In the case of disruptions in the blood supply chain, rapid reorganization of distribution processes is required to ensure an effective response to emergency situations. This paper considers the problem of redistributing available blood stocks from the institute and hospitals to hospitals affected by a disruption. A mathematical formulation of the problem is developed, with a multi-objective function aiming to: (i) minimize the blood delivery time to the locations of disruption, (ii) minimize violations of predefined safety stock levels at the institute and hospitals, and (iii) minimize the amount of blood taken from hospitals not affected by the disruption. The formulation also introduces constraints that ensure balanced violations of safety stock levels across unaffected facilities. Computational experiments are conducted on test scenarios generated from real case studies from the healthcare system of the Republic of Serbia. Small-sized instances can be solved exactly, providing benchmarks for evaluating a General Variable Neighborhood Search metaheuristic designed for larger problem instances. The results indicate that the proposed metaheuristic produces high-quality solutions within negligible CPU time.

This paper studies a three-level supply chain with one retailer, one distributer and one manufacturer under disruption. First, we show that profit sharing and return policy contracts established between members can coordinate this supply chain. Then, we investigate the effect of demand disruption in supply chain and we illustrate the possibility of breaking the initial desirable coordination. We consider two possible scenarios for the demand disruption separately; afterwards we define the revised coordinator parameters to respond to their effect in supply chain. Numerical examples for validation and evaluation of our model are also elaborated.

For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. Recently, high incidences of natural disasters, terrorism attacks, labor strikes, and other kinds of risks, also known as disruptions, indicate the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert is asked to determine the reliability of each procurement element (i.e., production, transportation, and communication) based on some proposed risk factors. Then, a distinct Multi-Layer Perceptron (MLP) network is trained to play the role of the expert opinion for estimating the reliability scores of each procurement. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality, and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker is recommended to choose between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework.

COVID-19 pandemic was one of the most severe sources of disruptions that recently threatened the supply chain networks. As such disruptions are inevitable in today's business world, learning lessons from the COVID-19 pandemic can significantly help supply chains prepare for future disruptions. This study aims to investigate how Australian supply chains were affected during the COVID-19 pandemic and provide some key resilience strategies to face disruptions. These strategies can assist SC managers in protecting their networks from possible disruption in future.

Supply chain disruption has become a serious world's problem during the past few years. Many businesses lose their customers due to late delivery or shortage of raw materials. Thus, it is necessary to look for expert systems to handle such issues. The paper presents a scientometrics survey on the role of artificial intelligence on supply chain management. The study uses the Scopus database to collect data from 1995 to 2022. The study collects nearly 750 articles which are sorted based on their citation records. Using some scientometric tools, the study has determined that the decision support system has been the most important tool to handle disruption in supply chain management. Moreover, the study shows that most studies were accomplished in North America, and some were partnerships with China. The study also detected nine groups of researchers who contributed the most in the supply chain. Moreover, the study discusses the concept of uncertainties associated with mathematical modeling associated with supply chain management and categorizes different works according to the methods used to handle the uncertainties. Finally, the study explains some of the recent developments of the implementation of artificial intelligence in various areas of supply chain management such as waste management, blood supply chain, etc. The results indicate that artificial neural networks are the most popular technique used among researchers to provide more efficient solutions for supply chain management.

During the past two years, Canadian economy was on hold since there were many businesses closed due to COVID-19 pandemic. Many business owners decided to ask their employees to work from their homes and working in distance created some troubles for many clients. Businesses asked Canada post to deliver their orders. As a result, Canada post was met with huge orders where most of them were delivered late. It is estimated that supply chain disruption has hurt Canadian economy, significantly. This paper surveys the effects of supply chain disruption on Canadian economy.