Kalyanmoy Deb
Computational Optimization and Innovation Laboratory Dept. of Electrical and Computer Eng. Michigan State University

Christie Myburgh
Principal R&D Engineer Maptek Pty Ltd

Abstract This paper outlines a computationally fast method to solve an integer linear programming problem: find optimal pouring sequences at a metal foundry to reduce metal heating costs and wastage as it produced castings of various volumes. Based on a population based evolutionary optimization framework, we develop a computationally fast method to arrive at a near-optimal solution repeatedly. Two popular softwares (glpk and CPLEX) are not able to handle around 300 and 2,000 integer variable version of the problem, respectively, even after running for several hours. Our proposed method is able to find a near-optimal solution in less than a second on the same computer. This method scales in a sub-quadratic computational complexity in handling 50,000 to one billion variables, and is believed to be the first time such a large-sized real-world constrained problem has ever been handled using any optimization algorithm.