It is nowadays quite challenging to manage and control the supply chain concerning transparency, traceability, and zero‐trust security. Digital technology such as blockchain has shown promising features to ease the global supply chain for tracking, tracing, and authenticity. This study critically examines the potential of blockchain technology and smart contracts to manage global supply chain sustainability. It also analyzes the inherent opportunities, benefits, and common barriers to deploying blockchain in the supply chain. Moreover, an overview of blockchain technology and its application in the various industries’ supply chain management is illustrated in this study. Furthermore, an application demo related to blockchain in the supply chain is provided within the scope of this study with the view to demonstrating how various transactions in the supply chain are executed with higher authenticity. The study is concluded with several future research propositions and directions that may provide insight into overcoming current challenges and the adoption of blockchain for the supply chain.

Climate change affects the world. Due to excessive GHG emissions, urban transportation contributes to this threat. Policymakers and authorities want to reduce transportation-related GHG emissions. An imaginary urban area with high transportation-related greenhouse gas emissions, dense, interconnected transportation modes, and a high population density is considered. Istanbul, Turkey meets the criteria of this imaginary place, so the case analysis considers this city. Istanbul’s decision-makers are looking for effective strategies to prioritize urban climate change policy alternatives for transportation activities. Four alternative strategies and 13 criteria are presented in this context. Innovative multi-criteria decision-making (MCDM) method with the interval-valued Fermatean fuzzy sets (IVFFSs) strategies is proposed for advantage-prioritization so decision-makers can select the most effective strategies for policies. Utilizing the IVFFSs, the proposed method effectively tackles the qualitative/quantitative data and uncertain information that occurs in realistic applications. In this study, firstly IVFF-heronian mean operators with their desirable characteristics are presented to aggregate the IVFF information. The proposed operators can overcome the drawbacks of existing IVFF information-based operators by considering the relationships between IVFF numbers. Based on IVFF heronian mean operators, a hybrid decision-making framework is proposed by integrating criteria importance through inter-criteria correlation (CRITIC), rank sum (RS), and the double normalization-based multi-aggregation (DNMA) methods with IVFF information. In this method, the CRITIC and RS methods are implemented to derive the objective and subjective weights of the considered evaluation criteria and DNMA is applied to prioritize urban climate change policy alternatives for transportation activities. Sensitivity and comparative analyses with existing studies confirm the proposed framework. The evaluation results show that the integration of transportation sectors, strategies, and innovations across different urban areas in all regions option has the highest overall utility degree (0.731) among a set of four urban climate change policy alternatives for transportation activities.

Today's business environment has created a high level of uncertainty and disturbed procedures in supply chains. Suppliers have been often identified as the main source of risks in creating the massive levels of disruptions in supply chains. That is why resilient supplier selection can greatly reduce purchase costs and time delays and can create stability in business practices, thereby increasing competitiveness and customer satisfaction. Pharmaceutical companies play an important key role in the health of society, and these companies are frequently exposed to this disorder. Hence, this paper tries to propose a new integrated approach based on traditional (delivery, quality, price, technology level) and resilient criteria for supplier selection in pharmaceutical companies using the Z-number data envelopment analysis (Z-DEA) model and artificial neural network (ANN). In the proposed approach, expert opinions have been provided based on Z-numbers due to the inherent ambiguity and uncertainty in the evaluation process. This is the first study that evaluates the pharmaceutical industry based on traditional and resilience factors by presenting a methodological structure under the uncertainty environment. Here, a fuzzy mathematical model is used. A real case study is utilized to indicate the applicability of the proposed approach to resilient supplier selection in the pharmaceutical industry. Finally, the suppliers are ranked and the best supplier is selected regarding the reliable level of α. To indicate the features and capabilities of the selected approach, the performance analysis is presented in three parts. First, the obtained results are compared with a fuzzy DEA (FDEA) method in the form of validation and verification. Second, a sensitivity analysis is executed to show the effects of different criteria on ranking results, and the price index is identified as the most important evaluation criteria. Third, a predictive model is presented based on ANN that is able to detect the efficiency or inefficiency of suppliers with an 83% accuracy.

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.

Advance-selling (AS) is a commonly observed industrial practice in which a retailer allows consumers to prebook the fashionable product before the real selling season starts. Motivated by this practice, this paper studies AS strategy for a retailer who sells a newsvendor-type of fashionable product in light of potential consumer opportunistic returns. In our model, the consumers face valuation uncertainty and know their valuation realization only after product acquisition. There also exists aggregate demand uncertainty, captured in the conventional newsvendor model. All preorders are fulfilled at the beginning of a normal-selling season. We build analytical optimization models and consider three strategic options for the retailer, namely, no advance-selling allowed (NAP), advance-selling with full refund (AFP) and advance-selling with partial refund (APP), where there are two suboptions under APP. We derive the retailer's optimal pricing and refund policies for each option. By comparing the results in the above options, important insights are generated. Finally, we conduct a numerical analysis to further examine the impacts brought by consumers valuation, market condition, and consumers classification on the optimal strategy.