Nowadays, concerns about environmental issues are increasing. Therefore, companies and producers are under pressure from government rules and regulations on one hand, and on the other hand, maintaining customer satisfaction concerning cares about the environment. Green supply chain management (GSCM) is a procedure to increase efficiency and decrease environmental effects for companies that collaborate with customers and suppliers. According to GSCM, there is some research about applying green aspects of purchasing, design, manufacture, distribution, packaging, marketing, and reverse logistics of supply chains to improve their company’s performance regarding environmental issues. Moreover, recently, DEA as a nonparametric model is used to evaluate the efficiency and performance of supply chains as decision-making units (DMUs). However, previous studies on efficiency improvement in GSCM did not investigate the effect of some economic and environmental factors together such as service level, emissions (CO2), and size of the supply chains (arcs) on the efficiency of the whole supply system. These factors are essential as they can affect the manager’s ability to distinguish the true performance of a green supply chain. Thus, evaluating the efficiency of GSCM by DEA models and imposing the green principles to find out the efficient ones for increasing management performance is vital. Fulfilling the mentioned research gap, this paper developed a benchmark approach to verifying efficient DMUs and potential efficient DMUs which may improve costs and efforts to become efficient. In the case study, the benchmarks and potentially efficient DMUs are found by DEA standard models and slight adjustment is conducted for potentially efficient DMUs to change their status to efficient DMUs. Moreover, the effect of some green principles on the efficiency value of DMUs is verified using Tobit regression before and after the mentioned modification. A set of realistic results provided for the priority of potential DMUs modification confirmed the applicability of the proposed procedure.
Supply chain management is a set of techniques used for effective and efficient integration of suppliers, manufacturers, warehouses and dealers in such a way that system costs to be minimized and goods service needs to be realized with the correct number in the right place and at the right time. Since the important role of three factors of localization, routing and assignment is not covered in the survival of a supply chain life, therefore, integration of these factors will result in an effective supply chain. This research aims to study the issue of supply chain network design including the localization of facilities, allocation flow among facilities and routing decisions. The issue is to determine the number, location and capacity levels of distribution centers, to allocate customers to distribution centers and distribution centers to suppliers and routing decisions such as determination of the products transport route from distributors to customers and type of transport vehicles so that the total cost of the system to be minimized and customer coverage to be maximized. In addition to reducing costs and increasing quality, improving the environmental performance of the supply chain and decreasing the costs of environmental degradation is also included in the proposed issue.This necessity which is known as a green supply chain is observed by choosing vehicles with lower emissions and reducing transport distances. A. Kazemi et al. J Fundam Appl Sci. 2016, 8(3S), 1340-1365 1341 On the other hand, this research role includes the impact of sharing information through raising and reducing waiting times for carriers. All of the above will be formulated by an integer linear programming model. Given that the mentioned issue is located in the group of problems with hard complexity, this article suggests using multi-objective meta-heuristic algorithms for optimization of the problem and compares the efficiency of the proposed algorithms with each other using several random sample problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.