OPTIMIZING THE PERMUTATION FLOWSHOP SCHEDULING PROBLEM (PFSP) USING THE SCATTER SEARCH METHOD

Abstract

Scheduling is the process of optimizing limited resources, depending on the objectives. Scheduling problems are one of the decision-making problems that play a critical role in production and service systems. Continuing production regularly and systematically is an important issue for production planners. Permutation flow shop scheduling, which is a sub-branch of production scheduling, is defined as “n” jobs being processed simultaneously on “m” machines. Permutation Flow Shop Scheduling Problems (PFSPs) are in the complex and difficult problem class. Many metaheuristic methods have been proposed to solve such problems. In this study, the Scatter Search method, which is one of the population-based evolutionary methods of metaheuristic methods, was used to solve the Permutation Flow Shop Scheduling Problem (PFSP). The scatter search method was analyzed with the algorithm prepared on JavaScript programming language. With the scatter search, the total completion time of the jobs was minimized and the effectiveness of the method was tested on the problem groups frequently used in the literature. The use of the JavaScript programming language in this study has contributed to the literature on testing large-scale problems. The distribution search algorithm has a positive effect on the PTSP with an average of 2% difference from the best-known solutions due to the minimization of work times.