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Preparing the Optimization Problem in HeuristicLab 3.3 You can now start the HeuristicLabOptimizationExperiement, although of course it won't do anything but block until it receives parameters. This sends the mean quality of the (possible) replications back to HeuristicLab, resets the variables and receives a new message with which to perform the next simulation runs.
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The following code samples need to be entered into the respective boxes.ĬndQuality(currentSolution, quality / replications) Next we need to go to the "Advanced" section of the properties and configure the experiment loop: perform the parameter retrieval, run the simulation, send the quality back, reset the model and continue with another attempt of retrieving parameters. Now in the upper right hand corner of this property page is a button called "Create default UI", click that now and you have some user interface elements added to this experiment. So instead of the first number 20 you type sLoRetailer, then you replace the 80 with SHiRetailer, then the next 20 you replace with sLoWholesaler, then SHiWholesaler, then sLoFactory, and finally SHiFactory. Then type in each field in the column "Expression" the same word as you see on the general property page of the Simulation experiment. In the "Properties" view of this experiment go to the "General" tab and choose "Freeform" instead of "Varied in range" and put a large number in the box asking for the "Number of runs", e.g.: 1,000,000,000. Now we have created a new experiment with which we can optimize the parameters. Leave everything else as it is and click "Finish". The best type of experiment for this task is a "Parameters Variation" experiment, so choose this and choose a name for it, e.g. Now we need to add a new experiment which we can run to serve as our evaluation function. Choose the file you just downloaded, make sure "Import to model folder is ticked" and click "Finish" to add the library. In the properties window of this model in the section "Jar files and class folders required to build the model:" click the add button.

Then switch to AnyLogic 6 again and click the model icon in the project view (should be called "Supply Chain" if you followed the model name suggestion above).
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your Downloads directory you should not download it into the model folder already). Now download the H元 External Evaluation Java library to a place that you remember (e.g. That way it will save it in a folder called "Models" in your user's directory. For the sake of simplicity just put "Supply Chain" in the box that asks for a model name and leave everything else. Preparing the Simulation Model in AnyLogic 6įirst save the model in a new location. In total we have 6 decision variables, one target variable and one constraint on the output. Each echelon in the chain uses an (s,S) ordering policy. The optimization goal is to minimize the sum of all costs in the supply chain while satisfying a certain service level for the end user. There is also an Optimization Experiment in this model which uses the proprietary optimization package OptQuest. After you opened the model you can try to run the Simulation.

You can open it either on the Welcome page after starting AnyLogic 6 or by choosing "Sample Models" from the Help menu. The model which we are going to optimize with HeuristicLab comes preinstalled with AnyLogic 6 and is called Supply Chain.

Anylogic tutorial text dest how to#
If you go through this tutorial you will hopefully get an idea on how to solve your specific problem and implement the connector in your model. This howto is written as a tutorial by taking one of the sample models that ships with AnyLogic and add connectors to optimize its parameters with HeuristicLab algorithms. We assume the reader is familiar with xj technologies AnyLogic 6 and has basic understanding of the Java programming language. If you prefer a slide version of this tutorial with images, please download the PDF tutorial of optimizing external applications and follow the instructions in Demonstration Part I therein. optimize parameters of AnyLogic 6 simulation models
