Simulation-based optimization: Difference between revisions

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Revision as of 21:26, 12 September 2020

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Specific simulation–based optimization methods can be chosen according to figure 1 based on the decision variable types.<ref>Jalali, Hamed, and Inneke Van Nieuwenhuyse. "[https://core.ac.uk/download/pdf/34623919.pdf Simulation optimization in inventory replenishment: a classification]." IIE Transactions 47.11 (2015): 1217-1235.</ref>

[[File:Slide1 1.jpg|thumb|Fig.1 Classification of simulation based optimization according to variable types]]

[[Optimization (computer science)|Optimization]] exists in two main branches of operationaloperations research:

''Optimization [[Parametric programming|parametric]] (static)'' – The objective is to find the values of the parameters, which are “static” for all states, with the goal of maximizing or minimizing a function. In this case, one can use [[mathematical programming]], such as [[linear programming]]. In this scenario, simulation helps when the parameters contain noise or the evaluation of the problem would demand excessive computer time, due to its complexity.<ref name=":0" />

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== Simulation-based optimization methods ==

The main approaches in simulation optimization are discussed below.

<ref name=Fu>{{cite book|last=Fu|first=Michael, editor|title=Handbook of Simulation Optimization|publisher=Springer|year=2015|url=https://www.springer.com/us/book/9781493913831}}</ref>

TheSome mainimportant approaches in simulation optimization are discussed below.

<ref name=Fu>{{cite book|last=Fu|first=Michael, editor|title=Handbook of Simulation Optimization|publisher=Springer|year=2015|url=https://www.springer.com/us/book/9781493913831}}</ref>

<ref>Spall, J.C. (2003). ''Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control''. Hoboken: Wiley.</ref>

=== Statistical ranking and selection methods (R/S) ===

Ranking and selection methods are designed for problems where the alternatives are fixed and known, and simulation is used to estimate the system performance.

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The limitations of derivative-free optimization:

1. ItSome usuallymethods cannot handle optimization problems with more than a few tens of variables; the results via this method are usually not so accurate. However, there are numerous practical cases where derivative-free methods have been successful in non-trivial simulation optimization problems that include randomness manifesting as "noise" in the objective function. See, for example, the following

<ref name=Fu>{{cite book|last=Fu|first=Michael, editor|title=Handbook of Simulation Optimization|publisher=Springer|year=2015|url=https://www.springer.com/us/book/9781493913831}}</ref>

<ref>Fu, M.C., Hill, D. Optimization of discrete event systems via simultaneous perturbation stochastic approximation. ''IIE Transactions'' 29, 233–243 (1997). https://doi.org/10.1023/A:1018523313043 </ref>.

2. When confronted with minimizing non-convex functions, it will show its limitation.

3. Derivative-free optimization methods are relatively simple and easy;, howeverbut, theylike aremost notoptimization somethods, goodsome care is required in theorypractical andimplementation (e.g., in practicechoosing the algorithm parameters).

=== Dynamic programming and neuro-dynamic programming ===