Scheduling of steel melt shop by using teaching learning based optimization

Abstract

Scheduling of Steel Melt Shop is a Non-deterministic Polynomial optimization problem, which is one of the most substantial areas of research survey in Integrated Steel Industry. In SMSSP there are n No. of jobs and m No. of machines where each job is processed on every machine in predefined operation sequence. Many traditional and Non - traditional methods such as First cum First Serve, Shortest Job First, Earliest Deadline First, Mixed Integer Linear Programming Model, Integrated Production Process, Ant Colony Optimization, Non Linear Optimization, Linear Programming Model, Lagrangian Relaxation Methods had been applied for some years in the past to find an accurate optimal operation scheduling with minimum Operation Time. The major contribution of this work has been the implementation of an effective scheduling method based on Teaching Learning process for solving SMSSP. TLBO is a recently developed optimization technique based on random population for solving any type of scheduling problem. It consists of two phases namely, Teacher Phase and Learner Phase. Teacher phase signify learning something from a teacher and Learner phase tells about learning by self study. TLBO performance can be attained by solving Scheduling of Steel Melt Shop by minimizing Operation time, Reduction of Tardiness and maximization of number of charges at each stage of SMS at population size of 20,39,1132 charges. The present work is a realistic case study and has proved by the results thus obtained show that TLBO is an active evolutionary algorithm to prosper a best operations scheduling.