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Merge pull request #2392 from oracle-devrel/operational-research
initial commit for operational research demos
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‎data-platform/data-science/oracle-data-science/operational-research/LICENSE‎

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

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

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

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‎data-platform/data-science/oracle-data-science/operational-research/files/flight_planning.ipynb‎

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

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

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