Tanveen Kaur Bhatia

Bio: Tanveen Kaur Bhatia is an academic researcher from Thapar University. The author has contributed to research in topics: Shortest path problem & Fuzzy logic. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Mehar Approach for Finding Shortest Path in Supply Chain Network

Abstract: The aim of each company/industry is to provide a final product to customers at the minimum possible cost, as well as to protect the environment from degradation. Ensuring the shortest travel distance between involved locations plays an important role in achieving the company’s/industry’s objective as (i) the cost of a final product can be minimized by minimizing the total distance travelled (ii) finding the shortest distance between involved locations will require less fuel than the longest distance between involved locations. This will eventually result in lesser degradation of the environment. Hence, in the last few years, various algorithms have been proposed to solve different types of shortest path problems. A recently proposed algorithm for solving interval-valued Pythagorean fuzzy shortest path problems requires excessive computational efforts. Hence, to reduce the computational efforts, in this paper, firstly, an alternative lexicographic method is proposed for comparing interval-valued Pythagorean fuzzy numbers. Then, using the proposed lexicographic comparing method, a new approach (named as Mehar approach) is proposed to solve interval-valued Pythagorean fuzzy shortest path problems. Furthermore, the superiority of the proposed lexicographic comparing method, as well as the proposed Mehar approach, is discussed.

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

Abstract: The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

A Pythagorean Fuzzy Approach for Sustainable Supplier Selection Using TODIM

Abstract: Several firms have become increasingly concerned with sustainability in recent decades and are thus implementing environmental and social changes in their businesses and supply networks. This article aims to assess suppliers based on green design, corporate social responsibility, energy consumption, and other sustainability factors that might aid the growth of a company. Characteristics used in this study will help to accomplish economic, environmental, and social responsibility for organizations to reduce global warming and natural resource depletion. We have used the data given in the article of Zolfani et al. by implementing Pythagorean fuzzy TODIM (an acronym in Portuguese for iterative multicriteria decision making) to calculate the rank of suppliers based on the Triple Bottom Line (TBL) sustainability framework. Both TODIM and PF-TODIM are simple to compute, stable, consistent, and accurate, but we have proved by calculations why Pythagorean fuzzy TODIM should be chosen over TODIM in such situations, where decision makers do not have access to a reliable data source. Finally, we performed a sensitivity analysis on both TODIM and PF-TODIM, and the results bolstered the utility of the model.

A Novel Shortest Path Problem using Dijkstra Algorithm in Interval-Valued Neutrosophic Environment

Abstract: In this fast-changing world, amid a global pandemic there is a need of minimizing costs in terms of time, money, and kind. We represent all our major real-life decisionmaking problems like – telecommunication, social networking, time-scheduling problems, google map, DNA mapping, shortest air/ship routes, etc. using a graph in the forms of nodes and edges. Now, from the single source node if we would like to reach the multiple nodes, we require the shortest path then we use Dijkstra's algorithm, also known as the minimization algorithm, which uses the greedy approach and gives an optimal solution. In this paper, a novel method is proposed to find the shortest path problem (SPPr) in the interval-valued neutrosophic environment, where the uncertain/indeterminate data is expressed as three independent variables i.e., truth membership, indeterminate membership, and false membership. To validate the proposed method, a hypothetical problem of road networking is solved efficiently to determine the shortest route from the multiple available routes, where the cost associated with nodes is taken as an interval-valued neutrosophic fuzzy number.

A Novel Shortest Path Problem using Dijkstra Algorithm in Interval-Valued Neutrosophic Environment

Abstract: In this fast-changing world, amid a global pandemic there is a need of minimizing costs in terms of time, money, and kind. We represent all our major real-life decisionmaking problems like – telecommunication, social networking, time-scheduling problems, google map, DNA mapping, shortest air/ship routes, etc. using a graph in the forms of nodes and edges. Now, from the single source node if we would like to reach the multiple nodes, we require the shortest path then we use Dijkstra's algorithm, also known as the minimization algorithm, which uses the greedy approach and gives an optimal solution. In this paper, a novel method is proposed to find the shortest path problem (SPPr) in the interval-valued neutrosophic environment, where the uncertain/indeterminate data is expressed as three independent variables i.e., truth membership, indeterminate membership, and false membership. To validate the proposed method, a hypothetical problem of road networking is solved efficiently to determine the shortest route from the multiple available routes, where the cost associated with nodes is taken as an interval-valued neutrosophic fuzzy number.