Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems

• Published in: INFORMS journal on computing Vol. 13; no. 4; pp. 258 - 276
• Main Authors: Jain, Vipul, Grossmann, Ignacio E
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.11.2001; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Benders-Decomposition; Combinatorial optimization; Computer programming; Constraint Programming; Integer Programming; Linear programming; Mathematical optimization; MILP CP Hybrid Algorithms; Optimization; Parallel Machine Scheduling; Studies
• ISSN: 1091-9856; 1526-5528; 1091-9856
• DOI: 10.1287/ijoc.13.4.258.9733

The goal of this paper is to develop models and methods that use complementary strengths of Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques to solve problems that are otherwise intractable if solved using either of the two methods. The class of problems considered in this paper have the characteristic that only a subset of the binary variables have non-zero objective function coefficients if modeled as an MILP. This class of problems is formulated as a hybrid MILP/CP model that involves some of the MILP constraints, a reduced set of the CP constraints, and equivalence relations between the MILP and the CP variables. An MILP/CP based decomposition method and an LP/CP-based branch-and-bound algorithm are proposed to solve these hybrid models. Both these algorithms rely on the same relaxed MILP and feasibility CP problems. An application example is considered in which the least-cost schedule has to be derived for processing a set of orders with release and due dates using a set of dissimilar parallel machines. It is shown that this problem can be modeled as an MILP, a CP, a combined MILP-CP OPL model (Van Hentenryck 1999), and a hybrid MILP/CP model. The computational performance of these models for several sets shows that the hybrid MILP/CP model can achieve two to three orders of magnitude reduction in CPU time.