International Journal of Innovations in Engineering and Technology (IJIET) http://dx.doi.org/10.21172/ijiet.193.03 Volume 19 Issue 3 June 2021 16 ISSN: 2319-1058 Performance Enhancement of Flexible Manufacturing System Using Meta-Heuristics Hybrid Algorithm Muleta Tiki Lemi 1 1 Lecturer, Department of Mechanical Engineering, College of Engineering and Technology, Wollega University, Post Box No: 395, Nekemte, Ethiopia. Mahesh Gopal 2* 2* Assistant Professor, Department of Mechanical Engineering, College of Engineering and Technology, Wollega University, Post Box No: 395, Nekemte, Ethiopia. Adugna Fikadu Geleta 3 3 Lecturer, Department of Mechanical Engineering, College of Engineering and Technology, Wollega University, Post Box No: 395, Nekemte, Ethiopia. Abstract- The goal of today's manufacturing strategy is to maximize the benefits of flexibility. Only when a manufacturing system is completely controlled by FMS technology is this possible. A flexible manufacturing system is one in which there is some degree of flexibility that enables the system to adapt to changes, whether anticipated or unanticipated. This flexibility may be broken down into two main categories and various subcategories. The first is what is known as machine flexibility, which allows a machine to produce a variety of items. The second category is routing flexibility, which allows different computers to perform the same job. CNC machine tools, transport systems, and control systems are the three main components of flexible manufacturing systems. The so-called intelligent manufacturing systems represent a higher degree of flexible manufacturing systems. Petrinets are a form of modeling construct that may be used in a variety of situations, including data analysis, simulations, business process modeling, and other scenarios. This mathematical construct can aid in the planning of workflows or the presentation of data on complex systems. Six machines, six jobs are considered in the case study. The Petrinet concept is proposed to solve scheduling problems and compared with Scatter Search algorithm. The results are compared for case study (6 machines X 6 jobs) and it is observed that the Petrinet provides better result when compared with Scatter Search with respect to machine utilization. When comparing Petrinet to Scatter Search in terms of machine utilization, the results show that Petrinet outperforms Scatter Search. Keywords – Flexible Manufacturing System, Machines, Scatter Search approach, Petrinet model, Machine utilization I. INTRODUCTION Flexible Manufacturing System (FMS) is an automated production environment in which numerous processes may run at the same time. Different items may be made at the same time, and shared resources are often used to save costs. The system is made up of machines that can perform a wide range of tasks on a set of parts. The unpredictable situation of today's market is requiring the manufacturing managers to develop the flexible manufacturing systems (FMS) to meet the difficulties imposed by worldwide competition, constantly changing consumer needs, speedy delivery to market and progress in technology Raj et al (2008) [1]. A simulation mechanism and a real-time control system are two major components of the scheduling mechanism. The simulation system assesses dispatching rules and chooses the most appropriate dispatching rule for each criterion. The real-time control system examines the system's performance value and monitors the shop floor on a regular basis. Until the difference between the real performance value and the value anticipated by simulation reaches a certain threshold, the specified dispatching rule is utilized Kim et al (1994) [2]. In a flexible manufacturing system (FMS), a scheduling challenge is made up of two interdependent tasks: loading and sequencing. The loading issue has two objectives: minimize system workload imbalance and minimize system imbalance and the number of late jobs, with restrictions such as the number of tools slots with duplications, unique job routing, non-splitting of tasks, and machine capacity Kripa, and Tzen (1985) [3].