initial commit for operational research demos

data-platform/data-science/oracle-data-science/operational-research/LICENSE

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+Copyright (c) 2026 Oracle and/or its affiliates.
+
+The Universal Permissive License (UPL), Version 1.0
+
+Subject to the condition set forth below, permission is hereby granted to any
+person obtaining a copy of this software, associated documentation and/or data
+(collectively the "Software"), free of charge and under any and all copyright
+rights in the Software, and any and all patent rights owned or freely
+licensable by each licensor hereunder covering either (i) the unmodified
+Software as contributed to or provided by such licensor, or (ii) the Larger
+Works (as defined below), to deal in both
+
+(a) the Software, and
+(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+one is included with the Software (each a "Larger Work" to which the Software
+is contributed by such licensors),
+
+without restriction, including without limitation the rights to copy, create
+derivative works of, display, perform, and distribute the Software and make,
+use, sell, offer for sale, import, export, have made, and have sold the
+Software and the Larger Work(s), and to sublicense the foregoing rights on
+either these or other terms.
+
+This license is subject to the following condition:
+The above copyright notice and either this complete permission notice or at
+a minimum a reference to the UPL must be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

data-platform/data-science/oracle-data-science/operational-research/README.md

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+# Overview
+
+This project presents three operational research use cases with different levels of complexity:
+
+1. **Finding the best route using Dijkstra’s algorithm** – the simplest use case.
+2. **Flight scheduling using integer linear programming.**
+3. **Individual pricing optimization with global and individual constraints** – an advanced use case that also showcases the use of different resources and features in the OCI Data Science Platform.
+
+Reviewed: 2026.01.05
+
+# What You’ll Learn
+
+This project covers the following topics:
+
+1. Background on optimization tools used in operational research, including linear programming, the Brent method, and root-finding techniques for computing Lagrange multipliers.
+2. Background on the different use cases, with a focus on the individual pricing optimization use case.
+3. OCI Data Science Platform components used in this project, including:
+   - Notebook Sessions
+   - Model serialization
+   - Model Catalog
+   - Model deployment
+   - Endpoint invocation for predictions
+   - Data Science Jobs
+
+# Prerequisites
+
+- Access to the OCI Data Science Platform
+- Basic familiarity with Python and machine learning concepts
+- A valid OCI compartment, resource principal, and policies configured for Data Science services
+
+# How to Use this asset?
+
+1. Open the provided notebook in your OCI Data Science Notebook Session.
+2. Select the following conda environment: generalml_p311_cpu_x86_64_v1
+3. Run the notebook cells in sequence to reproduce the complete workflow.
+
+# License
+
+Copyright (c) 2026 Oracle and/or its affiliates.
+
+Licensed under the Universal Permissive License (UPL), Version 1.0.
+
+See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/main/LICENSE) for more details.

data-platform/data-science/oracle-data-science/operational-research/files/individual_optimization/data_simulation.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "33545924-d88b-4b8b-9a71-a0f229b58759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c76dba70-77fb-4ea8-a82f-ef7f44b23400",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulate_training_data(size):\n",
+    "    age = np.random.uniform(20, 70, size)\n",
+    "    risk = np.random.uniform(0, 1, size)\n",
+    "    price = np.random.uniform(2, 4,size)\n",
+    "\n",
+    "    beta_0 = 3.0\n",
+    "    beta_price = -1.5\n",
+    "    beta_age = 0.05\n",
+    "    beta_risk = -1.0\n",
+    "    \n",
+    "    logit = (\n",
+    "        beta_0\n",
+    "        + beta_price * price\n",
+    "        + beta_age * age\n",
+    "        + beta_risk * risk\n",
+    "    )\n",
+    "\n",
+    "    prob = 1 / (1 + np.exp(-logit))\n",
+    "    purchase = np.random.binomial(1, prob)\n",
+    "\n",
+    "\n",
+    "    df = pd.DataFrame({\n",
+    "        \"price\": price,\n",
+    "        \"age\": age,\n",
+    "        \"risk\": risk,\n",
+    "        \"purchase\": purchase\n",
+    "    })\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5da7f09a-04ad-4ac6-b648-2109bf476285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=new_customer_data=simulate_training_data(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "036a12b9-256f-46ca-8909-f886f95349b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"./data/price_opt_data_1000.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "806cb91d-d16d-44fb-80f0-b77c647ff0a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulate_new_cases(size):\n",
+    "    age = np.random.uniform(20, 70, size)\n",
+    "    risk = np.random.uniform(0, 1, size)\n",
+    "\n",
+    "    df = pd.DataFrame({\n",
+    "        \"age\": age,\n",
+    "        \"risk\": risk,\n",
+    "    })\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4fdf26b0-edc0-404d-a3cb-027eeb615d19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_customer_data=simulate_new_cases(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5b2851e2-87cc-4e5f-9a79-76d73e31051f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_customer_data.to_csv(\"./data/new_customers_50.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0a0579ff-02a4-40c4-b1e3-649eeb39da3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<oci.response.Response at 0x7f4665b62d50>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import oci\n",
+    "from oci.object_storage import UploadManager\n",
+    "\n",
+    "signer = oci.auth.signers.get_resource_principals_signer()\n",
+    "object_storage = oci.object_storage.ObjectStorageClient({}, signer=signer)\n",
+    "namespace = object_storage.get_namespace().data\n",
+    "\n",
+    "bucket_name = \"filesdemo\"\n",
+    "file_name = \"operational_research/new_cases.csv\"\n",
+    "\n",
+    "local_path='./data/new_customers_50.csv'\n",
+    "\n",
+    "upload_manager = UploadManager(object_storage, allow_parallel_uploads=True)\n",
+    "upload_manager.upload_file(\n",
+    "    namespace_name=namespace,\n",
+    "    bucket_name=bucket_name,\n",
+    "    object_name=file_name,\n",
+    "    file_path=local_path\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "923a05a3-88b3-4568-b7c2-508160b08189",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:generalml_p311_cpu_x86_64_v1]",
+   "language": "python",
+   "name": "conda-env-generalml_p311_cpu_x86_64_v1-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}