OR Methods

Agent Based Modelling

Agent-based modelling (ABM) is a computational modelling technique used to simulate complex systems by representing individual agents and their interactions within a dynamic environment. In agent-based models, agents are autonomous entities with predefined characteristics, behaviours, and decision-making rules. These agents can interact with each other and their environment, leading to emergent behaviour at the macroscopic level. ABM is widely used in various fields, including economics, sociology, ecology, epidemiology, and transportation planning.

Bayesian Analysis

Bayesian analysis is a statistical approach that involves using Bayes' theorem to update the probability of hypotheses or parameters considering new evidence or data. It provides a framework for incorporating prior knowledge, beliefs, or assumptions about uncertain quantities, known as prior distributions, and updating these beliefs based on observed data to obtain posterior distributions. Bayesian analysis is widely used in various fields, including statistics, machine learning, artificial intelligence, economics, and engineering.

Data Analytics and Big Data

The increasing availability of large datasets has led to a growing intersection between operational research and data analytics. OR techniques are being applied to extract valuable insights and optimise decision-making processes in the context of big data.

Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a non-parametric method used for assessing the relative efficiency of decision-making units (DMUs) in the presence of multiple inputs and outputs. It is particularly useful when traditional methods such as regression analysis or parametric optimization are not applicable due to the complexity or lack of explicit functional relationships between inputs and outputs. DEA was originally developed by Abraham Charnes, William W. Cooper, and Edwardo Rhodes in the late 1970s and has since been widely used in various fields, including operations management, economics, finance, and healthcare.

Decision Analysis

Decision analysis involves evaluating decision alternatives in situations involving uncertainty. Techniques such as decision trees, multi-criteria decision analysis (MCDA), and game theory are used to analyse and make decisions under uncertainty.

Forecasting

Forecasting methods are used to predict future events or trends based on historical data and other relevant information. Time series analysis, regression analysis, and simulation are common techniques used in forecasting.

Game Theory

Game theory is used to analyse strategic interactions between decision-makers and optimize their decisions in competitive situations. It has applications in various fields such as economics, politics, and business strategy.

Heuristics

Heuristic methods involve using rules of thumb or approximate algorithms to find good solutions to complex optimization problems in a reasonable amount of time. Metaheuristic algorithms such as genetic algorithms, simulated annealing, and tabu search are examples of heuristic methods used in OR. 

Machine Learning and Artificial Intelligence (AI)

Integrating machine learning and AI algorithms into operational research models enhances the ability to handle complex and dynamic systems. These technologies contribute to predictive modelling, optimisation, and decision support in various domains

Mathematical Modelling

This involves representing real-world problems using mathematical equations, functions, and variables. Optimisation, simulation, and queuing theory are common mathematical modelling techniques used in OR.

Multicriteria Analysis

Multi-criteria modelling, also known as multi-criteria decision-making (MCDM) or multi-objective optimisation, is a branch of decision science that deals with problems involving multiple conflicting objectives or criteria. In many real-world situations, decision-makers need to consider multiple criteria simultaneously when evaluating alternatives or making decisions. Multi-criteria modelling provides systematic approaches for handling such complex decision problems.

Neural Networks

Neural networks are computational models inspired by the structure and functioning of the human brain. They consist of interconnected nodes, or neurons, organised into layers. The most common type of neural network is the feedforward neural network, where information flows from input nodes through hidden layers to output nodes.

Optimisation

Optimisation methods aim to find the best solution from a set of feasible solutions. Linear programming, integer programming, nonlinear programming, and dynamic programming are examples of optimisation techniques used in OR.

Queueing

Queuing theory deals with the study of queues or waiting lines and helps in optimizing the performance of systems involving waiting, such as service systems, telecommunications networks, and manufacturing processes

Simulation

Simulation involves building mathematical or computational models of real-world systems to analyse their behaviour under different conditions. Monte Carlo simulation, discrete-event simulation, and agent-based modelling are common simulation techniques used in OR.

Supply Chain Optimisation

With globalisation and advancements in technology, supply chain optimisation remains a dynamic field within OR. The focus is on creating resilient and efficient supply chains, incorporating real-time data, and addressing sustainability concerns For more information on Operational Research: Methods and Applications click: https://orsociety.tandfonline.com/doi/full/10.1080/01605682.2023.2253852. 

System Dynamics

System dynamics is an approach to understanding the behaviour of complex systems over time. It is a methodology that combines various concepts from mathematics, computer simulation, and feedback control theory to analyse and model the dynamic behaviour of systems.

Vehicle Routing Problem

Vehicle Routing Problem (VRP) is a classic problem in operational research and optimisation that involves determining the optimal routes for a fleet of vehicles to deliver goods or services to a set of customers while minimising costs or maximising efficiency. The VRP is NP-hard, meaning it's computationally challenging to find the exact optimal solution for large instances, and various algorithms and techniques are used to approximate solutions