Mathematical modeling and optimization approach to study flow shop scheduling models

Abstract

In the thesis, the mathematical models of multi-machine permutation flow shop scheduling problems are presented. Flow shop is one of the widely studied system configuration of production environment which ensures the natural ordering of machines through which the jobs are processed in a fixed order. This system configuration has been studied under the deterministic and stochastic variations of scheduling parameters. Further, the impact of various forms of production environments, including no-wait, no-idle time on scheduling of jobs over the available system of machines have been reported. The importance of different scheduling parameters, specifically sequence-dependent setup time, sequence-independent setup time, uncertain processing time, and uncertain maintenance activities, in real world production enterprises has been illustrated. Single-objective and multi-objective flow shop scheduling problems of optimizing the different performance measures of production environments, particularly makespan, system utilization time, and system idle time have been introduced. Various computational techniques specifically the heuristics and metaheuristics have been successfully developed and implemented to solve these NP-complete combinatorial optimization problems. Extensive computational experiment has been carried out to analyse the superiority of the proposed computational techniques in term of effectiveness and efficiency in solving the presented mathematical models. newline