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With the advancement of global sustainable development goals, promoting sustainable supply chain management has become the key to enhance the competitiveness of enterprises. However, it is difficult for the existing management methods to deal with the balance between cost, efficiency and environmental impact, and the complexity of decision-making under uncertain conditions has increased significantly. Therefore, a mixed integer programming model based on the branch delimitation method and relaxation variables is proposed to transform the enterprise management problem of the sustainable supply chain into a mathematical programming model. With the goal of minimizing the total cost, the effective search is carried out through boundary conditions and priority queues, and the relaxation variable technique is used to reduce the complexity of the problem and ensure that the model can still obtain a feasible solution when considering the uncertainty factors. The results showed that the hybrid integer programming model had the smallest annual discount cost, the largest number of item searches, and an average processing time of less than 2 s, which effectively solved the problem between supplier selection and order allocation in the supply network, and showed good algorithm efficiency and application efficiency. The model can provide reference guidance for enterprise management decision-making and provide a reference value for the improvement of enterprise environmental, social and economic performance.

This paper aims to develop a DEA-based framework to evaluate the efficiency of the supply chain based on the seller–buyer structure and with respect to win–win strategy. This is a bi-stage model employing the CCR model in the forms of input-oriented and output-oriented considering the intermediate measures for two different conditions under a centralized point of view. The obtained results from the extension of this model to supply chain network lead to introduce “efficient path” concept being a path including different components of the supply chain that are efficient in terms of DEA. Other kinds of proper information provided by the proposed model can help the managers and decision-makers of the supply chain field in supplier selection procedure and making efficient portfolios and collaborations across the supply chain network.

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.

Due to the expansion of privacy protection, and data sharing gets complicated and is known to be a hot issue in the research areas. Many business scenarios in the supply chain network have been highly important in research areas. Especially, blockchain technology ensures the essential solution in the supply chain network for securing information sharing. Yet, it is difficult to maintain security at every blockchain level, whereas it is suggested to use “public–private key cryptography.” The fundamental goal of this research work is to present new privacy preservation of data using hybrid meta-heuristic algorithms with blockchain technology. The secured supply chain network is built with blockchain technology. The privacy preservation of data sharing in blockchain technology is handled by the electric fish-Harris hawks optimization (EF-HHO). This new algorithm is utilized for the key generation phase of two processes like “data sanitization and restoration.” The optimal key generation follows a multi-objective problem is solved by the variables like “Hiding Failure (HF) rate, information preservation (IP) rate, and false rule (FR) generation, and degree of modification (DM)”. The result analysis shows the offered model provides effective performance when compared with existing conventional techniques.

This paper addresses a multi-echelon capacitated location–allocation–inventory problem under uncertainty by providing a robust mixed integer linear programming (MILP) model considering production plants at level one, central warehouses at level two, and the retailers at level three in order to design an optimal supply chain network. In this model, the retailer’s demand parameter is uncertain and just its upper and lower bounds within an interval are known. In order to deal with this uncertainty, a robust optimization approach is used. Then, a self-learning particle swarm optimization (SLPSO) algorithm is developed to solve the problem. The results show that the proposed algorithm outperforms the exact method by providing high quality solutions in the reasonable amount of computational runtime.