We are interested in methods to solve mixed-integer nonlinear optimal control problems (MIOCPs) constrained by ordinary differential equations and combinatorial constraints on some of the control functions. To solve these problems we use a first discretize, then optimize approach to get a specially structured mixed-integer nonlinear program (MINLP). We decompose this MINLP into an NLP and an MI...

Abstract Superstructure optimization is a powerful but computationally demanding task that can be used to select the optimal structure among many alternatives within single optimization. In chemical engineering, such problems naturally arise in process design, where different need considered simultaneously minimize specific objective function (e.g., production costs or global warming impact). C...

Adaptive Random Searches (ARS) are simple and effective optimization methods used for handling complicated nonconvex / multimodal nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) problems. ARS iteratively adapt search characteristics according to the past successful / failure steps. Periodic search domain expansions and contractions improve significantly the reliabili...

We describe a procedure to reduce variable bounds in Mixed Integer Nonlinear Programming (MINLP) as well as Mixed Integer Linear Programming (MILP) problems. The procedure works by combining pairs of inequalities of a linear programming (LP) relaxation of the problem. This bound reduction technique extends the feasibility based bound reduction technique on linear functions, used in MINLP and MI...

Many applications must ingest and analyze data that are continuously generated over time from geographically distributed sources such as users, sensors and devices. This results in the need for efficient data analytics in geo-distributed systems. Energy efficiency is a fundamental requirement in these distributed systems, and its importance is reflected in much recent work on performance analys...

Catastrophic oil spills [1], such as the recent Deepwater BP oil spill in the Gulf of Mexico [2], have demonstrated the importance of developing responsive and effective oil spill response planning strategies for the oil industry and the government. Although a few models have been developed for oil-spill response planning, response operations and the oil weathering process are usually considere...

This paper focuses on the collision-free coordination of multiple robots with kinodynamic constraints along specified paths. We present an approach to generate continuous velocity profiles for multiple robots; these velocity profiles satisfy the dynamics constraints, avoid collisions, and minimize the completion time. The approach, which combines techniques from optimal control and mathematical...

This paper focuses on the coordination of multiple robots with kinodynamic constraints along specified paths. The presented approach generates continuous velocity profiles that avoid collisions and minimize the completion time for the robots. The approach identifies collision segments along each robot’s path and then optimizes the motions of the robots along their collision and collision-free s...

Here we present a center-cut algorithm for convex mixed-integer nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like many other algorithms for convex MINLP, the center-cut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order...

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) in electric power systems considering voltage security. VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control va...