the first step in solving operations research problem is

A solution to a problem usually implies changes for some individuals in the organization. Because resistance to change is common, the implementation of solutions is perhaps the most difficult part of a problem solving exercise. Some say it is the most important part. Although not strictly the responsibility of the analyst, the solution process itself can be designed to smooth the way for implementation. The persons who are likely to be affected by the changes brought about by a solution should take part, or at least be consulted, during the various stages involving problem formulation, solution testing, and the establishment of the procedure.

Combining the steps we obtain the complete OR process. In practice, the process may not be well defined and the steps may not be executed in a strict order. Rather there are many loops in the process, with experimentation and observation at each step suggesting modifications to decisions made earlier. The process rarely terminates with all the loose ends tied up. Work continues after a solution is proposed and implemented. Parameters and conditions change over time requiring a constant review of the solution and a continuing repetition of portions of the process.

There are a number of ways to to test a solution. The simplest determines whether the solution makes sense to the decision maker. Solutions obtained by quantitative studies may not be predictable but they are often not too surprising. Other testing procedures include sensitivity analysis, the use of the model under a variety of conjectured conditions including a range of parameter values, and the use of the model with historical data.

If the testing determines that the solution or model is inappropriate, the process may return to the formulation step to derive a more complex model embodying details of the problem formerly eliminated through abstractions. This may, of course, render the model intractable, and it may be necessary to conclude that an acceptable quantitative analysis is out of reach. It may also be possible to construct a less abstract model and accept less powerful solution methods. In many cases, finding a good or an acceptable solution is almost as satisfactory as obtaining an optimal one. This is particularly true when the quality of the input data is low or when important parameters cannot be specified with certainty.

Different organizations have different ways of approaching a problem, and many do not admit quantitative techniques or analysts as part of the process. It is important to note, however, that in today’s world problems do arise and decisions are made (even inaction is a decision made by default). Many problems are solved in the first step of our process, but there will be cases when complexity, variability or uncertainty suggest that further analysis is necessary. In these cases, the Operations Research process will assist problem solving and decision making.

Operations Research Methodology

• Describing the problem;
• Formulating the OR model;
• Solving the OR model;
• Performing some analysis of the solution;
• Presenting the solution and analysis.
• Describing the Problem The aim of this step is to come up with a formal, rigorous model description. Usually you start an optimisation project with an abstract description of a problem and some data. Often you need to spend some time talking with the person providing the problem (usually known as the client ). By talking with the client and considering the data available you can come up with a more rigorous model description required for formulation. Sometimes not all the data will be relevant or you will need to ask the client if they can provide some other data. Sometimes the limitations of the available data may significantly change your model description and subsequent formulation.
• Formulating the OR Model The aim of this step is to translate the problem description into a valid OR model. The implementation of this step may be quite different depending on the OR model you are using. For example, if you are using linear programming to solve your problem, then formulating an OR model involves translating your problem into a linear programme. If you are using simulation to solve your problem, then formulating an OR model entails breaking down the behaviour of the system being simulated into a sequence of events and determining the random variables that "drive" the simulation.
• Solving the OR Model The aim of this step is to solve your OR model. Just as the formulation step depended on the OR model being used, this solution step depends on your OR model. Additionally, there may be more than one solution method for a particular OR model. For example, solving a linear programme may be done using the Revised Simplex Method or an interior point method. Often, in practice, OR models may not be solved completely due to time constraints. Other algorithms may partially solve OR models (for optimisation models, these algorithms are known as heuristics and terminate with a "good" solution that is not necessarily optimal).
• Performing analysis of the solution Often there is uncertainty in the problem description (either with the accuracy of the data provided or with the value(s) of data in the future). In this situation the robustness of our solution to the OR model can be examined using analysis. Analysis involves identifying how the solution would change under various changes to the problem data (for example, what would be the effect of a given cost increasing, or a particular machine failing?). This sort of analysis can also be useful for making tactical or strategic decisions (for example, if we invested in opening another factory, what effect would this have on our revenue?). Another important consideration in this step (and the next) is the validation of the OR model's solution. You should carefully consider what the solution means in terms of the original problem description. Make sure it makes sense to you and, more importantly, to your client. Hence, the next step, presenting the solution and analysis is very important.
• Periodic monitoring of the validity of your OR Model;
• Further analysis of your solution, looking for other benefits for your client;
• Identification of future OR opportunities.

What is Operations Research? | NC State OR

What is Operations Research?

Last Updated:  07/08/2024 | All information is accurate and still up-to-date

The Simple Answer: Operations Research (OR) is a discipline of problem-solving and decision-making. It uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps.

• Identify a problem
• Build a model around the real-world problem
• Use the model and data to arrive at solutions
• Test the solution and analyze its success
• Implement the solution

The Technical Answer: Operations Research, also known as management sciences, uses scientific methods to study systems that require human decision-making. Consequently, OR helps make the most effective systems design and operation decisions. Moreover, OR’s strength and versatility come from its diagnostic power through observation and modeling and its prescriptive power through analysis and synthesis.

Additionally, OR is interdisciplinary, drawing on and contributing to techniques from many fields. These include mathematics, engineering, economics and physical sciences. Furthermore, OR practitioners have solved various real-world problems. These range from optimizing telecommunications networks to planning armed forces deployment during wartime. Many new applications, therefore, come from current energy production and distribution issues.

The CEO of the Future is an Engineer

Studies show three times as many S&P 500 CEOs hold degrees in engineering rather than business administration. This trend includes operations research practitioners among the next generation of engineers and scientists. They are tomorrow’s business leaders.

Operations Research Offers Workplace Freedom

Operations research practitioners have offices but also work in the settings they aim to improve. For example, when collecting data, they may observe restaurant staff or watch factory workers assembling parts. Additionally, when solving problems, they analyze data in an office. This combination of fieldwork and analysis creates a dynamic and flexible work environment.

The World Needs more Operations Research

As companies compete globally, the need for operations research practitioners grows. They are engineers trained to improve productivity and quality. Their common goal is saving companies money and increasing performance.

Operations Research is all about Options

Operations research practitioners work in almost any industry worldwide. They can work in and out of the office while interacting with people and processes they aim to improve. This flexibility gives them a career advantage over other types of engineers. Operations research practitioners don’t need to specialize, keeping their options open. Consequently, they remain immune to the ups and downs of any individual industry.

Careers in Operations Research

When considering a career in operations research, it’s logical to ask,  Will I be able to get a job?” Answer:  “YES”

Operation Research Continues to Grow

According to the Bureau of Labor , operations research jobs will grow over 32% between the years 2022-2032. This is faster than average for all occupations.

Companies Seek Efficiency

Every day, companies seek new ways to reduce costs and raise productivity. They rely on operations research practitioners to develop efficient processes and reduce costs, delays, and waste. This need drives job growth for these engineers, even in slow-growing or declining manufacturing industries.

Path to Management

Many operations research practitioners become managers because their work involves management tasks.

A Promising Future

It’s a great time to be an operations research practitioner. They solve problems, and there’s never a shortage of those!

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Top 6 steps involved in operation research – explained.

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The six methodology involves in operation research are as follows: 1. Formulating the Problem 2. Constructing a Model to Represent the System under Study 3. Deriving Solution from the Model 4. Testing the Model and the Solution Derived from it 5. Establishing Controls over the Solution 6. Implementation of the Solution.

Quantitative basis for decision making is provided to managers by O.R. It enhances a manager’s ability to make long range plans and to solve the routine problems of running an enterprise/ concern. O.R. is a systematic and logical approach to provide a rational footing for taking decisions.

Operation research, like a scientific research is based on scientific methodology which involves following steps.

1. Formulating the Problem :

O.R. is a research into the operation of a man, machine, organization and must consider the economics of the operation.

In formulating a problem for O.R. study analysis must be made of the following major components:

(i) The environment

(ii) The objectives

(iii) The decision maker

(iv) The alternative courses of action and constraints.

Out of the above four components environment is most comprehensive as it provides a setting for the remaining three. The operation researcher shall attend conferences, pay visits, send observation and perform research work thus succeeds in getting sufficient data to formulate the problems.

2. Constructing a Model to Represent the System under Study :

Once the project is approved by the management, the next step is to construct a model for the system under study. The operation researcher can now construct the model to show the relations and interrelations between a cause and effect or between an action and a reaction.

Now the aim of operation researcher is to develop a model which enables him to forecast the effect of factors crucial to the solution of given problem. The proposed model may be tested and modified in order to work under stated environmental constraints. A model may also be modified if the management is not satisfied by its performance.

3. Deriving Solution from the Model :

A solution may be extracted form a model either by conducting experiments on it i.e. by simulation or by mathematical analysis. No model will work appropriately if the data is not appropriate. Such information may be available from the results of experiments or from hunches based on experience.

The date collection can clearly effect the models output significantly. Operation researcher should not assume that once he has defined his objective and model, he has achieved his aim of solving the problem. The required data collection consumes time to prepare if data collection errors are to be minimized.

4. Testing the Model and the Solution Derived from it:

As has been pointed out earlier a model is never a perfect representation of reality. But if properly formulated and correctly manipulated, it may be useful in providing/predicting the effect of changes in control variables on overall system effectiveness.

The usefulness or utility of a model is checked by finding out how well it predicts the effect of these changes. Such an analyze is usually known as sensitivity analysis. The utility or validity of the solution can be verified by comparing the results obtained without applying the solution with the results obtained when it is used.

5. Establishing Controls over the Solution:

The next phase for the operation researcher is to explain his findings to the management. It may be pointed out that he should specify that condition under which the solution can be utilized.

He should also point out weaknesses if any so that management will know what risks they are taking while employing the model to generate results. Thus he should also specify the limits with in which the results obtained from using the model are valid. He should also define those conditions under which the model will not work.

6. Implementation of the Solution :

The last phase of the operation research methodology is implementation of solutions obtained in the previous steps. In operation research though decision making is scientific but its implementation involves so many behavioral issues. Therefore the implementing authority has to resolve the behavioral issues. He has to sell the idea of utility of O.R not only to the workers but also to superiors.

The distance between O.R. scientist and management may create huddles thus the gap between one who provides a solution and the other who wants to utilize it must be eliminated, to achieve this both the management and O.R. scientist should play positive role. A properly implemented solution obtained through application of O.R. techniques results in improved working conditions and gains the management support.

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Operations Research Phases

Following are the six phases and processes of operational research:

Formulate the problem: This is the most important process, it is generally lengthy and time consuming. The activities that constitute this step are visits, observations, research, etc. With the help of such activities, the O.R. scientist gets sufficient information and support to proceed and is better prepared to formulate the problem.

This process starts with understanding of the organizational climate, its objectives and expectations. Further, the alternative courses of action are discovered in this step.

Develop a model: Once a problem is formulated, the next step is to express the problem into a mathematical model that represents systems, processes or environment in the form of equations, relationships or formulas. We have to identify both the static and dynamic structural elements, and device mathematical formulas to represent the interrelationships among elements. The proposed model may be field tested and modified in order to work under stated environmental constraints. A model may also be modified if the management is not satisfied with the answer that it gives.

Select appropriate data input: Garbage in and garbage out is a famous saying. No model will work appropriately if data input is not appropriate. The purpose of this step is to have sufficient input to operate and test the model.

"Data quality is the difference between a data warehouse and a data garbage dump." - Jarrett Rosenberg

Solution of the model: After selecting the appropriate data input, the next step is to find a solution. If the model is not behaving properly, then updating and modification is considered at this stage.

Validation of the model: A model is said to be valid if it can provide a reliable prediction of the system’s performance. A model must be applicable for a longer time and can be updated from time to time taking into consideration the past, present and future aspects of the problem.

Implement the solution: The implementation of the solution involves so many behavioural issues and the implementing authority is responsible for resolving these issues. The gap between one who provides a solution and one who wishes to use it should be eliminated. To achieve this, O.R. scientist as well as management should play a positive role. A properly implemented solution obtained through O.R. techniques results in improved working and wins the management support.

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Operations Research Simplified Back Next

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• What Is Operations Research And Its Best Practices

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When I mention the word research, what comes to mind? For most people, research has a lot to do with a team of strict-looking intelligent individuals going over piles and piles of documentation, going to various places to obtain data, and undergoing numerous processes and activities to analyze the data they were able to gather.

That just goes to show how little we know about research. Chances are high that, even if you are knee-deep in the operations side of things in your job, day in and day out, you still don’t fully know or understand the concept of operations research, or what others also refer to as operational research. This is our opportunity to correct that.

WHAT IS OPERATIONS RESEARCH?

Decision-making is one of the most vital processes in management since decisions are made in order to achieve something. In the context of business management, managerial and organizational goals are what most managers seek to achieve. And that’s what they are driving towards with every decision they make.

But decision-making is not something to be taken lightly. Considering what’s being aimed for, and what’s at stake in the grand scheme of things, you can’t just randomly pick one among a few options. (Well, technically, you can do that, but considering the risks involved, especially if you end up making a wrong choice, you’d want to spend more time thinking about it and weighing your options before deciding.)

With that being said, describing decision-making as a process is accurate. You have to undergo several steps or phases before you can confidently arrive at a decision. The same thing applies with problem-solving. There are also several steps to pass through before you can get to a viable solution to a problem. There are certainly more than two ways to go about the decision-making and problem-solving process, and operations research is one of them.

Have you ever heard of the phrases “management science” and “industrial engineering”? They are terms that also apply to operations research. If you hear about management using business analytics, marketing analysis, logistics planning and even the more broad-sounding decision support, it is basically the application of operations research.

“Operations research”, or simply OR, is described as an analytical method of problem-solving and decision-making used in managing businesses or organizations. It involves the application of advanced quantitative techniques in order to arrive at a decision or solution to a problem, so we’re talking about using mathematical and numerical techniques here.

What sets OR apart from other types of decision-making processes is how mathematical analysis plays a central role. The identified problem is broken down into its most basic components, and mathematical methods and techniques are employed to solve them.

The application of OR is widespread. In fact, all businesses can never be completely free from having to apply OR in their own business environments, regardless of the nature of their business operations or the size and scale. Retail businesses and service providers, which have pretty much straightforward business processes, will still need OR in their decision making processes.

With that said, the application of OR is more necessary on the larger and more complex operations, such as companies involved in highly technical industries such as information technology, biotechnology, engineering, military operations, and telecommunications.

The fact that all businesses have to perform functions and processes such as financial planning , manpower and resource allocation, and risk management means that they all require the practice of OR.

Here are some examples of OR applied or used in a real-world business settings:

• Forecasting and planning , such as in the determination of production capacity, manpower and resources allocation, and establishing the economic order (and reorder) quantity.
• Scheduling , such as sequencing in a supply and procurement chain, or processing orders in manufacturing assembly line.
• Marketing , such as in customer profiling and implementation of sale promotions and other campaigns.
• Facility planning or layouting , such as when designing an online processing system or the floor plan of a manufacturing building.

THE IMPORTANCE OF OPERATIONS RESEARCH

If you’re wondering why there is a need for OR, the most obvious answer is in order to facilitate business decision-making. After all, OR is a huge part of planning.

The decision-making we are referring to in this specific context has to do with optimization. Therefore, we can say that OR is very important because it enables businesses to “do things best under the given circumstances”.

But that’s too broad of an answer, and does not really explain in detail why you should use OR in decision-making and problem-solving and in managing your business organization in general. Let’s drill in.

• OR simplifies the business environment . Now you might be wondering, how is that possible? Wouldn’t it be even more complicated if we threw in some mathematical elements in the mix? Well, yes and no, but that mainly depends on how you go about the OR process. In a business environment, numbers and figures often provide the most reliable information. Quantification gives more room for objectivity, so business decisions can be made objectively, since there numbers say so.
• OR maximizes the usefulness of data . Depending on the size of the business operations, there are a lot of data that have to be dealt with on a daily basis. Larger operations are faced with millions of bits of information, and going through each and every piece of data can be tedious, time-consuming and, therefore, counter-productive. Through the use of OR techniques and analytical methods, there is a way to handle all those volumes and volumes of data in significantly less time. Obviously, this will lead to being able to make better decisions, faster.
• OR aids in the optimization of resources . Resources are scarce, so businesses have to find ways to make the best use of the resources that are currently available to them, while ensuring that they are of high quality or, at least, with quality that meets the expectations of the end users.
• OR ensures effective and efficient delivery of products or services to the end users . By applying OR in decision-making, the process becomes more systematic, so that you are able to provide the high quality products or services to the customers or end users when and where they are needed. Having high-quality products will be of no use if you are unable to deliver them to the end users when you’re supposed to.

BEST PRACTICES IN OPERATIONS RESEARCH

Follow a systematic and logical approach. for that, we suggest the seven steps of or, which we will get into more detail below..

Other literature broadly described OR to involve only three steps. First, you identify the potential answers or solutions to the problem, and they will then be analyzed and narrowed down into the most feasible or viable options. The third step involves further analysis, this time using more specific analytical tools.

When we talk of OR, however, there is the “Operations Research Approach”, which is composed of seven sequential steps. Let us walk through it together.

The Seven-Step Operations Research Approach

This approach is represented in this  diagram . We’ll be taking a look at the activities involved in each step.

Step 1. Orientation

OR is not a one-man activity. It takes a team, with members equipped with various skills and specializations assigned with various tasks and functions, depending on their strengths or the areas they excel at. Therefore, there are two things that must be done in this step.

• Form the team that will conduct the OR study . Take into account the multifunctional nature of OR when choosing the members of the team. You want them to be qualified to conduct OR, so you don’t have to start pulling in just any random person from the other departments midway through the study for the simple reason that the existing members turn out to be unable to do the job. It is advised that the areas or divisions that are directly or even indirectly affected by, or related to, the OR be represented in the team. If the OR is on product design, you’d want to include the engineering, assembly and quality control divisions to be represented, along with someone from finance and marketing, specifically those that are involved in customer and market research. Install a team leader who will be able to steer the team in the right direction, and one with the ability to manage both the work and the members of the OR team.
• Ensure that all members of the team fully understand the issues at hand , specifically on the matter regarding the OR. What are they supposed to study, and what should they pay attention to? For what reason are they conducting this specific activity, and how will it benefit the organization? These are only a few of the primary questions that you must address before the members of the team so that they won’t be “flying blind”.

Bringing them into the loop will also motivate them to do the best they could in the OR study. It is also important that you are able to instill an appreciation within the team for the objectives of the activity and for what have been done so far (if there are).

Step 2. Problem Definition

Most processes – even the scientific method – puts this on the first step, and it is considered by most to be the most difficult part of the entire process, since it will set the tone for the rest of the activities or tasks that will follow. If you don’t know what the problem is, then you will simply be spinning your wheels and going nowhere.

If, on the other hand, you were able to identify a problem, but it’s not the actual or real problem, then you will also end up wasting a lot of time and resources, and you might even end up making the wrong decisions.

In defining the problem, you have to clearly identify its scope and the results that you desire or expect to have at the end. This time, you will be more specific. Instead of saying that you want to improve the company’s product design system, you will have a more targeted objective, such as “to lower the unit production cost of the product”.

Once you’ve identified the specifics, delve deeper into it.

• Identify the specific factors that will affect your objective , clearly distinguishing those that are within your control from those that are not, and determine all possible alternative courses of action that may be taken. Say that you want to lower the unit production cost of the product, so the factors may include the flexibility of product design, factors of production used (e.g. direct materials, direct labor, overhead).
• Identify the constraints on the courses of action . There are bound to be limits that all decision-makers in business have to operate within. It is possible that the nature of the product and even government regulations and legislation do not provide enough room for flexibility in product design. Availability of resources – especially the alternative resources should you decide to change some of the inputs into the product – is also another constraint.

Step 3. Data Collection

In this step, there are two things you should take note of before you can go about successful data collection. Of course, this is under the assumption that you already know what type of data you should collect.

• Sources of data. There are many identifiable sources of data, depending on the data type you need. Generally, we look to existing standards, such as current and historical trends and set values. Another source is the system or process that is being studied, particularly on how it works in actuality.
• Methods and tools for data collection. Observation remains to be one of the most commonly used methods of data collection and, thanks to automation and computerization, combined with the flexibilities brought on by the internet, data collection is greatly facilitated. What used to take businesses years to collect data and process it into valuable information is now doable in just a matter of hours, days even.

Step 4. Model Formulation

Modeling is what sets OR apart from other decision-making processes. Where other approaches would directly look into the system and analyze it, OR goes about it by formulating a model, or a representation of the system, and using that model for its analysis.

Modeling allows the researchers to simplify the system while maintaining its accuracy and faithfulness to the original. Besides, it is much easier – and less costly – to analyze the model instead of the actual system.

The team conducting OR may create different types of models, and there are four general types of models that are often formulated and employed.

• Analog models. These are models with physical properties that are significantly smaller than the actual system being studied, and having similar characteristics with the latter. These similarities make the model and the original analogous, even if they are not identical.
• Simulation models. This involves the approach where the behavior of individual elements within the system is mimicked or mirrored. In other words, a model of a real-life situation is created, and that’s where techniques such as sampling and experimentation, if need be, are conducted. This method is usually favored as it allows testing for future improvement. Through simulation, you can analyze even complex systems by coming up with estimates of statistical measures. Values are inputted and, with every replication, you can observe the response of the system. In this day and age, when technology plays a very important role in almost all businesses, computer simulation is often applied. This allows you to look for areas of improvement, specifically in an automated business environment.
• Mathematical models. OR is considered one of the many branches of mathematics, so do not be surprised when you find yourself having to apply many mathematical methods in your OR. Without going into the most intricate details, let us list down the various logical methods employed in OR, which were also cited by Springer . The preference for usage of mathematical models is how they effectively map out all the variables and describe their relationships with each other.
• Physical models. As the name implies, this is a tangible model, which is basically a copy of the original system, but scaled down appropriately. Unlike the analogic models, which are simply made to be analogous to the original system, these scaled down versions are smaller replicas of the original. Among the four model categories, this is the hardest to pull off, especially in the case of complex systems.

Step 5. Model Solution

This is where you will attempt to solve the problem; in other words, it’s the analysis stage. Needless to say, this is the part where the OR team will spend the most amount of time and resources, employing a variety of analytical methods and techniques on the models formulated in the previous step.

Briefly, the most commonly used techniques are:

• Simulation techniques, for the analysis of simulation models. These techniques often come part and parcel with several statistical techniques. That’s right. If you though that resorting to simulation will save you from dealing with numbers, you can’t fully get away from it, since statistical computations will still hound you.
• Mathematical analysis techniques , which dominantly utilizes statistical methods, such as regression analysis, variance analysis, queuing, and statistical inference.
• Optimization techniques , where you will try to determine the best values or the optimum levels that will affect decision-making. That involves the application of various mathematical programs and statistical methods. Mathematical programming techniques often used include linear and non-linear programming, integer programming and network theory.

At the end of this step, you should have obtained a solution, after considering the results of the analytical tasks you used.

Step 6. Validation and Output Analysis

Does the process end once you’ve identified the solution? No, it doesn’t. You still have to make sure that the model you used in your analysis is, indeed, an accurate representation of the system. This is the validation part.

And that’s not all. If you thought you’re done with the analysis bit, there’s still more analysis to be done. In this case, you’ll be going over various “what if” scenarios, where you will consider the possible outcomes if the solutions obtained are implemented.

Step 7. Implementation and Monitoring

Finally you settled on the best solution or recommendation and made a decision. It is time to implement that decision.

Of course, you need to still have control over the implementation, which is why there should be a team in place to be in charge of the implementation. It is highly recommended that you place some members of the OR team in the implementing team.

Monitoring is a must, since you want to ensure that the solution decided upon is the one actually being implemented. This is also a way to remain on your toes, since unforeseen circumstances might lead to some aspects of the solution needing some tweaking along the way.

Use only the relevant data.

Out of a million pieces of data, you’re probably going to need only a fraction of it. Wading through all that data may be all right with you, but SHOULD YOU? Think of the resources you will be wasting if you do that. It will also take a lot of time, which you can devote to other core functions, instead of poring over data that won’t have an impact – even if indirect – to the matter at hand.

One of the reasons that you use advanced analytical methods is so that you can maximize data and handle as much of it as you can at one time. But that does not mean that you should analyze 100% of the data, even if 50% of it are not relevant to the problem you are solving or the decision you are trying to arrive at.

But do not focus solely on the quantity of data; you also have to count quality. Having too much data is not the only problem; having poor quality data is also just as problematic. In fact, researchers prefer having a small amount of high quality data, instead of having too much data of poor quality or no relevance at all.

Maintain close collaboration between managers and the researchers.

A certain degree of independence is encouraged when it comes to the people directly conducting OR, or the researchers. This is so that they can maintain a level of objectivity in their analysis.

But that does not mean that they should be completely removed from the management. Management support is vital if you want your OR to be successful. After all, at the end of the day, it is the management that will make the decision, and will see to its implementation. By striking a partnership with the researchers, the process will be smoother.

In fact, it is recommended that researchers work alongside the managers, or those who are directly involved in the process being analyzed.

Establish policies or a framework for the conduct of OR.

One way to give OR a strong presence in the organization is to institutionalize it. How can you go about that?

• Create a policy framework, providing details about OR – even if they are couched in general terms – which will then serve as a guide for staff who will later on conduct research. This is also one way to impress on the members of the organization the important role of OR.
• Create a reference document containing the policies or the framework , and disseminate it to the members of the organization. It won’t make any sense if you have a framework, and it is well-documented, but it remains inside the office of select few members of top management.

Make Operation Research an integral part of your business processes.

In other words, do not treat it as just a minor function that you can just assign to whoever has free time. Research activities, in general, take time and certain level of commitment on the part of the researchers, so treating it as a throwaway task is not a good idea.

• Assign staff members to focus on OR. Some businesses, refusing to spend on OR, make research as an additional task for its managers. This may be workable, but if the managers are already overworked, chances are high that they won’t devote as much time and attention to the research side of things. Oh, and one other thing: do not forget to assign someone to manage the research activities carried out. Pick someone to lead the team, so as to maintain some supervision and cohesiveness in the unit.
• Specify a dedicated time to conduct research activities. Again, it’s not a good idea to request your staff to “do their research whenever they have free time or during breaks”, or even demand that they render overtime specifically for research activities. Doing that will only make OR seem like an afterthought, instead of the important business process that it actually is. Maybe you can schedule at least one day per week for staff to do their research. This way, they will also be able to maintain focus when they’re on the job.
• Make room in your budget for OR. Yes, you need funding to conduct research successfully. Conducting OR means you will have to spend on salaries and compensation of the researchers, and other expenses incidental to the conduct of the research activities.
• Pick the right people. You have to make sure that the researchers understand what they are supposed to do, and they have the skills required to carry out the research successfully. You may have to conduct some on-the-job trainings, if necessary.
• Equip your people with the right tools. Arm them with the things they need in conducting a successful OR. If you think they will benefit more by undergoing training and workshops on OR, then send them to those activities. And we’re also talking about providing the staff with the hardware they’d need to carry out the many OR techniques that they have to use.

At the end of the day, the best practice that a company can apply in operations research is to fully commit itself to actually doing it. OR is an indispensable process in managing a business, and you’d do well to keep that in mind, if you plan on taking your business to greater heights.

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Operations Research and Optimization Techniques

• First Online: 01 January 2015

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• Ali D. Haidar 2  

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This chapter will look at the principles of operations research and quantitative methods that are most accessible and suitable for program managers. Operations research is, in principle, the application of scientific methods, techniques, and tools for solving problems involving the operations of a system in order to provide those in control of the system with optimum solutions to problems. Put simply, it is a systematic and analytical approach to decision making and problem solving. This chapter provides an overview of operations research, its approach to solving problems, and some examples of successful applications. From the standpoint of a program manager, operations research is a tool that can do a great deal to improve productivity, assist in decision making, and optimize solutions. Therefore, the potential rewards can be enormous. Optimization techniques are also explained in this chapter to help program managers understand their importance. The last part of the chapter will look at linear programming methods and applications for construction, as this is the most widely applicable field for these types of problems. Linear programming can be used to allocate, assign, schedule, select, or evaluate whatever possibilities limited resources possess for different jobs. It has been used extensively in construction-related problems, where it can deduce the most profitable methods of allocating resources.

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Dar al Riyadh-Engineering and Architecture, Riyadh, Saudi Arabia

Ali D. Haidar

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Correspondence to Ali D. Haidar .

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© 2016 Springer International Publishing Switzerland

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Haidar, A.D. (2016). Operations Research and Optimization Techniques. In: Construction Program Management – Decision Making and Optimization Techniques. Springer, Cham. https://doi.org/10.1007/978-3-319-20774-2_5

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DOI : https://doi.org/10.1007/978-3-319-20774-2_5

Published : 13 September 2015

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-20773-5

Online ISBN : 978-3-319-20774-2

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