scholarly journals STOCHASTIC OPTIMIZATION OF HEURISTIC METHOD RULE TO DETERMINE ASSET ALLOCATION TO RETIREMENT PORTFOLIO / STOCHASTINIS EURISTINIO METODO TAISYKLĖS PENSIJOS PORTFELIO SUDĖČIAI NUSTATYTI OPTIMIZAVIMAS

2011 ◽  
Vol 12 (1) ◽  
pp. 92-98
Author(s):  
Aušra Klimavičienė
Keyword(s):  
Stochastic Optimization ◽  
Stochastic Simulation ◽  
Asset Allocation ◽  
Heuristic Method ◽  
Simulation Models ◽  
Optimization Techniques ◽  
Human Lifespan ◽  
Stochastic Returns ◽  
Dynamic Stochastic ◽  
Population Mortality

The article examines the problem of determining asset allocation to sustainable retirement portfolio. The article attempts to apply heuristic method – 100 minus age in stocks rule – to determine asset allocation to sustainable retirement portfolio. Using dynamic stochastic simulation and stochastic optimization techniques the optimization of heuristic method rule is presented and the optimal alternative to „100“ is found. Seeking to reflect the stochastic nature of stock and bond returns and the human lifespan, the dynamic stochastic simulation models incorporate both the stochastic returns and the probability of living another year based on Lithuania‘s population mortality tables. The article presents the new method – adjusted heuristic method – to be used to determine asset allocation to retirement portfolio and highlights its advantages.

scholarly journals Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model

animal ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 145-154 ◽  
Author(s):  
P.F. Mostert ◽  
E.A.M. Bokkers ◽  
C.E. van Middelaar ◽  
H. Hogeveen ◽  
I.J.M. de Boer
Keyword(s):  
Dairy Cattle ◽  
Simulation Model ◽  
Economic Impact ◽  
Stochastic Simulation ◽  
Subclinical Ketosis ◽  
Dynamic Stochastic ◽  
Stochastic Simulation Model

Dynamic stochastic optimization

2014 ◽  
Author(s):  
Craig Wilson ◽  
Venugopal Veeravalli ◽  
Angelia Nedic
Keyword(s):  
Stochastic Optimization ◽  
Dynamic Stochastic

scholarly journals A New Heuristic Method for Transportation Network and Land Use Problem

2016 ◽  
Vol 8 (3) ◽  
pp. 94 ◽  
Author(s):  
Mouhamadou A.M.T. Bald ◽  
Babacar M. Ndiaye
Keyword(s):  
Land Use ◽  
Transportation Network ◽  
Heuristic Method ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Mixed Integer ◽  
Individual Agent ◽  
The Road ◽  
Agent Simulation ◽  
Network Costs

Our paper deals with the Transportation Network and Land Use (TNLU) problem.  It consists in finding, simultaneously, the best location of urban area activities, as well as of the road network design that may minimize the moving cost in the network, and the network costs. We propose a new mixed integer programming formulation of the problem, and a new heuristic method for the resolution of TNLU. Then, we give a methodology to find locations or relocations of some Dakar region amenities (home, shop, work and leisure places), that may reduce travel time or travel distance. The proposed methodology mixes multi-agent simulation with combinatorial optimization techniques; that is individual agent strategies versus global optimization using Geographical Information System. Numerical results which show the effectiveness of the method,  and simulations based on the scenario of Dakar city are given.

Intelligent Time-Stepping for Practical Numerical Simulation

2021 ◽  
Author(s):  
Soham Sheth ◽  
Francois McKee ◽  
Kieran Neylon ◽  
Ghazala Fazil
Keyword(s):  
Heuristic Method ◽  
Simulation Models ◽  
Model Complexity ◽  
Large Field ◽  
Optimal Time ◽  
Time Step ◽  
Non Linear ◽  
Set Up ◽  
Reservoir Simulator ◽  
Step Selection

Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation faster. An optimal time-step choice avoids wasted non-linear and linear equation set-up work when the time-step is too small and avoids highly non-linear systems that take many iterations to solve. Typical time-step selectors use a limited set of features to heuristically predict the size of the next time-step. While they have been effective for simple simulation models, as model complexity increases, there is an increasing need for robust data-driven time-step selection algorithms. We propose two workflows – static and dynamic – that use a diverse set of physical (e.g., well data) and mathematical (e.g., CFL) features to build a predictive ML model. This can be pre-trained or dynamically trained to generate an inference model. The trained model can also be reinforced as new data becomes available and efficiently used for transfer learning. We present the application of these workflows in a commercial reservoir simulator using distinct types of simulation model including black oil, compositional and thermal steam-assisted gravity drainage (SAGD). We have found that history-match and uncertainty/optimization studies benefit most from the static approach while the dynamic approach produces optimum step-sizes for prediction studies. We use a confidence monitor to manage the ML time-step selector at runtime. If the confidence level falls below a threshold, we switch to traditional heuristic method for that time-step. This avoids any degradation in the performance when the model features are outside the training space. Application to several complex cases, including a large field study, shows a significant speedup for single simulations and even better results for multiple simulations. We demonstrate that any simulation can take advantage of the stored state of the trained model and even augment it when new situations are encountered, so the system becomes more effective as it is exposed to more data.

An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

Sign in / Sign up

Export Citation Format

Share Document