With the development of communication infrastructure, the design of supply chains has changed significantly. Blockchain technology, as one of the most cutting-edge technologies, can promote transparency among members of the supply chain network. To the best of our knowledge, this is the first study that tries to develop a novel bi-objective optimization model to integrate the transparency resulting from the use of blockchain for designing a three-level supply chain network. The first objective function is to minimize total cost while the second objective function seeks to maximize transparency based on the application of blockchain technology. Moreover, it is worth noting that it is the first attempt to investigate the role of a blockchain model under stochastic conditions. The bi-objectiveness and stochastic nature of the proposed model are then treated using Fuzzy Goal Programming (FGP) and Chance-Constrained programming (CCP) approaches, respectively. To tackle the problem, an improved Branch and Efficiency (B&E) algorithm is developed by incorporating transparency along with cost and service. The impacts of blockchain exclusively through transparency (Case 1) or through transparency, cost, and benefits (Case 2) in Supply Chain Design (SCD) are compared. The results demonstrated that the first case has less computational complexity and better scalability, while the second case has more transparency, less congestion, and more security. As one of the main implications, supply chain managers who are focused on cost minimization as well as transparency maximization are advised to take into account the trade-off between featuring costs and benefits of blockchain technology.

As the growth of e-commerce continues to accelerate, there is a need for new and innovative strategies in last-mile delivery to meet the changing demands of customers. The main objective of this study is to address this need by optimizing the last-mile delivery problem with service options (LMDPSOs) through a novel two-phase approach that considers various delivery options such as home delivery, self-pickup, and delivery at different prices. This original approach enables simultaneous optimization of the selection of the most appropriate pickup and delivery points and determination of the most efficient vehicle routing. The LMDPSOs reduces overall costs, minimizes environmental impact, and considers customer satisfaction levels by determining the most appropriate trips according to the available service options. This research employs a two-phase methodology for decision making. The first phase determines the value of the proposed locations through a novel multi-criteria decision-making (MCDM) approach that incorporates sustainability criteria. In the second phase, a tailored mathematical model is proposed for vehicle routing with service options. The model is coded in the CPLEXsoftware version 12.6 in various dimensions. We evaluate the potential and advantages of diverse delivery choices, illustrating that aggregating orders at pickup and delivery points can reduce delivery costs and minimize environmental impact. Additionally, this paper directs managers in selecting the most appropriate delivery method for last-mile delivery, considering environmental, social, and economic factors.

Satisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient.