Optimal / heuristic approach in solving certain production scheduling problems in fuzzy and non fuzzy environment

Abstract

The present thesis entitled Optimal / Heuristic Approach in Solving newlineCertain Production Scheduling Problems in Fuzzy and Non Fuzzy newlineEnvironment consists of 7 chapters as a whole. The work embedded in this thesis is devoted to the least time and least cost scheduling problems. The models related to the Satisfaction level of demand maker, various performance measures under deterministic, probabilistic and fuzzy environment, which is more or less applied in industries, production concerns, manufacture processes and in real world situations in order to minimize the production cost. The summary of the thesis at a glance is as follows: Chapter-1 Basics, Development and Brief Survey of Scheduling Problems is introductory, which deals with the origin of operation research, conceptual development of scheduling theory with realistic applications, mathematical techniques and improvement technology used in the current scheduling problems. This chapter is classified into various sections in sequential manner such as: Classification of scheduling problem, Performance measures, component of production cost, concept of fuzzy logic and fuzzy scheduling mathematical techniques used in solving scheduling problems and historical survey etc. Chapter-2 Analysis of Satisfaction level of demand maker in scheduling under fuzzy environment is bi-objective, as it presents an analytical study regarding completion time and due time of each job in order to find the satisfaction level of demand maker in scheduling under fuzzy environment as well as it minimizes the earliness and lateness penalties in a flowshop production process in 2-stage, 3-stage and general n- stage flow shopproblems. This chapter is divided into three sections: section 2.1 presents a heuristic algorithm for n jobs and 2 machines production scheduling taking processing time of jobs in fuzzy environment. The section 2.2 is the extended work of section 2.1 as the problem discussed here is on 3 machines with an additional parameter of transportation time of jobs.