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Co-Organisers:
I-RICE'26
4th International Research, Innovation, Creativity & Engineering Project Competition 2026
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Project ID:
ITCS04303
Optimising Green Affordable Housing: A Genetic Algorithm-Based Decision Support Prototype
Project Title:
Category:
Information Technology/ Computer Science/ AI
Inventors:
Masitah binti Zulkepli, Wu Ziou Hon, Nur Yong Ai Chan binti Abdullah, Noor Fatihah bt Mazlam, Chan Ler Kuan, Calvin Teo Yee Jie, Mohd Shahril bin Abdul Rahman
Institution/Company:
Southern University College
Invention Description/ Abstract:
Green Affordable Housing aims to balance two important goals: keeping housing affordable while improving environmental performance. However, selecting green building features can be challenging because different features involve different costs, sustainability benefits and stakeholder priorities. This invention presents a Genetic Algorithm-Based Decision Support Prototype designed to support the selection of suitable green building features for affordable housing development.
The prototype integrates four key inputs: green building criteria and features, Incremental Cost (IC), Stakeholder Preference Score (SPS), and Sustainability Score (SS). These inputs are processed through a Genetic Algorithm optimisation model to search for suitable combinations of green features within a given budget. Instead of evaluating or ranking features individually, the prototype focuses on identifying an optimised package of features that provides a better balance between affordability, sustainability and stakeholder preference.
The prototype guides users through project setup, applicability checking, must-include feature selection, optimisation and package comparison. It can generate different decision scenarios, such as Market-Oriented, Balanced and Sustainability-Oriented packages, allowing users to compare cost, SPS coverage, SS coverage and composite benefit.
At the current stage, the system is developed as a proof-of-concept prototype using literature-informed provisional data. It demonstrates the feasibility of translating research evidence into a practical decision-support tool. The prototype has potential application for developers, government agencies, consultants, quantity surveyors and other housing stakeholders, and may be further developed into a validated web-based or desktop decision-support system for Green Affordable Housing planning.
Invention Technical Description
The proposed invention is a **Genetic Algorithm-Based Decision Support Prototype** developed to optimise the selection of green building features for Green Affordable Housing. The system is designed around four main input components: **green building features, Incremental Cost (IC), Stakeholder Preference Score (SPS), and Sustainability Score (SS)**.
Each green building feature is represented as a binary decision variable, where **1 indicates that the feature is selected and 0 indicates that it is not selected**. The system also allows users to define project-specific conditions such as available budget, number of dwellings, feature applicability and selected must-include features.
The optimisation engine applies a **Genetic Algorithm (GA)** to search through many possible combinations of green features. The algorithm evaluates candidate combinations through fitness assessment, selection, crossover and mutation processes. The optimisation objective is to maximise the overall benefit derived from stakeholder preference and sustainability performance, while ensuring that the total incremental cost remains within the specified budget constraint.
The prototype generates several decision scenarios, including **Market-Oriented, Balanced and Sustainability-Oriented** packages. Users can compare these scenarios based on total cost, SPS coverage, SS coverage and Composite Benefit Score. The system also provides a visual dashboard showing category coverage and the selected green features within the recommended package.
The current version functions as a **proof-of-concept prototype** using literature-informed provisional data to test the optimisation logic, interface flow and output generation. The prototype is intended to demonstrate the technical feasibility of integrating cost, sustainability and stakeholder preference within one decision-support platform. Future development will replace provisional inputs with empirical stakeholder and cost data, followed by scenario testing, expert validation and system refinement.
Demostration/ Presentation Video
Poster/ Broucher/ Invention Photo
Additional Documents
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