Applications of Stochastic Programming

Review of the book
Many real-world decision problems involve uncertainty. Probabilistic planning is the study of decision-making processes despite uncertainty in time. The certainty number can be in the parameters of the problem or the structure of the model itself. The certainty number in the input parameters can exist due to errors in the calculation, unpredictability of the future and other cases. Uncertainty in the model can arise due to the unpredictability of events and earnings from doing a project. Uncertainty in mathematical models has been investigated since the birth of operations research by Dantzick. This book tries to show you the applications of probabilistic programming.

Table of Contents
The contents of this book are as follows:

Implementation of probabilistic programming with computer
SMPS format for probabilistic linear programming
IBM Probabilistic Planning Systems
Software for solving probabilistic programming problems with gradient probabilistic methods
Computational networking for probabilistic programming
Constructing and solving probabilistic linear programming models with SLP-IOR
Probabilistic programming modeling languages
Aggregated environments of probabilistic planning
Optimization and probabilistic modeling using stochastics
Aggregated modeling environment for probabilistic planning
An introduction to probabilistic programming applications
Fleet management
Modeling and production planning and scheduling despite uncertainty
Supply chain optimization model for Norwegian meat production company
Metal melting control with probabilistic programming approach
Presenting a probabilistic planning model for the use of network resources with the presence of uncertainty in demand
Probability approximation, momentum and Nash game
weather changes
Risk management in floods and earthquakes
Installments in Switzerland
Management of risk distribution in insurance companies
Cost strategies for oil companies