Operational research

Operational research involves analysis of various operations performed in human and technical systems. It is basic tool used by mathematical theory of decision.

Genesis of this method is connected with a operations research group of experts working at the headquarters of the British army in 1938 and directed by the famous British physicist P.M. Blackett. The group, with representatives from various science areas, developed quantitative solutions to problems related to the organization of military pursuits. After the war, methods used in the military proved to be useful to solving problems in various fields of management. During time decision problems solved by the operational are more and more complex and therefore in these studies managers began to use system approach.They are also the primary area of use of newly emerging in the 40s and 50s of twentieth century computer technology.

In practice, an operational research is a management tool for the analysis and the precise phenomena and processes in the enterprise by using mathematical methods. Operational research is a collection of structured methods and techniques derived from the theory of mathematical programming and econometrics used to describe, analyse and search for optimal solutions for decision making. The subject of operational research are description and the solution based on the use of mathematical models and tools

Operational research methodology

Operations research methodology includes the following steps:

• Identification of the decision problem.
• Construction of a mathematical model.
• Solution of a developed model.
• Verification of a model.
• The development of the control system.

The basic problems solved using operations research include: inventory management, analysis of structure of production, apportionment, transport, storage handling, ordering, route selection, exchange, competition.

Linear programming

Linear programming was the earliest method used in operations research for building and solving deterministic models. It is applicable to the analysis of the decision in which both the constraints and the objective function are linear. Due to simplicity, to solve linear programming models managers use graphical methods. Typical decision-making problems analyzed using linear programming include the following:

• Selection of structure of production,
• Compound problem (the problem of diet),
• The problem of cutting,
• Selection of production process,
• The issue of transportation and related issues
• Location of production problems,
• The problem of minimizing empty runs,
• Routing issues.

Queueing theory

The issue of queues is a typical model of stochastic process involving both probabilistic and statistical models. Typical queuing problems arise when some place is randomly visited by any number of people or objects. This could be, for example, the post office box, the patients coming to the doctor, customers coming in to cash registers at the supermarket, etc. Specifying those persons and other objects as "customers" you will find that they present themselves in time that could not be predicted in advance. It can be represented as a random process which can be described by a suitable probability distribution. Goal of solving the problem of queuing may be, for example, identify and minimize customer waiting time in the queue, specify the amount of service positions, to avoid unnecessary delays resulting from lack of customers.

Network programming

Methods for network programming. Models of network are used to minimize the duration and costs of complex operations. Network models can be divided into two basic groups - methods for deterministic network logical structure (DAN - Deterministic Activity Networks) and stochastic network logical structure (GAN - Generalized Activity Networks). The networks of deterministic selection order is clearly defined. This group includes the critical path method (CPM - Critical Path Method), its various forms and the method of PERT (Program Evaluation and Review Technique). The CPM method selection activities and their duration is deterministic. The PERT method selection is a deterministic function, and their duration is random. Both the CPM method and the PERT method may be supplemented by an analysis of the time-cost - the method of CPM-COST and PERT-COST.

Modelling of competition and conflict

Modelling of competition, conflict and negotiation. Issues of competition, conflict and negotiation are modeled using game theory. Currently, game theory is often used to build models of decision-making, which have been identified as a complex. Game theory is sometimes referred to as "interactive decision theory". It concerns the behavior of rational decision makers whose decisions affect each other. Its name stems from the fact that it was originally used in the analysis of games such as chess or poker. Game theory is also used in the description and study of all kinds of conflicts and negotiations.

Examples of Operational research

• Supply Chain Optimization: Supply chain optimization focuses on improving the efficiency of the supply chain by minimizing costs, reducing lead times, and increasing customer satisfaction. It involves analyzing various aspects of the supply chain, such as inventory levels, transportation routes, and the use of technology, to identify areas of improvement.
• Process Improvement: Process improvement involves analyzing processes to find ways to make them more efficient. This includes analyzing the process steps, identifying bottlenecks, and developing new methods to improve the process.
• Network Optimization: Network optimization focuses on improving the efficiency of networks by minimizing costs and maximizing performance. It involves analyzing the structure of the network, such as the number of nodes, the topology, and the amount of traffic, to identify areas of improvement.
• Predictive Analytics: Predictive analytics involves using data to predict future outcomes. It involves analyzing historical data to identify patterns and trends, and using machine learning algorithms to make predictions.
• Decision Modeling: Decision modeling involves creating mathematical models to help decision makers make better decisions. It involves collecting data, analyzing it to identify patterns, and using mathematical models to determine the best course of action.

Advantages of Operational research

Operational research offers various advantages that help organizations in optimizing their operations and improving their decision making process. The advantages of operational research include:

• Improved Efficiency: Operational research helps organizations to identify and eliminate inefficiencies in their operations, enabling them to increase their efficiency and productivity.
• Cost Reduction: By eliminating wasteful processes and identifying more cost-effective ways of achieving desired outcomes, operational research can help organizations reduce their costs.
• Enhanced Decision Making: Operational research helps organizations to develop better decision-making models and to make more informed decisions by providing accurate and up-to-date data about their operations.
• Improved Quality: Operational research can help organizations improve the quality of their products and services by providing insights into their processes and operations.
• Increased Profits: By optimizing their operations and improving their decision-making process, operational research can help organizations increase their profits.

Limitations of Operational research

• Operational research is limited by the availability of data and information. Data and information is essential for the development of models and their analysis. Without accurate and complete data and information, models are likely to be inaccurate and unreliable.
• Operational research requires significant computing power and sophisticated software. This can be expensive and beyond the reach of many organisations.
• Operational research is based on assumptions which may not be valid in the real world. This can lead to inaccurate results and conclusions that are not applicable.
• Operational research requires a level of expertise and knowledge to ensure that the models are accurate and the results are reliable. This may be difficult to find in many organisations.
• Operational research relies on accurate forecasting of future conditions and events. This can be difficult to do accurately, especially over a long period of time.

Other approaches related to Operational research

Operational research is an important tool for decision making, and there are various approaches related to it. These approaches include:

• Mathematical Optimization: This approach involves the use of mathematical techniques to solve problems. These techniques include linear and nonlinear programming, dynamic programming, game theory, and integer programming.
• Simulation: This approach involves the use of computer models to simulate the behavior of a real-world system. This can be used to analyze complex systems, to predict outcomes, and to test different strategies.
• Queuing Theory: This approach involves the use of mathematical models to study the behavior of queues. This can be used to analyze the performance of systems and to optimize the delivery of services.
• Heuristics: This approach involves the use of heuristics, or rules of thumb, to solve complex problems. Heuristics can be used to narrow down the search space and to make decisions in uncertain or complex environments.
• Network Optimization: This approach involves the use of algorithms to optimize the performance of networks. Network optimization can be used to optimize the flow of data or resources in a network.

In summary, Operational Research involves the use of mathematical and computer models to analyze and optimize operations and decision making. There are various approaches related to it including mathematical optimization, simulation, queuing theory, heuristics, and network optimization.

Operational research — recommended articles

Advantages of artificial intelligence — CAE — Descriptive model — Information gap — Artificial intelligence — Innovative research — Organizational techniques — Impact of information on decision-making — Management by the numbers

References

• Ackoff, R. L. (1979). The future of operational research is past. Journal of the operational research society, 93-104.
• Checkland, P. (1981). Systems thinking, systems practice.
• Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10), 1037-1041.
• Larnder, H. (1984). OR Forum—The Origin of Operational Research. Operations Research, 32(2), 465-476.