STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Production Research & Management Original Research

Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes

  1. Arunesh Mishra Department of Mechanical Engineering, RKDF University, Bhopal
  2. Rahul Rajput Department of Mechanical Engineering, RKDF University, Bhopal
  3. Amit Sharma Department of Mechanical Engineering, Vedica Institute of Technology, Bhopal
  4. Arvind Gwatiya Department of Mechanical Engineering, Vedica Institute of Technology, Bhopal

Abstract

During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. Substantial gains in performance were made by applying optimized parallel line layouts, notably a 24% gain in throughput, 829 to 1,032 units, testifying to the real payback of strategic line-of-production optimization. Such outcomes attest to the merits of forward-looking scenario planning and dynamic allocation of resources based on lean manufacturing philosophy and for delivering on the essence of Industry 4.0. At the center of this strategy are simulation methods like Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modeling (ABM), each with unique strengths based on the complexity and character of the production setting. The study also assesses commonly adopted simulation software like Arena, Simul8, and FlexSim, focusing on their strengths in modeling, performance analysis, and scenario assessment. Concurrently, assorted optimization methods like line balancing, genetic algorithms, and hybrid scheduling models are investigated to optimize workflow distribution and increase system productivity. Visual aids like bottleneck analyzers and Gantt charts also assist decision-making by providing transparent, fact-based insights into system performance. Finally, the research emphasizes that simulation is not only a forecast tool but a strategic tool for designing agile, scalable, and lean production systems. When paired with smart planning paradigms and real-time production feedback, simulation enables organizations to break free from conventional manufacturing limitations and progress toward more adaptive, intelligent manufacturing solutions.

Keywords

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