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Requirements engineering is the initial process of software development that critically determines the overall software process quality. However, this process is error-prone. This is generally related to the factors of communication, knowledge, and documentation. With the lack of business knowledge, it is complicated for (technical) engineers to define customer needs. Also, modeling and documenting requirements need much time and effort to ensure that the requirements are valid, and nothing is missed. The current approach of the requirements modeling process mostly focuses on the Unified Modeling Notation (UML) use case, that not provide enough information for a stakeholder to define system requirements. The generated SRS is still lack of detail development guideline that increase risk of development error. The purpose of this research is to guide for the elicitation process to avoid missing and mismatch requirements, and to make the modeling and documentation process more effective and efficient. We propose an ontology framework for generating requirements specification. This framework, namely the Rule-Based Ontology Framework (ROF) consists of two main processes: First, requirements elicitation. This step provides a guideline for the stakeholder to define system requirements based on the problem of the current system and business process enhancement. Based on this final requirement list, the requirements ontology is generated. Second, the auto-generation of the requirements specification document. The document consists of semi-formal modeling and natural language. In this research, we use Business Process Modeling Notation (BPMN) is a modeling language. For natural language documents, we use IEEE for the SRS template.

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This paper takes a practical case study approach to demonstrate the genetic algorithm (GA)'s ability to help purchasing manager in making a better decision on procurement of products or materials. The GA implemented in the supplier selection function aims to allow purchasing managers to get better decisions in choosing the appropriate suppliers by choosing the appropriate products under various contextual situations. By allowing the purchasing managers to set their criteria based on priority helps the company to choose good product with best price and best quality, thus decreases procurement budget while increases company reputation. Information regarding evaluation criteria and data for our experiments are obtained through a local company which provides automobile service and repairs. Results generated from experiments based on various scenarios by prioritizing different evaluation attributes have demonstrated the GA's ability in choosing the "fittest" solution.

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