We present a flexible framework for general mixed-integer nonlinear programming (MINLP), called Minotaur, that enables both algorithm exploration and structure exploitation without compromising computational efficiency. This paper documents the concepts and classes in our framework and shows that our implementations of standard MINLP techniques are efficient compared with other state-of-the-art...

This paper addresses the optimal design and planning of sustainable chemical supply chains (SCs) in the presence of uncertainty in the damage model used to evaluate their environmental performance. The environmental damage is assessed through the Eco-indicator 99, which includes the recent advances made in Life Cycle Assessment (LCA). The overall problem is formulated as a bi-criterion stochast...

The paper examines the applicability of mathematical programming methods to the simultaneous optimization of the structure and the operational parameters of a combined-cycle-based cogeneration plant. The optimization problem is formulated as a non-convex mixed-integer nonlinear problem (MINLP) and solved by the MINLP solver LaGO. The algorithm generates a convex relaxation of the MINLP and appl...

We propose a bilevel mixed-integer nonlinear programming (MINLP) model for the optimal investment of biorefinery facilities considering non-cooperative farmers and biofuel consumers. Interactions among the supply chain players are captured through a single-leader-multiple-follower Stackelberg game under the generalized Nash equilibrium assumption. Given a three-echelon superstructure, the lead ...

This paper studies the integration of geological sequestration and microalgae biofixation options for greenhouse gas (GHG) abatement using a supply chain optimisation approach. We propose a multi-scale multi-period mixed-integer nonlinear programming (MINLP) model, which accounts for CO2 transportation pipeline network design, algae processing route and product selection, as well as the seasona...

In this paper a new approach for the global solution of nonconvex MINLP (Mixed Integer NonLinear Programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applying different variable transformation techniques and a discretization scheme a lower bounding convex MINLP problem can be derived. The convexified MINLP problem can be solved with st...

Mixed Integer Nonlinear Programming (MINLP) problems present two main challenges: the integrality of a subset of variables and nonconvex (nonlinear) objective function and constraints. Many exact solvers for MINLP are branch-and-bound algorithms that compute a lower bound on the optimal solution using a linear programming relaxation of the original problem. In order to solve these problems to o...

This paper considers the solution of Mixed Integer Nonlinear Programming (MINLP) problems. Classical methods for the solution of MINLP problems decompose the problem by separating the nonlinear part from the integer part. This approach is largely due to the existence of packaged software for solving Nonlinear Programming (NLP) and Mixed Integer Linear Programming problems. In contrast, an integ...

We present a specialized branch-and-bound (b&b) algorithm for the Euclidean Steiner tree problem (ESTP) in R and apply it to a convex mixed-integer nonlinear programming (MINLP) formulation of the problem, presented by Fampa and Maculan. The algorithm contains procedures to avoid difficulties observed when applying a b&b algorithm for general MINLP problems to solve the ESTP. Our main emphasis ...

Mathematical programming has long been recognized as a promising direction to the efficient solution of design, synthesis and operation problems hat can gain industry the competitive advantage required to survive in today’s difficult economic environment. Most of the engineering design roblems can be modelled as MINLP problems with stochastic parameters. In this paper a decomposition algorithm ...