Flexibility in Modeling Any Optimization Problem
Unlike other systems, QR Optimization platform is not narrowly designed for a specific purpose. It offers utmost flexibility to model in great detail any complex optimization problem. E.g., financial portfolio, power generation portfolio, high-frequency trading, real-asset operations, airlines, shipping, R&D investment and business development.
The optimization has a multi-tier modular architecture. In particular the following tiers are fully separated.
Configuration of real-asset operations, stages, input, output, heat-rates, transformation processes, constraints and various rules.
Definition of detailed cost, revenues, losses and valuation rules, including nonlinear relationships among the quantities.
Modeling and forecasting of data curves needed, e.g., prices.
Ability to define custom optimization objective functions via mathematical formulae, e.g., minimize costs, maximize profits, maximize transportation capacity, minimize risk, optimize hedge ratio, etc.
QR Optimization can handles a wide range of optimization problem as long as:
Portfolio allocation, or operational states of the problem, i.e., volumes or positions can be discretized.
Operational transitions across states and levels can be expressed as rules, along with linear and non-linear constraints.
All key quantities can be added as needed, e.g., volumes, prices, charges, schedules, fees, costs, and revenues across various operational levels.
The optimization objective function can be expressed in terms of mathematical formulas involving key quantities of the problem at hand.
QuantRisk is the Perfect Tool to Optimize Your Bottom Line
Strategic Optimal Decision Making
Optimization and what-if-scenario projections over long horizons are the best tools for the valuation (net present value, net residual value, and P&L) of complex financial contracts, physical assets, logistics and operations.
Optimize decision making in planning, business development, acquisition, hedging, expansion or scaling back a given operation or asset.
Enhance the value or profit of an asset by exploring operational or financial strategies. Define different pricing rules, or remove or add some constraints to increase or limit some contractual flexibilities. Then execute optimization on these scenarios to measure and compare the effect on the cash flow and P&L.
Tactical Optimal Decision Making
Maximize profit in high-frequency trading over short horizons or operation of assets such as power plant generation and bidding to spot electricity markets.
When used over short horizons, QR optimization platform runs a full optimization cycle and P&L valuation in seconds. This exceptional speed allows the execution of optimization in real time as prices change, every trading period around the clock.
Dynamic trading assisted and guided by real-time optimization can increase trading P&L significantly, e.g., in power portfolio optimization.
Where Can QuantRisk Optimization be Expertly Used?
For a generation asset selling wholesale physical energy to utilities or retail marketers, QR Optimization maximizes profit by trading real-time generation in and out of the spot market to satisfy contractual obligations, subject to all constraints. The excess capacity beyond contractual obligations is treated as merchant.
For a generation asset that has merchant capacity, QR Optimizer will maximize profit by dispatching the plant to maximum capacity when it is in the money or shut it down when out of the money, subject to all operational constraints.
For a utility or energy marketer whose supply portfolio is composed of physical bilateral PPA contracts and the spot market, QR will optimize nomination across the said supply portfolio to minimize energy cost, while fulfilling all supply contractual obligations, and total SCADA demand load.
For resource allocation and operations QR Optimization can have a wide range of applications in operational decision making as well as resource allocation. Resources can be financial, human, physical and material. Target operations can be any real asset operation such as R&D, exploration, production, transportation and logistics of commodities, goods, products, or health care.
For financial portfolios QR Optimization will determine the portfolio allocation across assets that minimizes a given risk indicator (VaR, exposure, or cash flow at risk), or maximizes net cash, profit or liquidity ratio. Optimal decision making in business development activities such as acquisition, development, hedging, expansion, or retraction.
Any complex optimization problem whose operational states, volumes, and positions can be discretized, and whose objective function can be expressed in terms of any mathematical formulas involving some quantities, can be handled.
Real-time Business Intelligence and Process Automation
Performance analysis is provided as decision making support. QR System not only suggests optimal operational levels, but it also provides a real-time performance analysis of losses and gains. To accomplish this, it computes and compares in real-time the full P&L under two different scenarios: actual operation or trading, versus the optimal strategy. These are then displayed on a live Trading or Decision-Making Panels as described below.
QR Optimization is seamlessly integrated with operations and logistics. Optimal operational levels are transferred seamlessly to any desired operational or logistics system for scheduling, dispatch, nominations, transportation, storage, etc.
QR Optimization panel displays on a live web panel, the optimal decision levels for each asset or operation along with their corresponding P&L with any desired breakdown, such as revenue, costs, net cash, market, and operational data. This enables real-time optimal decision making in fast moving markets, where traders have to make split-second buy or sell decisions.
Achieve High-Performance Computing on Off-the-Shelf Servers
QR Optimization methodology is neither single period nor linear programming.
QR framework is based on multi-period dynamic optimization. This means when the problem at hand requires it, we optimize multiple periods together allowing for path dependencies across time.
The user can define any desired nonlinearity in the objective function and the constraints. For example, when optimizing power generation assets, the fuel cost is a nonlinear function of the load.
Implementation uses very advanced high-performance computing technologies, including advanced sparse large scale mixed integer programming, and evolutionary algorithms for global optimization.
QR Optimization engine has built-in computational efficiency controls to ensure that the Optimization Model you have created can be optimally executed on your server while best managing its CPU or RAM resources. You can indeed calibrate the optimization engine by adjusting the convergence tolerance level, or the duration of iteration cycles.
The global optimization algorithms and methodologies are powerful enough to allow the handling of nonlinear multi-objective functions to either combine different optimization perspectives for a single player, or to model multiple players in the market.
QR Optimization System is implemented within QR Parallel Processing Architecture. More specifically, you can create a customized execution architecture breaking down the full optimization cycle into sub-tasks. Then the parallel processor automatically assigns, dispatches, and manages execution across multiple threads. This built-in computational scalability leverages server resources optimally and accelerates the entire optimization process. It produces optimal strategies under a minute (on off-the-shelf servers), with the ultimate goal of enabling real-time optimal decision making in fast moving markets, where traders have to make split-second buy or sell decisions.
QR Optimization System allows you to create any desired valuation model for real assets and operations by defining them as portfolios of path dependent call and put options. The call options operate the asset when it is in the money, i.e., when operational cash flow is positive. The put options do not operate the asset when it is out of the money, i.e., when operational cash flow is negative. Both options are subject to operational and contractual constraints. In reality, a decision made in any time frame impacts the range of possible decisions in past and subsequent time frames. To capture this in a dynamic optimization perspective, we model path dependency by optimizing or evaluating the call and put options across blocks of 24 hours or longer.