Sunil Kumar Jauhar

An Approach to Solve Multi-criteria Supplier Selection While Considering Environmental Aspects Using Differential Evolution

Selection of an appropriate supplier is gaining a lot interest among researchers working in the field of supply chain management .Often many suppliers are available in the market that fulfills some preliminary criteria. However the real task is to determine the most suitable set of suppliers or key suppliers subject to management as well as environmental aspects. In the current study, we present an approach to solve the multiple-criteria green supplier selection problem mathematical model formulated with Data Envelopment Analysis with the application of differential evolution. A hypothetical case demonstrates the application of the present approach.

A Novel Approach for Sustainable Supplier Selection Using Differential Evolution: A Case on Pulp and Paper Industry

Diverse sustainable supplier selection (SSS) methodologies have been suggested by the practitioners in earlier, to find a solution to the SSS problem. A SSS problem fundamentally is a multi-criteria practice. It is a judgment of tactical significance to enterprises. The nature of this decision usually is difficult and unstructured. Optimization practices might be useful tools for these types of decision-making difficulties. During last few years, Differential Evolution has arisen as a dominating tool used for solving a variety of problems arising in numerous fields. In the current study, we present an approach to find a solution to the SSS problem using Differential Evolution in pulp and paper industry. Hence this paper presents a novel approach is to practice Differential Evolution to select the efficient sustainable suppliers providing the maximum fulfillment for the sustainable criteria determined. Finally, an illustrative example on pulp and paper industry validates the application of the present approach.

Genetic Algorithms, a Nature-Inspired Tool: Review of Applications in Supply Chain Management

The use of genetic algorithm for supply chain management with its ability to evolve solutions, handle uncertainty, and perform optimization remains to be a leading field of study. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, this paper presents a review of existing research activities inspired by the genetic algorithm application in supply chain management (SCM) aimed at presenting key research themes, trends, and directions of future research.

Differential Evolution for Supplier Selection Problem: A DEA Based Approach

Deciding an appropriate approach for supplier selection is however a demanding research task as there are often thousands of potential suppliers and identifying a subset of these suppliers can be a difficult practice. During last few years, Differential Evolution has come out as a dominant tool used for solving a wide range of problems arising in numerous fields. In the current study, we present an approach to solve the supplier selection problem mathematical modeled with Data envelopment analysis using differential evolution. A case study demonstrates the application of the present approach.

Integrating DEA with DE and MODE for sustainable supplier selection

Abstract In this study an effort is made for developing an efficient system for SSS by integrating together the traditional multi criteria performance evaluation tool DEA with DE algorithm and further with MODE to overcome the inherent drawbacks of DEA. The proposed model is implemented on a hypothetical but realistic automobile industry. The results indicate that the proposed integration of DEA with DE and MODE increases the discriminatory powers of DEA thereby resulting in more realistic outcomes.

Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI

Data envelopment analysis or DEA methodology is employed for assessing the relative efficiency of different homogeneous units. Through DEA one can analyze the areas which need more attention and can suggest measures for improving the performance of different sectors. Through this article, the authors have tried to analyze the relative efficiency of IITR (The Indian Institute of Technology Roorkee), a higher educational institute (HEI) in India. The efficiency of nineteen academic departments of IIT Roorkee is measured with respect to teaching and research. The novlty of the paper is twofold (1) the authiors have considered the environmental aspects (sustainability criteria) while measuring efficiency (2) Differential Evolution (DE) algorithm is employed in accordance with DEA on the fractional model generated for calculating efficiency.

Performance measurement of an Indian higher education institute: a sustainable educational supply chain management perspective

The present study focuses on embedding the Supply chain management concepts on to the education sector i.e. educational supply chain management. It is an important area of research as it helps in the management of one of the most important sector of a country, viz. the education sector. The objective here is to measure efficiency of 19 departments of Indian Institute of Technology Roorkee (IIT Roorkee), a reputed higher education institute of India.

Using Differential Evolution to Develop a Carbon-Integrated Model for Performance Evaluation and Selection of Sustainable Suppliers in Indian Automobile Supply Chain

Automobile industries worldwide are unified in opinion, that successful management of sustainable supply chains is the most important driver to improve both their economic and ecological performances. The significance of sustainable supply chain management (SSCM) is a critical corporate matter in the automobile industries that offers incredible potential for achieving better environmental performance, consumer fulfillment, pull down operating expenditures, reducing inventory investments in addition to achieving better fixed asset usage. The environment concerns, climatic changes, and additional ecological concerns in automobile industries are not only articulated by campaigners or researchers, but also by the common man as well, which has motivated the industries to focus on sustainability. The present research focuses on a DEA-based mathematical model and employs differential evolution to select the competent suppliers providing the utmost fulfillment for the sustainable criteria determined. This study aims to examine the sustainable supplier evaluation and selection practices likely to be adopted by the Indian automobile industry for their products.

Sustainable Supplier Selection: A New Differential Evolution Strategy with Automotive Industry Application

Modern times, highly competitive, global operating environment, sustainability plays a very vital role. Changing climatic conditions and environmental deterioration has multi dimensional impact on every sphere of life forms and life form driving processes. Public hold on corporations responsible for ecological misconduct in their supply chains getting more firm with time. To counter the threat it’s a high time industries started taking initiatives for sustainability in their supply chains. In spite of that, suppliers often are unsuccessful to appropriately contribute in these initiatives. Hence, present paper justifies the supplier involvement in sustainable initiatives in supply chain management (SCM) with using differential evolution (DE) to select the efficient sustainable suppliers providing the maximum fulfillment for the sustainable criteria determined. Finally, two illustrative cases on automotive industry validate the application of the present approach.

Estimating Technical Efficiency of Academic Departments: Case of Government PG College

This paper estimates the technical efficiencies of 20 departments of a government PG college Gopeshwar, Chamoli, Uttarakhand (India), for the academic year 2012–2013 through DEA technique. Number of academic staffs, number of taught course students, average students’ qualification, departmental operating cost (DOC) are taken as input variables, and number of graduates from taught courses, average graduates’ results (%), and graduate rate (%) are taken as output variables. Input–output data are collected from the NAAC report 2014 and annual SSR report academic year 2012–2013 and from the examination section of the government PG college, Gopeshwar. Some data are collected from the departments of the college and college annual book Madhuri. The study concludes that overall efficiency of departments is 87.8%, which indicates that the departments are not using their potential by 12.20%.