We propose an exact penalty approach for solving mixed integer nonlinear programming (MINLP) problems by converting a general MINLP problem to a finite sequence of nonlinear programming (NLP) problems with only continuous variables. We express conditions of exactness for MINLP problems and show how the exact penalty approach can be extended to constrained problems.

Recently, the so-called ψ-learning approach, the Support Vector Machine (SVM) classifier obtained with the ramp loss, has attracted attention from the computational point of view. A Mixed Integer Nonlinear Programming (MINLP) formulation has been proposed for ψ-learning, but solving this MINLP formulation to optimality is only possible for datasets of small size. For datasets of more realistic ...

This work presents a review of the applications of mixed-integer nonlinear programming (MINLP) in process systems engineering (PSE). A review on the main deterministic MINLP solution methods is presented, including an overview of the main MINLP solvers. Generalized disjunctive programming (GDP) is an alternative higher-level representation of MINLP problems. This work reviews some methods for s...

Wireless Mesh Networking is a promising proposal for broadband data transmission in a large area with low cost and acceptable QoS. These features’ trade offs in WMNs is a hot research field nowadays. In this paper a mathematical optimization framework has been developed to maximize throughput according to upper bound delay constraints. IEEE 802.11 based infrastructure backhauling mode of WMNs h...

The principal idea of this paper is to exploit Semidefinite Programming (SDP) relaxation within the framework provided by Mixed Integer Nonlinear Programming (MINLP) solvers when tackling Binary Quadratic Problems (BQP). SDP relaxation is well-known to provide strong bounds for BQP in practice. However, the method is not typically implemented in many state-of-the-art MINLP solvers based on Line...

We give a short introduction to mixed-integer nonlinear programming from a linear programming perspective. In this approach, all nonlinearities are approximated by linear inequalities and spatial branching, such that in the end only linear programs remain that can be solved efficiently. We present two transportation applications from rather different fields: Railway freight on one side, and nat...

We present a method, based on formulation symmetry, for generating Mixed-Integer Linear Programming (MILP) relaxations with fewer variables than the original symmetric MILP. Our technique also extends to convex MINLP, and some nonconvex MINLP with a special structure. We showcase the effectiveness of our relaxation when embedded in a decomposition method applied to two important applications (m...

This paper aims to compare a two-echelon and a single-echelon distribution system. A mathematical model for the Single-Echelon Capacitated Vehicle Routing Problem (SE-CVRP) is proposed. This SE-CVRP is the counterpart of Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) introduced in the authors’ previous work. The proposed mathematical model is Mixed-Integer Non-Linear Programming (MIN...

This article addresses a general tri-objective two-echelon capacitated vehicle routing problem (2E-CVRP) to minimize the total travel cost, customers waiting times and carbon dioxide emissions simultaneously in distributing perishable products. In distributing perishable products, customers’ satisfaction is very important and is inversely proportional to the customers waiting times. The propose...

Purpose – To discuss a new parallel algorithmic platform (minlp_machine) for complex mixed-integer non-linear programming (MINLP) problems. Design/methodology/approach – The platform combines features from classical non-linear optimization methodology with novel innovations in computational techniques. The system constructs discrete search zones around noninteger discrete-valued variables at lo...