of six provinces and one city in East China separately. The performance status of each individual indicator of green development of logistics industry in each province and city in East China from 2015 to 2019 was calculated according to the formula (1) to (11), and its specific relative closeness (score) and ranking are shown in Table 2 and Figure 1, Figure 2, Figure 3 and Figure 4 below. From Figure 1, it can be seen that the economic development of logistics industry in East China held a stable growth trend in general from 2015 to 2019, among which Shandong Province leads other provinces and cities with an absolute advantage, and its rise is the largest, followed by Zhejiang Province, Jiangsu Province, Fujian Province, Anhui Province, Shanghai City, and Jiangxi Province.…”

Section: Evaluation Of Single Indicator Of Green Development Of Logis...mentioning

confidence: 99%

Research on the Evaluation of Green Development of Logistics Industry in East China under the “Carbon Peak and Carbon Neutrality” Goal

Fu¹

2022

BCPBM

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The strategy decision of “Carbon Peak and Carbon Neutrality” has brought extensive and complex impact to China’s economic and social development. In order to achieve the “Carbon Peak and Carbon Neutrality” goal, it is significant to promote the green development of the logistics industry, a high energy-consuming industry. Combined with the industry norms and relevant policy documents of logistics industry, this study selected secondary indicators containing four aspects of finance, resource, operation and environment, and eight tertiary indicators, and constructed the evaluation indicator system of green development of logistics industry in East China. Quantitative analysis and qualitative analysis of the green development of logistics industry in East China from 2015 to 2019 were conducted using entropy weighting TOPSIS method, and comprehensive performance and ranking of six provinces and one city were obtained. Results showed that Shandong Province ranked the highest in terms of the green development of logistics industry; Jiangsu Province and Zhejiang Province took the second and third places respectively, and Shanghai came in last. Finally, based on the in-depth analysis of empirical results, this paper provided suggestions for the green development of the logistics industry in East China.

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“…Lin noted that the WA model of Tiwari et al [45] yields objectives involving heavy weights with relatively greater achievement values. However, the proportion of the achievement levels is not essentially the same as that of the objective weights [20,44,46]. To solve the present SCPLCC problem, the following equations corresponding to Lin's weighted max-min model were employed [20]:…”

Section: Weighted Max-min Approachmentioning

confidence: 99%

Using a Product Life Cycle Cost Model to Solve Supplier Selection Problems in a Sustainable, Resilient Supply Chain

et al. 2022

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Supplier selection constitutes a crucial component of manufacturing procurement. We developed a product life cycle cost (PLCC) model to support Taiwanese light-emitting diode (LED) manufacturers in capacity planning for sustainable and resilient supply chain (SC) management. For firms, supply chain PLCC (SCPLCC) is a key consideration, but relevant evidence is scarce. We applied two types of goal programming, namely multiobjective linear programming and revised multichoice goal programming (RMCGP), to develop a PLCC-based model that minimizes net costs, rejections, and late deliveries. Moreover, we constructed a decision-making tool for application to a case of SC sustainable procurement management in a high-tech Taiwanese LED company. Managers can resolve relevant problems by employing the two approaches of the SCPLCC model with various parameters. The implementation of RMCGP with weighted linear goal programming sensitivity analysis produced sufficient findings, according to a study of five models for practical implications. The primary findings of the current model assist business decision-makers in minimizing PLCC, reducing PLCC cost, minimizing net cost, number of rejections, number of late deliveries, achieving PLCC goals, and selecting the best supplier in the context of sustainable SC development.

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Carbon-efficient closed-loop supply chain network: an integrated modeling approach under uncertainty

Cited by 18 publications

References 55 publications

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

Research on the Evaluation of Green Development of Logistics Industry in East China under the “Carbon Peak and Carbon Neutrality” Goal

Using a Product Life Cycle Cost Model to Solve Supplier Selection Problems in a Sustainable, Resilient Supply Chain

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