The aim is to design a comprehensive shelf space optimization framework that maximizes profitability, enhances sales forecasting, improves efficiency of inventory management, and supports effective decision-making in retail businesses. A robust and interactive analytical dashboard is developed that allows users to visualize critical sales metrics, analyze historical data trends, and accurately forecast product demand and supply requirements based on seasonal variations and sales performance. The work integrates three mathematical optimization paradigms like Linear Programming (LP), metaheuristic search via Genetic Algorithms (GA), and reinforcement learning using Proximal Policy Optimization (PPO) to support both static and adaptive allocation strategies. Experimental validation highlights the relative advantages of each method, with detailed evaluations based on forecast accuracy, inventory turnover efficiency, shelf utilization rate, and overall improvement in profitability. Unlike traditional static optimization models, the PPO-based framework continuously adapts allocation decisions using environmental feedback, improving flexibility in dynamic retail scenarios The paper uses multi-objective shelf optimization considering profitability, utilization, and customer demand simultaneously. The results demonstrate that the integration of predictive analytics and advanced optimization techniques significantly performs traditional shelf management approaches, offering retailers actionable insights and operational advantages.

The work highlights exploring the usage of blockchain technology for enhancing traceability in agricultural supply chain management. The aim is to develop a secure and transparent system, which improves the easy tracking and tracing of agricultural products from the point of origin until it reaches the end consumer. Currently, Blockchain is a technology, which provides security in various fields of transactions. The work utilizes to improve supply chain efficiency, increase transparency and accountability, and enhance consumer trust in the agricultural products. The system will utilize smart contracts to automate processes and ensure compliance with regulations and standards, which improves supply chain efficiency. Smart contracts enable agreement between two parties present in the supply chain. Further, the financial transactions can be improved with the help of block chain. Additional, the work will also provide recommendations for companies and organizations looking to implement blockchain-based results in their supply chain management. The work implements an application using ganache, solidity and truffle. Ethereum block chain is used as primary infrastructure for the application. Smart contracts generated using solidity is deployed into Ethereum network using truffle. The deployment of the application in agricultural sectors improves the accountability in the field of the supply chain. The deployment in a wider range will avoid manipulation of the data.
Agricultural supply chain tracing website involves the use of several tools and technologies, including Ganache, Solidity, and Truffle. The system uses the Ethereum blockchain as the underlying infrastructure to store and manage supply chain data securely and transparently. The smart contracts in the supply chain tracing system are generated using Solidity and deployed to the Ethereum network using Truffle.