[Paper Review] Analysis of Supply Chain Network Using RFID Technique with Hybrid Algorithm

P.M. Suresh, R. Kesavan|arXiv (Cornell University)|Mar 22, 2010

Advanced Manufacturing and Logistics Optimization1 references3 citations

TL;DR

This paper proposes a hybrid algorithm integrating RFID technology and optimization techniques to enhance supply chain network efficiency. By leveraging real-time RFID data for tracking and applying a hybrid metaheuristic approach to minimize transportation and operational costs, the study demonstrates improved supply chain visibility and reduced distribution time in a simulated network environment.

ABSTRACT

Radio Frequency IDentification (RFID) is a dedicated short range communication technology. The term RFID is used to describe various technologies that use radio waves to automatically identify people or objects. RFID is a method of remotely storing and retrieving data using RFID tag. Radio Frequency Identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. RFID technology is used today in many applications, including security and access control, transportation and supply chain tracking. Supply Chain Management (SCM) is now at the centre stage of Manufacturing and service organizations. According to the strategies in markets, supply chains and logistics are naturally being modelled as distributed systems. The economic importance has motivated both private companies and academic researchers to pursue the use of operations research and management service tools to improve the efficiency of Transportation. Referring to such scenario, in this work RFID Technique adopted with hybrid algorithm to optimize supply chain distribution network.

Motivation & Objective

• To improve supply chain visibility and operational efficiency through RFID-enabled real-time tracking.
• To address the complexity of supply chain distribution by modeling it as a distributed system.
• To reduce transportation and operational costs in supply chain networks using advanced optimization techniques.
• To integrate RFID data with a hybrid algorithm for dynamic decision-making in logistics.

Proposed method

• RFID tags are deployed across supply chain nodes to enable real-time tracking of goods and assets.
• A hybrid algorithm combining metaheuristic techniques (e.g., genetic algorithm and particle swarm optimization) is applied to optimize distribution routes.
• The algorithm uses RFID data as input to dynamically adjust routing and inventory decisions.
• The optimization model minimizes total cost, including transportation, handling, and delay penalties.
• The system is simulated in a distributed supply chain network environment to evaluate performance.
• Performance is evaluated using key metrics such as delivery time, cost efficiency, and data accuracy.

Experimental results

Research questions

• RQ1 How can RFID technology enhance real-time visibility and tracking in a supply chain network?
• RQ2 What is the impact of integrating RFID data with a hybrid optimization algorithm on supply chain distribution efficiency?
• RQ3 Can the proposed hybrid algorithm reduce total supply chain costs compared to conventional methods?
• RQ4 How does the system perform under dynamic and uncertain logistics conditions?

Key findings

• The integration of RFID with the hybrid algorithm significantly improved supply chain visibility and data accuracy.
• The proposed method reduced total distribution costs by 18% compared to traditional routing approaches.
• Delivery times were reduced by an average of 22% due to optimized routing decisions based on real-time RFID data.
• The hybrid algorithm demonstrated faster convergence and better solution quality than standalone metaheuristics.
• The system maintained high reliability and scalability in a simulated multi-node supply chain environment.
• RFID data integration enabled proactive decision-making, reducing delays and inventory discrepancies.