Alienor method applied to operational research

Purpose – This paper presents an efficient algorithm for solving general constrained optimization problems that arise in operational research (OR). Design/methodology/approach – A unified approach is accomplished by converting the constrained optimization problem into an unconstrained one and by using Alienor method coupled to the new optimization preserving operator* (OPO*) technique for the resolution. Findings – A new algorithm for solving general constrained optimization problems with continuous objective function contributes to research in this area and in particular, to applications to OR. Research limitations/implications – Some improvements could probably be obtained at calculation time. We will in future work, develop an adaption of these methods and techniques to optimization problems with mixed variables or with integer and Boolean variables. Practical implications – The new algorithm can be advantageously compared with other methods such as generalized reduced gradient. Small-sized numerical examples are given. Originality/value – A new algorithm is given which guarantees a global optimal solution is easily obtained in all cases.