ABSTRACT
Many practical problems in the field of industrial engineering can be modeled using operations research techniques. Such problems can be structured as the optimization of some decision variables that are restricted by a set of constraints. When the decision variables are discrete in nature, the problem of finding optimal solutions is known as combinatorial optimization. Examples of real-world combinatorial optimization problems include resource-constrained project scheduling problems, assembly-line balancing problems, vehicle routing and scheduling problems, facility location problems, facility layout design problems, job sequencing and machine scheduling problems, manpower planning problems, production planning and distribution, etc. Most of the combinatorial optimization problems found in practice are NP-complete, meaning that they cannot be solved to optimality in polynomial time. Therefore, heuristic solution approaches are commonly used to find solutions to many combinatorial optimization problems found in industrial engineering.
