A Distributed Retail Beer Game for Decision Support System

AbstractA beer game is a simulation tool for the study of Supply Chain Management (SCM) issues used by the students of MIT. It has been augmented over the time to make it industry ready for decision making and risk management. Apart from smooth information and material flow among the distributed partners excess inventory is still an issue to control. In this paper, an attempt is made to improvise the Beer Game model to a Petri Net model for risk analysis and decision making. A successful simulation of the Petri Net model on efficient redistribution of stock towards inventory management is presented in this paper. The paper also establishes that the analysis is done in polynomial time

Advanced Computing and Systems for Security - Volume Six

This book contains extended version of selected works that have been discussed and presented in the fourth International Doctoral Symposium on Applied Computation and Security Systems (ACSS 2017) held in Patna, India during March 17-19, 2017

Sentence Embedding Models for Similarity Detection of Software Requirements

Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sentence embedding models trained on a natural language requirements dataset in order to derive sentence embeddings specific to the software requirements engineering domain. We use cosine-similarity measures in both these models. The result of the experimental evaluation confirm that the proposed models enhance the performance of textual semantic similarity measures over existing state-of-the-art neural sentence embedding models: we reach an accuracy of 88.35%—which improves by about 10% on existing benchmarks.Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sentence embedding models trained on a natural language requirements dataset in order to derive sentence embeddings specific to the software requirements engineering domain. We use cosine-similarity measures in both these models. The result of the experimental evaluation confirm that the proposed models enhance the performance of textual semantic similarity measures over existing state-of-the-art neural sentence embedding models: we reach an accuracy of 88.35%—which improves by about 10% on existing benchmarks

Advanced Computing and Systems for Security - Volume Six

This book contains extended version of selected works that have been discussed and presented in the fourth International Doctoral Symposium on Applied Computation and Security Systems (ACSS 2017) held in Patna, India during March 17-19, 2017