Effective decision-making while trading off the constraints and conflicting multiple objectives under rapid technological developments, massive generation of data, and extreme volatility is of paramount importance to organizations to win over the time-based competition today. While agility is a crucial issue, the firms have been increasingly relying on evidence-based decision-making through intelligent decision support systems driven by computational intelligence and automation to achieve a competitive advantage. The decisions are no longer confined to a specific functional area. Instead, business organizations today find actionable insight for formulating future courses of action by integrating multiple objectives and perspectives. Therefore, multi-objective decision-making plays a critical role in businesses and industries. In this regard, the importance of Operations Research (OR) models and their applications enables the firms to derive optimum solutions subject to various constraints and/or objectives while considering multiple functional areas of the organizations together. Hence, researchers and practitioners have extensively applied OR models to solve various organizational issues related to manufacturing, service, supply chain and logistics management, human resource management, finance, and market analysis, among others. Further, OR models driven by AI have been enabled to provide intelligent decision-support frameworks for achieving sustainable development goals.

The present issue provides a unique platform to showcase the contributions of the leading international experts on production systems and business from academia, industry, and government to discuss the issues in intelligent manufacturing, operations management, financial management, supply chain management, and Industry 4.0 in the Artificial Intelligence era. Some of the general (but not specific) scopes of this proceeding entail OR models such as Optimization and Control, Combinatorial Optimization, Queuing Theory, Resource Allocation Models, Linear and Nonlinear Programming Models, Multi-objective and multi-attribute Decision Models, Statistical Quality Control along with AI, Bayesian Data Analysis, Machine Learning and Econometrics and their applications vis-à-vis AI & Data-driven Production Management, Marketing and Retail Management, Financial Management, Human Resource Management, Operations Management, Smart Manufacturing & Industry 4.0, Supply Chain and Logistics Management, Digital Supply Network, Healthcare Administration, Inventory Management, consumer behavior, security analysis, and portfolio management and sustainability. The present issue shall be of interest to the faculty members, students, and scholars of various engineering and social science institutions and universities, along with the practitioners and policymakers of different industries and organizations.

Showcases the contributions of the leading international experts on production systems and business Presents effective decision-making while trading off the constraints and conflicting multiple objectives Discusses multi-objective decision-making which plays a critical role in businesses and industries

Autorentext
Prof. Gunasekaran is currently the Special Assistant to the Provost for Academic Affairs and Student Success Professor of Operations Management (Tenured) at California State University, Bakersfield (USA). He obtained Ph.D. in Industrial Engineering and Operations Research from IIT, Mumbai in 1988 and participated in several training programs/seminars/workshops organised by prominent institutions such as UMass Dartmouth, CSU Bakersfield, AACSB, NASPAA, NEASC, WASC, George Blumenthal Scholar, and CSUB Deans Academy to name a few. Apart from holding many key academic and administrative positions earlier, Prof. Gunasekaran published over 400 research papers and authored several editorial notes on emerging areas of Operations Management and MIS. In addition to the recipient of many honours and awards, he is the Editor-in-Chief of several journals, including OPSEARCH.

Prof. Jai Kishore Sharma is at present Head, School of Business, Amity University, Noida (UP). Earlier, he was Professor, Faculty of Management Studies, University of Delhi. He was Visiting Professor, Department of Logistics and Production, Group ESSEC (Graduate School of Management), France; Amity University, Dubai Campus; and at Amity Institute of Higher Education, Mauritius. Apart from having different academic and administrative assignments at senior positions, his fields of academic interest are OR/decision science, supply chain management, business research methods, etc. He has 137 research papers, 24 case studies and 20 text-books to his credit. He is the President of Operational Research Society of India (ORSI).

Samarjit Kar is currently a professor in the Department of Mathematics, National Institute of Technology Durgapur, India. He is also an active participant in Chinese academia having served as a visiting professor at Tsinghua University since 2009. His academic collaborations/co-authors include academics from China, Poland, Norway, Canada, Serbia, Lithuania and Turkey. His current research interests include operations research and optimization, soft computing, machine learning and uncertainty modelling. He has published over 160 referred articles in international journals and authored six edited book volumes in Springer. His articles have been cited more than 5200 times on Google Scholar and have appeared in prestigious journals. He is serving as an Associate Editor of OPSEARCH and Guest editors of IEEE Transaction on Fuzzy Systems, Sustainability, Symmetry and Mathematics journals. Presently he act as secretary to Operational Research Society of India (Kolkata Chapter).

Klappentext

Chapter 1: Optimization of an inventory model with demand dependent on selling price and stock, nonlinear holding cost along with trade credit policy.- Chapter 2: Software Defect Prediction Through a Hybrid Approach Comprising of a Statistical Tool and a Machine Learning Model.- Chapter 3: Conservation of a prey species through optimal taxation: a model with Beddington-DeAngelis Functional Response.- Chapter 4: Investigate the reason for students' absenteeism in Engineering College in Fuzzy MCDM environment.- Chapter 5: Optimal inventory management policies for substitutable products considering non-instantaneous decay and cost of substitution.

Inhalt
Chapter 1: Optimization of an inventory model with demand dependent on selling price and stock, nonlinear holding cost along with trade credit policy.- Chapter 2: Software Defect Prediction Through a Hybrid Approach Comprising of a Statistical Tool and a Machine Learning Model.- Chapter 3: Conservation of a prey species through optimal taxation: a model with Beddington-DeAngelis Functional Response.- Chapter 4: Investigate the reason for students' absenteeism in Engineering College in Fuzzy MCDM environment.- Chapter 5: Optimal inventory management policies for substitutable products considering non-instantaneous decay and cost of substitution.