A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty

A systematic review of modeling approaches in green supply chain optimization

Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and industry professionals. This surge in interest has led researchers to employ diverse modeling approaches in the pursuit of enhancing green supply chain networks. In this systematic review, we analyze 159 recent GSCM optimization papers published from 2017 to 2022 and identify the recent trends in mathematical modeling, multi-objective optimization, and the modeling/solver tools utilized. We find that the primary green focus is on minimizing carbon emissions (n = 44), reflecting the increasing concern for environmental sustainability. Among the modeling approaches employed, mixed-integer linear programming has emerged as the most popular choice (n = 51), followed by game theory-based modeling (n = 30). When it comes to multiobjective optimization, the ε-constraint approach is the most widely used. Evolutionary algorithms have emerged as the dominant meta-heuristic optimization approach. Additionally, the widely utilized solver in this domain is CPLEX with the most popular modeling/solver combination being GAMS/CPLEX. Moreover, the Journal of Cleaner Production was the leading outlet for research in this domain (n = 35). In addition to these findings, this study also discusses some other research trends and future research directions. Finally, we discuss the theoretical, managerial, and policy implications of this study. By providing GSCM researchers and practitioners with the latest trends in GSCM optimization approaches, this study contributes to the further advancement of the field.

A sustainable two-echelon green supply chain coordination model under fuzziness incorporating carbon pricing policies

Noxious effect on environmental due to carbon emissions is being addressed worldwide by governments through carbon pricing instruments. Two of prevalent instruments adopted by governments are simple carbon tax and cap-and-trade policy. Effectiveness of carbon pricing instruments towards achieving reduction in carbon emissions is a matter of study on one hand. Whereas, the planning of supply chain operations under imposition of such financial instruments is a challenge for small and medium scale business enterprises. Addressing this situation, the present study develops a supply chain model for coordinated planning of production and inventory replenishment schedules along with decision on economic amounts of expenditure for green resources. This decision model is formulated separately under the enforcement of each of two carbon pricing policies. The study focuses a manufacturer-retailer duo which works by adopting certain sustainability and conservation practices. Manufacturer reworks on rectifiable proportion of defective units, while retailer launches discounts-based sales of partially damaged units. The decision-making model developed in this study incorporates such activities. Furthermore, practical aspects like a slowdown in production due to unforeseeable disruptions and the effect of the quality of product and advertisement campaigns on demand rates are included in the proposed model. Under the purview of each carbon pricing policy, decision-making model is formulated as a nonlinear constrained optimization problem with objective towards profit maximization. A novel conception of fuzziness has been suggested for tackling imprecision in the assessment of certain parameters involved in the model. A numerical study on an appropriate case of a manufacturer-retailer duo system is presented. Empirical results of numerical study evince that a substantial reduction in carbon emission is achieved, even with an escalation in the profit through appropriate green expenditure. This trend is observed under the imposition of each of the carbon pricing policies, thereby substantiating an encouragement to supply chain partners for making expenditures on green resources. Thereby, the hypothesis of getting desired response on curbing emissions by incentivization through carbon pricing is satisfied in the studied case. Furthermore, a sensitivity analysis concludes the stability of formulated model against most of the parameters involved.

Intelligent monitoring method of tridimensional storage system based on deep learning

Growing international trade requires more flexible warehouse management to match it. In order to achieve more effective warehouse management efficiency, a shelf status-detection method based on deep learning is proposed. Firstly, the image acquisition of a multi-level shelf containing multiple bays is performed under different time and lighting conditions. Due to the difference in image characteristics between the bottom shelf on the ground and the upper shelf on the non-ground level, the collected images were divided into two groups: floor images and shelf images; and the warehouse status recognition was performed on the two groups separately. The two sets of images are cropped and center projection transformed separately to obtain the region of interest. On this basis, the improved residual network model is used to construct different depot detection models for the two sets of images, respectively, and the above algorithm is verified by actual measurements. In this paper, 102,614 images of 3246 depots with different states of non-ground layer, and 27,903 images of ground layer are collected. They are divided into training set and test set according to the ratio of 4:1, and the accuracy of training set is 99.6%, and the accuracy of test set is 99.3%. The experimental outcomes provide a theoretical method and technical support for the intelligent warehouse system management.

The impact of network externalities and altruistic preferences on carbon emission reduction of low carbon supply chain

This paper explores the manufacturer's carbon emission reduction in the presence of network externalities and altruistic preferences. Existing literature mainly analyzes the impact of government regulations and firms' behavior characteristics on manufacturers' decisions, while little literature investigates the role of network externalities and altruistic preferences in promoting carbon emission reduction of manufacturers. Our results show that network externalities and altruistic preferences are conducive to improving manufacturers' profits and carbon emission reduction levels. However, we find that manufacturers' carbon emission reductions are more likely to be optimal without network externalities and altruistic preferences when the cost of low-carbon technologies is low, and consumer preferences are high. Interestingly, when the low-carbon technologies cost is high, the combined effect of network externalities and altruistic preferences is more favorable for manufacturers to implement carbon emission reductions. However, when the cost of low-carbon technologies is moderate, we find that the combined effect of network externalities and retailers' altruistic preferences does not always increase the level of carbon emission reductions. In addition, we also find that network externalities or altruistic preferences enhance manufacturers' ability to afford the costs of low-carbon technologies, which implies that manufacturers are more inclined to reduce carbon emissions compared to scenarios without network externalities and altruistic preferences.

Risk management in sustainable supply chain: a knowledge map towards intellectual structure, logic diagram, and conceptual model

The global spread of COVID-19, international trade protectionism, geopolitical conflicts, and climate change presents challenges and risks to sustainable supply chains (SSCs). In recent years, scholarly interest in sustainable supply chain risk management (SSCRM) has continued to rise. A helpful literature review is necessary to enable supply chain practitioners to apply empirical findings from academic research or conceptual frameworks to their operations to maintain the stability and competitiveness of sustainable supply chains. The knowledge map of SSCRM is explored in this study using both quantitative and qualitative analysis. A total of 793 articles were retrieved to reveal the knowledge map of SSCRM. Scientometric and context analysis are combined in quantitative analysis to identify the intellectual structure of risk management research related to SSC. Then, a critical review is conducted in qualitative analysis to summarize and analyze the motivations, strategies, approaches, and tools of SSCRM. Combining the quantitative and qualitative analysis results, a conceptual model is constructed for SSCRM from three aspects: (1) risk identification, (2) risk assessment, and (3) risk mitigating and responding. Finally, future research directions are suggested based on the conceptual model for guiding the theories and practice of SSCRM. This study can work as a roadmap for providing appropriate risk management policies and toolkits to SSC, which could advance theoretical thinking on how to mitigate SSC risks.

Sustainable multi-trip periodic redesign-routing model for municipal solid waste collection network: the case study of Tehran

Daily transportation of wastes due to its environmental, financial, and social aspects has been considered a challenging issue in developing countries' municipal solid waste management systems. The location of transfer stations as intermediate nodes in municipal solid waste management network affects optimal collection frequency. A sustainable multi-period and multi-trip vehicle routing problem integrated with relocation models was developed to redesign the intermediate transfer stations and find optimal vehicle routes and the best collection frequency for each municipal solid waste generation point. Regarding the social aspects of a sustainable solid waste management system, an extended social life cycle assessment methodology for redesign and routing operations was developed based on the UNEP guidelines. The social life cycle assessment methodology evaluated the probable social effects of the system throughout the entire life cycle using an iterative policy. In this study, selected impact subcategories and inventory indicators for the routing and redesign system were utilized to quantify the system social score. Besides, the developed model was solved for different problem instances. The results indicated that system social score was affected by collection frequencies decisions, redesign policy, and the number of demand nodes. Furthermore, the model was applied to a real-world case study resulting in a total cost reduction of 66% that occurred by a 86% reduction in weekly traveled distance and a 12% decrease in routing social score.

Sustainable Inventory Management in Supply Chains: Trends and Further Research

This article presents an overview of the models applied to sustainable inventory management in supply chains and a roadmap for new research. It aims to address the lack of understanding of how sustainability is being incorporated into quantitative inventory management models in the supply chain context. The study is based on a classification of the reviewed literature according to the following criteria: supply chain structure, environmental approach, problem type, modeling, and solution approach. As a result, 36 articles were analyzed and classified. The main findings show that studies that incorporate social sustainability into inventory management along supply chains are lacking, while environmental studies are a growing research area. Uncertainty issues also need to be incorporated into sustainable inventory management models. Another important result of this study is the definition of a roadmap with trends and future research guidelines. The identified future research guidelines include incorporating decisions that can help to improve economic, environmental, and social sustainability. Thus, future studies should focus on both following quantitative models that incorporate inventory decisions integrally with transportation and location decisions, and more complex models, and employing new algorithms and heuristics to solve them.