A novel method for solving linear programming with grey parameters

Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision-making in post-COVID era

Because of the COVID-19 outbreak, the supply of both essential and non-essential goods and services has been unprecedentedly disrupted. In the absence of any playbook, the need for innovative technologies to aid recovery from the Supply Chain (SC) disruptions quickly and effectively becomes persistent. The study aims to develop an innovative decision-making technology to handle the supplier selection problems arising from the frequent impreciseness and incompleteness in the nowadays' SC reports within the framework of the gresilient supply chain management. Through the integration of green and resilience aspects of the SCs, the supply chain 'gresilience' has been conceptualized. In light of this construct and based on the data collected from a manufacturing firm, the study deploys a two-fold decomposition of the core algorithm of the Ordinal Priority Approach (OPA), one for attributes and other for alternatives. It extends the OPA to the Fuzzy OPA (OPA-F) for solving the supplier selection problems. The study illustrates how green and resilience aspects of the SC can be integrated to better understand the gresilient suppliers in the wake of the SC disruptions. A novel construct of SC Gresilience is also furnished along with a novel definition of SC Gresilience. It also provides an innovative SC decision-making technology to help procurement managers to evaluate their suppliers. The resultant framework can help them better prepare for the COVID-19 like disruptions in the future.

Systems Evaluation through New Grey Relational Analysis Approach: An Application on Thermal Conductivity—Petrophysical Parameters’ Relationships

The current study introduces a new technique for the analysis of uncertain systems and uncertain processes in geothermics/earth sciences. The method is the second synthetic grey relational analysis (SSGRA) model, which incorporated the advantages of both Deng’s GRA model and the bidirectional absolute GRA model. The SSGRA model has been earlier successfully applied in project management and healthcare systems. The current study is a pioneer in demonstrating the feasibility of the SSGRA model in a geothermal environment. In the current study, the model was used to determine the associations between thermal conductivity and petrophysical parameters in an Algerian reservoir. The results revealed that thermal conductivity is most strongly associated with porosity followed by density and permeability. Their relationships are also discussed. The study concludes with valuable insights about the model and its application in engineering and natural sciences especially when the system contains uncertainty, which may arise either due to insufficient data or uncertain relationships among the parameters associated with the system or its processes.