A new hybrid algorithm for solving N jobs and M machines scheduling problems with improved local search techniques

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

Scheduling is one of the most critical tasks in manufacturing, assembling, service industries, etc. Current research attempts to maximize the makespan of any complex scheduling procedure that involves N jobs and M machines. Conventional algorithms fail to perform global optimum solutions when the number of machines & jobs is high due to the NP-hard nature of the problem. Proposed approach employs a hybrid genetic-simulated annealing algorithm with an improved local searching technique, which converges to the global optimum solutions more quickly. Numerous benchmark problems and case studies from the standard literature have been employed to test the suggested algorithm's performance. Comprehensive computational test results demonstrated that the recommended algorithm outperforms alternative heuristic and traditional approaches.