Meta heuristic optimization approaches for multi objective flow shop scheduling problems

Abstract

This thesis addresses Permutation Flow shop Scheduling Problems (PFSP), which are considered with n jobs (n =1,2,3...n), and they are to be processed through m machines (m = 1,2,3 ... m) with various objectives. The processing time of all the jobs is known in advance, and the jobs are processed in the same order on various machines. The selection of an appropriate sequence order for a set of jobs is known as sequencing. Every sequence of a set of jobs will have different performance measures such as makespan time completion time of the last job scheduled on the last machine), total flow time (total time spent by all jobs in the shop), and machine idle time (though jobs are available, machines are not). Most of the research works focus on the heuristic procedures to get near-optimal solutions as described by Mohamed Kurdi (2015). Most of the heuristic procedure will give only one sequence with an optimal makespan. It is desirable to have more sequences in hand, to deal with real-time conditions. As there are possibilities of improvement in the makespan, many effort has been devoted to the development of heuristic procedures in order to provide good approximate solutions to the problem. The objective of minimizing the makespan and total flow time are often used as a criterion for flow shop scheduling. Minimization of total machine idle time leads to maximum utilization of individual machine and will minimize production cost also. Since the problem is known to be NP-hard for more than two machines, the vast majority of the research effort has been dedicated to the improvement of heuristic methodology to get good approximate solutions to the problem. newline