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-Ã -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. Â 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.
General
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