Evolution of Simulation Paradigms in OR

Evolution of Simulation Paradigms in OR

Yang Li (Applications & Services, Research & Technology, British Telecom, UK)
Copyright: © 2014 |Pages: 9
DOI: 10.4018/978-1-4666-5202-6.ch083

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Co-Evolution Of Socio-Economy And Simulation Paradigms

Figure 1 shows the co-evolution of socio-economy and simulation paradigms viewed by the author.

Figure 1.

Co-evolution of socio-economy and simulation paradigms

SD was created during 1950s by Professor Jay Forrester (1961). Typical constructs in SD are stock, flow and feedback. Mathematically, these could be represented as a set of differential equations. The key feature of SD is to look at things in aggregated volumes rather than from individual perspective. 1950s is a post-war era in which world economy was being recovered and re-built. In most countries, this is a decade when agriculture sector made the first and biggest stride to feed hungry stomachs. This is also a time when collectivism prevailed as there was very limited resource to consume. In the UK, National Health Service (NHS) was set up to centrally control health service resources to ensure people from all walks of life have access to the service. Typical SD applications in this era are chemical factory flows, weather forecast, agricultural throughput and epidemic disease propagation, all looking at dynamics of these systems in aggregated numbers.

DES emerged during 1960s and has remained the main industrial simulation technique for over 40 years. Banks et al. (2005) provided a reference book on this topic. Typical constructs in DES are entities, activities and queues. These constructs are linked together to form a complex process flow in which multiple entities flow across various queues and activities. Entities could also be stored in the resource pool attached to an activity and are used to match incoming entities. The matching in DES is generally a simple linear mapping, as most commercial DES toolkits do not have free coding capabilities to customize more sophisticated behavioral logic. DES models often employ probability distribution functions such as Normal, Uniform, Weibull and Fatigue Life to handle stochastic processes.

During 1960s and 1970s, manufacturing has gradually overtaken agriculture to become the dominating sector, as consumers once again aspired for mass-produced industrial goods for comfort. Large manufacturing factories employed DES tools to help simulate and optimize their production processes. In the DES models, product components are modeled as incoming entities, conveyors as queues, processing machines as activities and resources. In some factories, such as those manufacture automobiles or washing machines, human labor becomes part of the activities and resources; they are strictly trained and managed to perform in time and in quality comparable to machines such that the whole manufacturing processes are in harmony.

Key Terms in this Chapter

Agent-Based Simulation (ABS): Is a relatively new paradigm that simulates the simultaneous operations and interactions of multiple agents, in an attempt to re-create and predict the appearance of complex phenomena. In OR context, allowing agents to behave differently and communicate among themselves distinguishes ABS from other simulation techniques such as SD and DES. Existing ABS applications are primarily found in the science, social, economy and market domains; the rules used in these agents are either theory-based or empirical-based and the results of simulation are often lack of convincing power.

Operational Research (OR): Encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory and other stochastic-process models. OR cover large areas in economy such as agricultural sector, manufacturing sector and service sector. In comparison to analysis in social science and economics domains, OR often employ more objective-based methodologies to gather, analyze and verify data as detailed data can be more easily collected during business operation.

Discrete Event Simulation (DES): Emerged during 1960s in the manufacturing boom; it models the operation of a system as a discrete sequence of events in time, typical of a manufacturing process. Each event occurs at a particular instant in time and marks a change of state in the system. This contrasts with continuous simulation in which the simulation continuously tracks the system dynamics over time. Conventional DES constructs are entities, activities and queues; these constructs are linked to form a complex process in which entities flow. Probability functions are also used to model stochastic processes.

Simulation: Is the imitation of the operation of a real-world process or system over time. Simulation can be used in many contexts, such as simulation of technology for performance optimization or scientific modeling of natural systems or human systems to gain insight into their functioning. In OR context, simulation is often used as part of business optimization to understand impact of changes. Typical OR simulation techniques are System Dynamics, Discrete Event Simulation and Agent-Based Simulation that were invented in various social and economic contexts and have different modeling power.

System Dynamics (SD): Is an approach to understanding the behavior of complex systems over time. Originally created during 1950s by Professor Jay Forrester, it deals with internal feedback loops and time delays that affect the behavior of the entire system. In comparison with other simulation techniques, SD uses feedback loops, stocks and flows as its constructs, which can often be represented as a set of differential equations in math. Initially used to help corporate managers improve understanding of industrial processes, SD is currently being used in social and economics domain for policy analysis.

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