Optimization of Machining Operation Sequence Problem using an Integrated Genetic and Simulated Annealing Algorithm

¹ Associate Professor, Department of Mechanical Engineering, Excel Engineering College, Komarapalayam - 637303, Tamil Nadu, India
² Associate Professor, Department of Aeronautical Engineering, Excel Engineering College, Komarapalayam - 637303, Tamil Nadu, India
³ Assistant Professor, Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam - 638 401, Tamil Nadu, India
⁴ Professor, Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil - 626126, Tamil Nadu, India
⁵ Associate Professor, Department of Mechanical Engineering, K.S.Rangasamy College of Technology, Tiruchengode - 637215, Tamil Nadu, India
⁶ Assistant Professor, Department of Agricultural Engineering, Dhirajlal Gandhi College of Technology, Salem – 636309, Tamil Nadu, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 26 September 2025

Abstract

Among the most important and difficult tasks in CAPP (computer-aided process planning) is optimizing the machining operation sequence, which calls for a lot of iterations and a significant amount of computational time. Decreasing the frequency of tool changes and machine setup iterations is the primary goal of improving the machine operation sequence. To solve the CAPP problem and determine the shortest manufacturing time, this study suggests a hybrid approach called the integrated genetic and simulated annealing (IGSA) algorithm. The best sequence with the shortest manufacturing time, discovered in the first algorithm (genetic), has been proposed to be the starting sequence of the second method (simulated annealing), which searches for neighbouring ideal sequences. The feasible solutions for a given issue are the best sequences that can be found using this integration technique. The performance of the suggested IGSA algorithm was tested, including various benchmark problems with simple precedence constraints. The comparisons of benchmark and case study results with other existing algorithms in the literature show a drastic improvement in the computational time and optimal solutions.