## Optimization and Decision Support Solution

Most systems function with their operators' inputs. An operator's input is basically a decision made by the operator as to how best to run the system to meet a specific objective. A system becomes intelligent when its operation is driven by optimized decisions.

Some systems can be operated in many different conditions and yet achieve somewhat similar output. However each operating condition may incur a different cost. An optimized system is the one operated with a specific condition which incurs the least cost while delivering the demanded output and operating within its limits.

Choosing a way to run a system is a decision making. It is a natural process to seek the best possible choice for a given objective as the decision making process takes place. Many real-world decision making processes are such complicated that a mathematical model is needed to simulate and evaluate the behavior of the system under different scenarios. They involve the exploration of a huge number of constrained options, which can't be easily derived and analyzed with simple logics and calculations, to identify the feasible optimal. It is a task far beyond the ability of a human mind. We call this type of decision making process an optimization problem and its solution is the best decision sought.

Our expertise and what we offer

At Optimize Systems, we develop software-based decision support systems that help companies to solve highly complicated design, operation, and scheduling optimization problems which are computational intensive, and thus require the use of computer.

We study a system at hand, identify optimization opportunity, collect data, construct mathematical model, develop optimization algorithm, and then build a software system that the users can use to support their decision making as to how to make the most out of a system to achieve optimization that results in, for example, lowest possible costs or highest possible performance or efficiency.

Competitive edge and benefits

The typical benefit of optimization is cost saving. Depending on the type of system, a saving of as much as tens to hundreds of thousands per month or per year is thinkable with optimization. Fertile grounds for optimization include industrial process

systems, production systems, and energy-intensive systems, which offer unimaginably attractive year-after-year savings if optimized. In some applications, optimization is used to maximize product performance to create a feature differentiation which enhances the company's competitive edge and brand. Optimization can be a powerful business strategy for a company to out-compete its competitors by inventing cost advantage and high-performing products or services.

How it all works

Generally, optimization is an activity of finding the best way of doing something that leads to the highest attainable efficiency or least cost or the fulfilment of some other type of objective. The process of solving an optimization problem can require the effort of searching through thousands to millions of possible solutions to pin down the best solution. In some cases, this laborious process also has to be continually repeated to find the new optimum solution as the operating conditions change. This is humanly impossible, and hence requires the problem to be formulated mathematically and solved programatically with the aid of an intelligent computer algorithm to facilitate and speed up the process. This computer algorithm would be so smart that it doesn’t have to evaluate all possible solutions and yet be able to rapidly nail down the best. We employ artificial intelligence to achieve this.

Technical insights

In order for an optimization problem to be solved mathematically, it has to be formulated mathematically into objective and constraints. Objective is a mathematical function of decision variables, which one wants to either minimize or maximize. Constraints refer to a set of mathematical equations which describe the boundaries that confine the values of the decision variables. An optimization problem is solved by using a mathematical algorithm to simulate the influence of the values of the decision variables on the objective function, and then pick the combination of decision variables that either minimize or maximize the objective function.

Some systems can be operated in many different conditions and yet achieve somewhat similar output. However each operating condition may incur a different cost. An optimized system is the one operated with a specific condition which incurs the least cost while delivering the demanded output and operating within its limits.

Choosing a way to run a system is a decision making. It is a natural process to seek the best possible choice for a given objective as the decision making process takes place. Many real-world decision making processes are such complicated that a mathematical model is needed to simulate and evaluate the behavior of the system under different scenarios. They involve the exploration of a huge number of constrained options, which can't be easily derived and analyzed with simple logics and calculations, to identify the feasible optimal. It is a task far beyond the ability of a human mind. We call this type of decision making process an optimization problem and its solution is the best decision sought.

Our expertise and what we offer

At Optimize Systems, we develop software-based decision support systems that help companies to solve highly complicated design, operation, and scheduling optimization problems which are computational intensive, and thus require the use of computer.

We study a system at hand, identify optimization opportunity, collect data, construct mathematical model, develop optimization algorithm, and then build a software system that the users can use to support their decision making as to how to make the most out of a system to achieve optimization that results in, for example, lowest possible costs or highest possible performance or efficiency.

Competitive edge and benefits

The typical benefit of optimization is cost saving. Depending on the type of system, a saving of as much as tens to hundreds of thousands per month or per year is thinkable with optimization. Fertile grounds for optimization include industrial process

systems, production systems, and energy-intensive systems, which offer unimaginably attractive year-after-year savings if optimized. In some applications, optimization is used to maximize product performance to create a feature differentiation which enhances the company's competitive edge and brand. Optimization can be a powerful business strategy for a company to out-compete its competitors by inventing cost advantage and high-performing products or services.

How it all works

Generally, optimization is an activity of finding the best way of doing something that leads to the highest attainable efficiency or least cost or the fulfilment of some other type of objective. The process of solving an optimization problem can require the effort of searching through thousands to millions of possible solutions to pin down the best solution. In some cases, this laborious process also has to be continually repeated to find the new optimum solution as the operating conditions change. This is humanly impossible, and hence requires the problem to be formulated mathematically and solved programatically with the aid of an intelligent computer algorithm to facilitate and speed up the process. This computer algorithm would be so smart that it doesn’t have to evaluate all possible solutions and yet be able to rapidly nail down the best. We employ artificial intelligence to achieve this.

Technical insights

In order for an optimization problem to be solved mathematically, it has to be formulated mathematically into objective and constraints. Objective is a mathematical function of decision variables, which one wants to either minimize or maximize. Constraints refer to a set of mathematical equations which describe the boundaries that confine the values of the decision variables. An optimization problem is solved by using a mathematical algorithm to simulate the influence of the values of the decision variables on the objective function, and then pick the combination of decision variables that either minimize or maximize the objective function.

Examples of where mathematical optimization is in use

Examples of where mathematical optimization is in use

- How should an airline schedule its fleet and price its tickets in order to minimize fuel costs and maximize booking rates in the presence of a set of conflicting priorities
- How the operation of a set of machines or production lines should be scheduled in order to maximize efficiency and minimize energy costs while fulfilling the expected demands and without exceeding the their operating limits
- How an energy-intensive system should be operated so that its energy consumption is minimum without compromising its operating and safety requirements while delivering the demanded output
- How a factory with a range of products manufactured on the same production lines should plan its production in order to maximize profit while meeting its production target and operating whithin its capacity
- How should a company with factories and retail shops in different locations plan the delivery of goods in order to minimize the transportation costs while meeting the orders from each retail shop and without having to disrupt the production rates of factories
- How to optimize a product's design to minimize its material cost without compromising product performance and safety
- How to optimize a system's design to maximize its performance without increasing fabrication/material or operation cost
- How to constantly optimize a certain property of a system in different operating modes or changing operating environment without violating the operating constraints

**Our structured processes of optimization solution development**