{"id":18685212,"url":"https://github.com/jcguidry/nonlinear-optimization-pyomo-py","last_synced_at":"2026-04-29T00:05:10.934Z","repository":{"id":235472359,"uuid":"694286023","full_name":"jcguidry/nonlinear-optimization-pyomo-py","owner":"jcguidry","description":"Multi-objective optimization to maximize business outcomes with python and pyomo ","archived":false,"fork":false,"pushed_at":"2023-09-20T17:34:46.000Z","size":104,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-28T00:24:56.372Z","etag":null,"topics":["optimization","pyomo","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jcguidry.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-09-20T17:34:45.000Z","updated_at":"2023-09-20T17:37:42.000Z","dependencies_parsed_at":null,"dependency_job_id":"ff263905-8be5-4f8c-a9bf-42037b31ff4e","html_url":"https://github.com/jcguidry/nonlinear-optimization-pyomo-py","commit_stats":null,"previous_names":["jcguidry/nonlinear-optimization-pyomo-py"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcguidry%2Fnonlinear-optimization-pyomo-py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcguidry%2Fnonlinear-optimization-pyomo-py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcguidry%2Fnonlinear-optimization-pyomo-py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcguidry%2Fnonlinear-optimization-pyomo-py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jcguidry","download_url":"https://codeload.github.com/jcguidry/nonlinear-optimization-pyomo-py/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239540680,"owners_count":19656057,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["optimization","pyomo","python"],"created_at":"2024-11-07T10:21:15.417Z","updated_at":"2025-11-07T22:30:26.793Z","avatar_url":"https://github.com/jcguidry.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./assets/multi-obj-opt-viz.png\" /\u003e\n\u003c/p\u003e\n\n# Pyomo Optimization Problem Set, Spring 2022\n\n- Collin Guidry\n\n# Overview\n\nThis repository contains code and discussion related to multi-objective optimization problems using Pyomo and IPOPT solver. Specifically, it tackles two real-world cases:\n\n1. Profit vs. Units Sold: A startup company wants to maximize its profit and the number of units sold through effective advertisement spending.\n2. Hiland Appliance Stocking: Hiland has to decide the optimal number of TVs and Blu-ray disc players to stock under budget and storage constraints, aiming to maximize profit.\n\n## Table of Contents\n\n- [Question 1: Profit vs Units Sold](#question-1-profit-vs-units-sold)\n  - [Part (a) Problem Formulation](#part-a-problem-formulation)\n  - [Part (b) Determine extreme points](#part-b-determine-extreme-points)\n  - [Part (c) Pareto-optimal points](#part-c-pareto-optimal-points)\n  - [Part (d) Discussion](#part-d-discussion)\n- [Question 2](#question-2)\n  - [Part (a) Problem Formulation](#part-a-problem-formulation-1)\n  - [Part (b) Pareto-optimal points](#part-b-pareto-optimal-points-1)\n\n## Question 1: Profit vs Units Sold\n\nA startup company needs to increase its sales of a product through advertisements. The company aims to find a balance between maximizing profit and maximizing the number of units sold.\n\n### Part (a) Problem Formulation\n\n1. **Decision Variables**: \\( a = \\) advertising investment in thousands of dollars.\n2. **Objective Function**: Maximize \\( f_1(a) = 890-3.8a+20\\sqrt{a} \\) and \\( f_2(a) = -1.444a^2 + 7.6\\sqrt{a}*a + 80\\sqrt{a} + 322a +3560 \\)\n3. **Constraints**: \\( a \u003e 0 \\)\n\nThe code implements this problem using Pyomo and solves it using the IPOPT solver.\n\n### Part (b) Determine extreme points\n\nThe code first deactivates one objective and solves for the other. This provides the limits for each objective.\n\n### Part (c) Pareto-optimal points\n\nThe code finds 10 Pareto-optimal points by varying the weights given to the objectives. A Pareto frontier is plotted using matplotlib.\n\n### Part (d) Discussion\n\nThe company should repeat this weighed sum method, but **maximize sales before profit** to better understand the trade-offs between the objectives.\n\n---\n\n## Question 2\n\nHiland Appliance must determine how many TVs and Blu-Ray disc players to stock, considering budget, profit, and storage.\n\n### Part (a) Problem Formulation\n\n- **Decision Variables**: \\( x_1 \\) = Number of TVs, \\( x_2 \\) = Number of Blu-Ray disc players\n- **Objective Function**: \\( f_1 = 150x_1 + 50x_2 \\) (Profit), \\( f_2 = 3x_1 + x_2 \\) (Storage)\n- **Constraints**: \\( 1000x_1 + 200x_2 \\leq 60000 \\), \\( x_1, x_2 \\geq 0 \\)\n\n### Part (b) Pareto-optimal points\n\nThe code first maximizes profit subject to constraints, then minimizes space while maintaining that level of profit. This is a form of lexicographic optimization, which is consistent with the company's priorities.\n\nThe Pareto optimal point will maximize the profit while minimizing the storage space, respecting the budget constraint.\n\n## Installation and Dependencies\n\n* Python 3.6+\n* Pyomo\n* IPOPT solver\n* Matplotlib\n* Pandas\n* NumPy\n\n## How to Run\n\n1. Clone this repository.\n2. Install all dependencies.\n3. Open the notebook and run all cells.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcguidry%2Fnonlinear-optimization-pyomo-py","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcguidry%2Fnonlinear-optimization-pyomo-py","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcguidry%2Fnonlinear-optimization-pyomo-py/lists"}