Mixed–Integer Real–Time Iterations

2011 ◽  
pp. 89-116
Author(s):  
Christian Kirches
Keyword(s):  
Real Time ◽  
Mixed Integer

scholarly journals Application of Predictive Control in Scheduling of Domestic Appliances

2020 ◽  
Vol 10 (5) ◽  
pp. 1627 ◽  
Author(s):  
Himanshu Nagpal ◽  
Andrea Staino ◽  
Biswajit Basu
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Predictive Control ◽  
Peak Power ◽  
Mixed Integer ◽  
Consumption Pattern ◽  
Problem Model ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Peak Power Consumption

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.

Mixed Integer Linear Programming for UAV Trajectory Planning Problem

2014 ◽  
Vol 541-542 ◽  
pp. 1473-1477 ◽  
Author(s):  
Lei Zhang ◽  
Zhou Zhou ◽  
Fu Ming Zhang
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Trajectory Planning ◽  
Arrival Time ◽  
Linear Constraints ◽  
Mixed Integer ◽  
Planning Problem ◽  
Convex Constraints ◽  
3D Environments ◽  
On Line

This paper describes a method for vehicles flying Trajectory Planning Problem in 3D environments. These requirements lead to non-convex constraints and difficult optimizations. It is shown that this problem can be rewritten as a linear program with mixed integer linear constraints that account for the collision avoidance used in model predictive control, running in real-time to incorporate feedback and compensate for uncertainty. An example is worked out in a real-time scheme, solved on-line to compensate for the effect of uncertainty as the maneuver progresses. In particular, we compare receding horizon control with arrival time approaches.

Real Time Optimal Task Allocation in Highly Dynamic Environments

2005 ◽  
Author(s):  
David R. Schneider ◽  
Mark Campbell
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Dynamic Test ◽  
Dynamic Environments ◽  
New Method ◽  
Mixed Integer ◽  
Combined Method ◽  
Test Environment ◽  
Time Optimal ◽  
Minimum Spanning Forest

Of the methods developed for Optimal Task Allocation, Mixed Integer Linear Programming (MILP) techniques are some of the most predominant. A new method, presented in this paper, is able to produce identical optimal solutions to the MILP techniques but in computation times orders of magnitude faster than MILP. This new method, referred to as G*TA, uses a minimum spanning forest algorithm to generate optimistic predictive costs in an A* framework, and a greedy approximation method to create upper bound estimates. A second new method which combines the G*TA and MILP methods, referred to as G*MILP, is also presented for its scaling potential. This combined method uses G*TA to solve a series of sub-problems and the final optimal task allocation is handled through MILP. All of these methods are compared and validated though a large series of real time tests using the Cornell RoboFlag testbed, a multi-robot, highly dynamic test environment.

Non-integer and mixed integer forms in long n -alkanes observed by real-time LAM spectroscopy and SAXS

Polymer ◽  
2000 ◽  
Vol 41 (25) ◽  
pp. 8775-8780 ◽  
Author(s):  
G. Ungar ◽  
X.B. Zeng ◽  
S.J. Spells
Keyword(s):  
Real Time ◽  
Mixed Integer

Hybrid intervention scheme-based optimization algorithm for real-time management of reactive power resources

2017 ◽  
Vol 65 (11) ◽  
Author(s):  
Deesh Dileep ◽  
José Luis Rueda Torres ◽  
Sander Franke ◽  
Peter Palensky
Keyword(s):  
Power System ◽  
Real Time ◽  
Reactive Power ◽  
Transmission System ◽  
Mixed Integer ◽  
Base Case ◽  
System Control ◽  
Case Scenario ◽  
Base Case Scenario ◽  
Non Linear

AbstractThis article introduces a Hybrid Intervention Scheme Based Optimization (HIBO) algorithm solving an Optimal Reactive Power Management (ORPM) problem in real-time using a Mixed Integer Linear Programming (MILP) solver. The ORPM problem presented here contains a linear objective function containing four objectives separated using a set of static penalty factors for each area. The non-linear optimization problem has been assumed linear by localizing the search for solution, this is done by introducing a penalty on the change from the original state or the base case scenario. Thereby, optimizing the non-linear ORPM in linear steps makes it a fast solver for small changes in power system state. A contingency analysis (for N-1 voltage violations) is included for ensuring the safety and reliability of the power system. The results are used to update the ORPM problem or stop if the system is secure. The optimization variables used to represent transformer taps and shunt device switches are handled as discrete integers and remaining variables as continuous real numbers. The intervention scheme, objectives and constraints used in the HIBO have been derived through surveys conducted at a transmission system control center and are supported using literature. Validation of the HIBO algorithm was performed on the Dutch transmission network model after dividing it into four regional areas. Convergence characteristics of the HIBO algorithm are compared using results. From the results, it is concluded that the HIBO algorithm is a competitive optimization solver, suitable for deployment in the secondary voltage control scheme within system operations domain for transmission system operators.

scholarly journals A Multi-Objective Mathematical Model for Real-Time Employee Assignment in the Service Industry

2021 ◽  
Author(s):  
Gercek Budak ◽  
Xin Chen
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Customer Service ◽  
Service Industry ◽  
Programming Model ◽  
Manufacturing Sector ◽  
Mixed Integer ◽  
Multi Objective ◽  
Service Rates ◽  
The Mathematical Model

Abstract The American economy has shifted toward services since the 1980s. The service industry is an important part of economy and is growing quickly in the last three decades. It is more human-capital intensive than the manufacturing sector and there is a shortage of highly-skilled workforce. One solution to this problem is to improve the efficiency through optimization. Because demand in the service industry changes constantly, it is a great challenge to determine the number of employees and their tasks to improve customer service while reducing cost. This article develops a multi-objective mixed-integer linear programming model to dynamically assign employees to different workstations in real time. A case study of the model is solved in less than one second and its pareto optimal solutions determine the number of employees who are assigned to each workstation and the expected customer service times. The mathematical model is robust and provides optimal employee assignment and service rates for workstations in many situations.

scholarly journals Probabilistic Model for Real-Time Flood Operation of a Dam Based on a Deterministic Optimization Model

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3206
Author(s):  
Víctor Cuevas-Velásquez ◽  
Alvaro Sordo-Ward ◽  
Jaime H. García-Palacios ◽  
Paola Bianucci ◽  
Luis Garrote
Keyword(s):  
Real Time ◽  
Optimization Model ◽  
Flood Control ◽  
Evaluation Method ◽  
Risk Index ◽  
Mixed Integer ◽  
Global Risk ◽  
Case Based Learning ◽  
Milp Model ◽  
Conventional Models

This paper presents a real-time flood control model for dams with gate-controlled spillways that brings together the advantages of an optimization model based on mixed integer linear programming (MILP) and a case-based learning scheme using Bayesian Networks (BNets). A BNet model was designed to reproduce the causal relationship between inflows, outflows and reservoir storage. The model was trained with synthetic events generated with the use of the MILP model. The BNet model produces a probabilistic description of recommended dam outflows over a time horizon of 1 to 5 h for the Talave reservoir in Spain. The results of implementing the BNet recommendation were compared against the results obtained while applying two conventional models: the MILP model, which assumes full knowledge of the inflow hydrograph, and the Volumetric Evaluation Method (VEM), a method widely used in Spain that works in real-time, but without any knowledge of future inflows. In order to compare the results of the three methods, the global risk index (Ir) was computed for each method, based on the simulated behavior for an ensemble of hydrograph inflows. The Ir values associated to the 2 h-forecast BNet model are lower than those obtained for VEM, which suggests improvement over standard practice. In conclusion, the BNet arises as a suitable and efficient model to support dam operators for the decision making process during flood events.

Real Time Iterations for Mixed-Integer Model Predictive Control

2020 ◽  
Author(s):  
Massimo De Mauri ◽  
Wim Van Roy ◽  
Joris Gillis ◽  
Jan Swevers ◽  
Goele Pipeleers
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Mixed Integer
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