Advances in Computational Management

Advances in Computational Management

Prof. Srikanta Patnaik, Editor-in-Chief

Prospective authors and/or editors can directly contact the Series Editor. She will also provide information on editorial projects in progress in which the inclusion of additional contributors is possible.


Computational Management is a special branch of artificial intelligence whose application is in the area of Business Management. It consists of a set of computational tools and techniques, inspired from various objects and aspects of nature dealing with decision making and survival. These intelligent tools and techniques can be utilized to address various business and management problems that couldn’t be solved by traditional mathematical, statistical and modeling approaches. These techniques include fuzzy logic, neural networks, evolutionary computation, swarm intelligence, support vector machines, expert systems and many more. In recent past, Computational Management has successfully addressed the requisites for diverse range of business applications from time to time including production and operations, finance, logistics, supply chain management, pricing, marketing and many more.

This book series attempts to cover all the aspects of business management including theoretical, empirical and experimental computational models as well as simulation of algorithms for optimizing business applications. This series is expected to be a resource for academics, researchers and practitioners working in the area of computational management. The topics of interest although not limited to are as follows:

  • Computational Aspects of Business Management
  • Computational Economics
  • Multi Criteria Decision Making
  • Design, Analysis and Applications of Optimisation Algorithms
  • Dynamic, Stochastic and Combinatorial Optimization Models
  • Price risk mitigation strategies
  • Learning and Forecasting Demand Uncertainty
  • Generic problems in logistic distribution networks
  • Collaboration in decentralized distribution networks
  • Supply chain scheduling and planning
  • Supply Chain Resilience
  • Transfer price optimization
  • Mathematical Modeling and Simulation
  • Models and Tools of Knowledge Acquisition
  • Neural Networks and Genetic Algorithms
  • Operations Research
  • Scheduling Planning
  • Solution Algorithms
  • Supply Chain Analytics
  • Theoretical and Empirical Computational Models