Designing a disruption-aware supply chain network considering precautionary and contingency strategies: a real-life case study

Due to the high risk in the business environment, supply chains must adopt a tailored mechanism to deal with disruptions. This research proposes a multi-objective formulation to design a robust and resilient forward supply chain under multiple disruptions and uncertainty. The mentioned objective functions include minimizing the total cost, environmental impacts, and the network non-resiliency associated with the supply chain simultaneously countered using an augmented ε-constraint method. A Mulvey robust optimization approach is also utilized to deal with uncertainty. Ultimately, the developed model is validated based on three datasets associated with a case study of the steel industry. The results indicate that preventive and mitigation resilience strategies have significantly promoted the supply chain’s capabilities to deal with disruptions. Controlling network resiliency via non-resiliency measures has also created a risk-aware and robust structure in the incidence of disturbances. Numerical results reveal that multiple sourcing, lateral transshipment, and fortification of facilities will lead to the greatest cost-efficiency in the case study. Observations also indicate that the fortified supply chain will be highly economically viable in the long run due to the reduction of costs resulting from lost sales, unnecessary inventory holding, and the company’s credit risk

An optimization approach for disaster relief network design under uncertainty and disruption with sustainability considerations

Human-made, natural, and unexpected disasters always cause human and financial losses to communities. Disaster management is a framework with proven performance to reduce the damage caused by disaster and supply chain disruptions. Transferring the injured people from affected areas to hospitals at the minimum possible time is a crucial goal in times of disaster. This paper develops a two-stage stochastic programming model to transport the injured people from affected areas to hospitals in the incidence of multiple disruptions at transportation links and facilities under uncertainties. Herein, economic, social, and environmental aspects of sustainability are considered, while simultaneous disruptions are managed to minimize the adverse impacts of the disasters. We aim to determine optimal locations to establish transfer points and flows between the relief network nodes with sustainability considerations. Ultimately, a case study in District 12 of Tehran, Iran is conducted to ensure the proposed model’s validity and performance. Various sensitivity analyses are also implemented to ensure the model’s effectiveness. The results indicate that disruptions in facilities and transportation links lead to increased relief time, hence has the most significant negative impact on relief operations

A robust-stochastic data envelopment analysis model for supplier performance evaluation of the telecommunication industry under uncertainty

The primary activities of any organization rely on the procurement of the required goods and services at the shortest time and highest quality possible. On this basis, the problem of supplier evaluation, ranking, and selection is considered critically important. Data envelopment analysis is a well-known and successful approach in this field. In this study, we propose a robust-stochastic data envelopment analysis model to measure the efficiency of decision-making units under uncertainty. We measure efficiency through a standard and an inverted model in terms of resilience and agility. In order to demonstrate the practical potential of the proposed model, we apply the model to a case study of the Iranian telecom industry with 90 decision-making units. Numerical results reveal that human resources and cash assets are the most important input criteria. Also, the output indicators, including adaptability, reliability, visibility, and coordination, have high importance in measuring the efficiency of decision-making units. It should be noted that employing the robust-stochastic optimization approach leads to controlling the fluctuations of uncertain parameters and maintaining a desirable optimal level of efficiency for decision-making units under different scenarios. The results suggest that the model is sufficiently valid and reliable for evaluating the performance of suppliers in the telecom industry, may be employed under uncertain conditions, and can incorporate decision-makers’ varying preferences. The managerial insights derived from this research indicate that, in the short term, uncertainty throughout the evaluation process of suppliers often leads to reduced efficiency among the decision-making units. However, operating under uncertainty is associated with several advantages in the long term, such as increased decision-making consistency and improved vital ability to cope with uncertainty