Operations Research 3.0

Constrained Optimization with Applications from Operations Research

Operations Research 3.0 is a Mathematica package for solving problems in linear optimization, quadratic programming, shortest path tasks, and combinatorial optimization and heuristics. With Operations Research 3.0 and Mathematica, you can solve a broad range of typical problems encountered in university courses and in daily practical work and can model and solve optimization problems in a very convenient and interactive manner.

Operations Research 3.0 contains five package files that are called from the master package OperationsResearch.m. These subpackages include:

• Linear optimization: revised simplex with sensitivity analysis, primal affine scaling, primal-dual interior-point method, and branch-and-bound methods
  
  
• Quadratic programming: affine scaling method with Karush-Kuhn-Tucker improvement
  
  
• Shortest path tasks: one-to-all and all-to-all shortest connections on a graph with substantial new features in Version 3.0, including a new and very efficient point-to-point routing algorithm and an improved Dijkstra's algorithm
  
  
• Combinatorial optimization and heuristics: several routines that can be used to solve assignment, traveling salesman, and other problems and implementation of additional effective heuristics, notably tabu search
  
  
• Reinforcement learning: a relatively new method for stochastic optimization and control that is quite powerful for some types of problems

An even larger selection of example notebooks containing typical problems that can be solved with Operations Research 3.0 is included in the documentation. Each problem is explained, modeled, and solved in full detail. The notebook collection will serve as a guide to dealing with a broad class of optimization tasks.

Developed and supported by SoftAS GmbH.