Summer School on Large Scale Optimization May 17-24, 2020

Aims and Scope: Several real-world optimization problems (for example, facility location problems, network design problems, vehicle routing problems, etc.) that commonly arise in the context of Supply Chain and Logistics, are often modeled as Integer Programs/Mixed Integer Programs Program (IPs/MIPs). IPs/MIPs can be notoriously difficult, often to an extent that even the best IP/MIP solvers, which use Branch and Cut algorithms, fail to solve them in a reasonable time. The objective of the Summer School on Large Scale Optimization is to train participants on how to take advantage of the often hidden special structures of such problems, either by relaxation or by decomposition into relatively easier/smaller problems, which can be solved efficiently using their special structures. The challenge then is how to recover the solution to the original problem from the solution to its relaxation/decomposition. To this end, it will provide tutorials on several decomposition techniques, namely, Lagrangian Relaxation, Benders Decomposition, Column Generation, Dantzig-Wolfe Decomposition methods, Cutting Plane Methods, and Generalized Benders for IPs/MIPs and Mixed Integer Non-linear Programs (MINLPs). Each session on a given topic will be followed by a research talk on a problem that demonstrates a successful application of the method.

Based on the enthusiastic response after the first Summer School on Large Scale Optimization held at the Indian Institute of Management Indore last year, we are pleased to announce its next offering, to be held at the Indian Institute of Management Ahmedabad, May 17-24, 2020.

Target Audience:

• Masters/PhD students in Operations Research/Management Science/Industrial Engineering
• Faculty members working with Integer Programs
• Industry professionals in Optimization/Logistics/Supply Chain Domain

Topics:

• Modeling for Integer Programs
• Introduction to AMPL (A Mathematical Programming Language)
• Introduction to Modeling in C++/Python (for IBM Cplex/Gurobi)
• Branch & Bound Method
• Valid Inequalities & Cutting Plane Method
• Benders Decomposition
• Generalized Benders Decomposition
• Lagrangian Relaxation
• Column Generation
• Dantzig-Wolfe Decomposition
• Non-linear programming
• Mixed Integer Non-linear Programming
• Heuristics & Matheheuristics
• Multi-level Optimization
• Stochastic Programming