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docs/index.html

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@@ -988,6 +988,156 @@ <h2>OPL &ndash; Optimisation problem library</h2>
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<td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf" target="_blank">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
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<td><a href="https://zenodo.org/records/8307853" target="_blank">https://zenodo.org/records/8307853</a></td>
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</tr>
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<tr>
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<td>MECHBench</td>
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<td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
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<td>Problem Suite</td>
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<td>1</td>
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<td>scalable'</td>
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<td>Continuous</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
1001+
<td>Not Present</td>
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<td>Real-World Application</td>
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<td><a href="https://arxiv.org/abs/2511.10821" target="_blank">https://arxiv.org/abs/2511.10821</a></td>
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<td><a href="https://github.com/BayesOptApp/MECHBench" target="_blank">https://github.com/BayesOptApp/MECHBench</a></td>
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</tr>
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<tr>
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<td>EXPObench</td>
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<td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
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<td>Problem Suite</td>
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<td>1</td>
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<td>10 to 135</td>
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<td>Continuous, Integer, Categorical, Conditional</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Real-World Application</td>
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<td><a href="https://doi.org/10.1016/j.asoc.2023.110744" target="_blank">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
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<td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark" target="_blank">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
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</tr>
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<tr>
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<td>Gasoline direct injection engine design</td>
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<td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
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<td>Single Problem</td>
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<td>2</td>
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<td>7</td>
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<td>Continuous, Ordinal</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Present</td>
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<td>Real-World Application</td>
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<td></td>
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<td><a href="https://doi.org/10.1016/j.ejor.2022.08.032" target="_blank">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
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</tr>
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<tr>
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<td>BEACON</td>
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<td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
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<td>Generator</td>
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<td>2</td>
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<td>scalable</td>
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<td>Continuous</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Artificially Generated</td>
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<td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303" target="_blank">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
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<td><a href="https://github.com/Stebbet/BEACON/" target="_blank">https://github.com/Stebbet/BEACON/</a></td>
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</tr>
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<tr>
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<td>TulipaEnergy</td>
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<td>Determine the optimal investment and operation decisions for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
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<td>Problem Suite</td>
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<td>1</td>
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<td>scalable</td>
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<td>Continuous</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Present</td>
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<td>Present</td>
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<td>Real-World Application</td>
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<td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references" target="_blank">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
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<td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/" target="_blank">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
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</tr>
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<tr>
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<td>ATO</td>
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<td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
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<td>Single Problem</td>
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<td>2</td>
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<td>10</td>
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<td>Continuous</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Real-World Application</td>
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<td></td>
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<td>-</td>
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</tr>
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<tr>
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<td>Brachytherapy treatment planning</td>
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<td>Treatment planning for internal radiation therapy</td>
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<td>Problem Suite</td>
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<td>2-3</td>
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<td>100-500</td>
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<td>Continuous</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Present</td>
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<td>Real-World Application</td>
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<td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781" target="_blank">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
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<td></td>
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</tr>
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<tr>
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<td>FleetOpt</td>
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<td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: https://dl.acm.org/doi/abs/10.1145/3638530.3664137</td>
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<td>Single Problem</td>
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<td>1</td>
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<td>Upper level: 54; lower level: 13208</td>
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<td>Integer</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Real-World Application</td>
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<td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137" target="_blank">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
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<td>Not public: was done for real client with their private data</td>
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</tr>
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<tr>
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<td>Building spatial design</td>
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<td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
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<td>Single Problem</td>
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<td>2</td>
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<td>scalable depending on problem size (e.g. 90 for)</td>
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<td>Continuous, Boolean</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Real-World Application</td>
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<td><a href="https://hdl.handle.net/1887/81789" target="_blank">https://hdl.handle.net/1887/81789</a></td>
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<td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox" target="_blank">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
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</tr>
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<tr>
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<td>Electric Motor Design Optimization</td>
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<td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
1129+
<td>Single Problem</td>
1130+
<td>1</td>
1131+
<td>13</td>
1132+
<td>Continuous, Integer</td>
1133+
<td>Present</td>
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<td>Not Present</td>
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<td>Present</td>
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<td>Not Present</td>
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<td>Real-World Application</td>
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<td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)" target="_blank">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)</a></td>
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<td>Implementation not freely available</td>
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</tr>
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</tbody>
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<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
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docs/problems.html

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<td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf" target="_blank">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
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<td><a href="https://zenodo.org/records/8307853" target="_blank">https://zenodo.org/records/8307853</a></td>
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</tr>
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<tr>
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<td>MECHBench</td>
967+
<td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
968+
<td>Problem Suite</td>
969+
<td>1</td>
970+
<td>scalable'</td>
971+
<td>Continuous</td>
972+
<td>Present</td>
973+
<td>Not Present</td>
974+
<td>Not Present</td>
975+
<td>Not Present</td>
976+
<td>Real-World Application</td>
977+
<td><a href="https://arxiv.org/abs/2511.10821" target="_blank">https://arxiv.org/abs/2511.10821</a></td>
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<td><a href="https://github.com/BayesOptApp/MECHBench" target="_blank">https://github.com/BayesOptApp/MECHBench</a></td>
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</tr>
980+
<tr>
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<td>EXPObench</td>
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<td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
983+
<td>Problem Suite</td>
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<td>1</td>
985+
<td>10 to 135</td>
986+
<td>Continuous, Integer, Categorical, Conditional</td>
987+
<td>Present</td>
988+
<td>Not Present</td>
989+
<td>Present</td>
990+
<td>Not Present</td>
991+
<td>Real-World Application</td>
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<td><a href="https://doi.org/10.1016/j.asoc.2023.110744" target="_blank">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
993+
<td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark" target="_blank">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
994+
</tr>
995+
<tr>
996+
<td>Gasoline direct injection engine design</td>
997+
<td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
998+
<td>Single Problem</td>
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<td>2</td>
1000+
<td>7</td>
1001+
<td>Continuous, Ordinal</td>
1002+
<td>Present</td>
1003+
<td>Not Present</td>
1004+
<td>Not Present</td>
1005+
<td>Present</td>
1006+
<td>Real-World Application</td>
1007+
<td></td>
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<td><a href="https://doi.org/10.1016/j.ejor.2022.08.032" target="_blank">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
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</tr>
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<tr>
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<td>BEACON</td>
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<td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
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<td>Generator</td>
1014+
<td>2</td>
1015+
<td>scalable</td>
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<td>Continuous</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Not Present</td>
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<td>Artificially Generated</td>
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<td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303" target="_blank">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
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<td><a href="https://github.com/Stebbet/BEACON/" target="_blank">https://github.com/Stebbet/BEACON/</a></td>
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</tr>
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<tr>
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<td>TulipaEnergy</td>
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<td>Determine the optimal investment and operation decisions for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
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<td>Problem Suite</td>
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<td>1</td>
1030+
<td>scalable</td>
1031+
<td>Continuous</td>
1032+
<td>Present</td>
1033+
<td>Not Present</td>
1034+
<td>Present</td>
1035+
<td>Present</td>
1036+
<td>Real-World Application</td>
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<td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references" target="_blank">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
1038+
<td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/" target="_blank">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
1039+
</tr>
1040+
<tr>
1041+
<td>ATO</td>
1042+
<td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
1043+
<td>Single Problem</td>
1044+
<td>2</td>
1045+
<td>10</td>
1046+
<td>Continuous</td>
1047+
<td>Not Present</td>
1048+
<td>Not Present</td>
1049+
<td>Not Present</td>
1050+
<td>Not Present</td>
1051+
<td>Real-World Application</td>
1052+
<td></td>
1053+
<td>-</td>
1054+
</tr>
1055+
<tr>
1056+
<td>Brachytherapy treatment planning</td>
1057+
<td>Treatment planning for internal radiation therapy</td>
1058+
<td>Problem Suite</td>
1059+
<td>2-3</td>
1060+
<td>100-500</td>
1061+
<td>Continuous</td>
1062+
<td>Present</td>
1063+
<td>Not Present</td>
1064+
<td>Not Present</td>
1065+
<td>Present</td>
1066+
<td>Real-World Application</td>
1067+
<td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781" target="_blank">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
1068+
<td></td>
1069+
</tr>
1070+
<tr>
1071+
<td>FleetOpt</td>
1072+
<td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: https://dl.acm.org/doi/abs/10.1145/3638530.3664137</td>
1073+
<td>Single Problem</td>
1074+
<td>1</td>
1075+
<td>Upper level: 54; lower level: 13208</td>
1076+
<td>Integer</td>
1077+
<td>Present</td>
1078+
<td>Not Present</td>
1079+
<td>Not Present</td>
1080+
<td>Not Present</td>
1081+
<td>Real-World Application</td>
1082+
<td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137" target="_blank">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
1083+
<td>Not public: was done for real client with their private data</td>
1084+
</tr>
1085+
<tr>
1086+
<td>Building spatial design</td>
1087+
<td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
1088+
<td>Single Problem</td>
1089+
<td>2</td>
1090+
<td>scalable depending on problem size (e.g. 90 for)</td>
1091+
<td>Continuous, Boolean</td>
1092+
<td>Present</td>
1093+
<td>Not Present</td>
1094+
<td>Not Present</td>
1095+
<td>Not Present</td>
1096+
<td>Real-World Application</td>
1097+
<td><a href="https://hdl.handle.net/1887/81789" target="_blank">https://hdl.handle.net/1887/81789</a></td>
1098+
<td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox" target="_blank">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
1099+
</tr>
1100+
<tr>
1101+
<td>Electric Motor Design Optimization</td>
1102+
<td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
1103+
<td>Single Problem</td>
1104+
<td>1</td>
1105+
<td>13</td>
1106+
<td>Continuous, Integer</td>
1107+
<td>Present</td>
1108+
<td>Not Present</td>
1109+
<td>Present</td>
1110+
<td>Not Present</td>
1111+
<td>Real-World Application</td>
1112+
<td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)" target="_blank">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)</a></td>
1113+
<td>Implementation not freely available</td>
1114+
</tr>
9651115
</tbody>
9661116
<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>

problems.yaml

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textual description: Determine the optimal investment and operation decisions for
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different types of assets in the energy system (production, consumption, conversion,
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storage, and transport), while minimizing loss of load.
1067-
reference: See https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references
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reference: https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references
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other info:
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name: null
10701070
partial evaluations: Unknown
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11741174
can be covered. Moving a vehicle from one base to another to help cover trips
11751175
is OK as long as the original base can still cover its trips. Link to paper with
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more details: https://dl.acm.org/doi/abs/10.1145/3638530.3664137'
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reference: ''
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reference: 'https://dl.acm.org/doi/abs/10.1145/3638530.3664137'
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other info:
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name: null
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partial evaluations: Present

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