BI STAGE FSSP WITH TRIANGULAR INTUITIONISTIC FUZZY NUMBER AND COMPARISON BETWEEN HEURISTICS

Scheduling theory focuses on the best way to distribute limited resources among tasks throughout time and allocating limited resources to tasks in the best possible way. This field's history begins when two people first fought over a resource and devised a strategy to divide it without resorting to violence. In real world applications, scheduling is a vital yet highly intricate issue. In industrial processes, managing production with the goal of reducing makespan is an essential duty. The processing time of each job on each machine has been taken to be a real number for the majority of scheduling problems studied already. However, processing times in real-world applications are frequently uncertain, meaning that human error or operational flaws may cause them to fluctuate easily. This study explores a two-stage FSSP, where parallel machines execute jobs in the both stages. The processing times in the flow shop scheduling are represented by fuzzy numbers in this work. This paper gives comparative study to address the FSSP under an uncertain environment based on triangular intuitionistic fuzzy numbers (TrIFNs). To accurately model uncertainty and imprecision in processing times, we employ TIFNs, which provide a more comprehensive representation of vagueness compared to traditional fuzzy sets. To find the best work sequence with the goal of minimizing the makespan, fuzzy and scheduling principles are applied to the flow shop scheduling challenges. The main goal is to optimize the flow time by comparing the efficacy of three distinct optimization approaches. A rigorous computational analysis is conducted to evaluate their performance, leveraging fuzzy arithmetic operations and defuzzification techniques. The three heuristics' performances are compared and their performance is analyzed. An analysis of the performance of three heuristics i.e. B&B, Dannenbring exhaustive search and the division algorithm proposed by santhi in previous paper has been conducted. The findings offer valuable insights into the applicability of fuzzy scheduling methodologies in complex manufacturing environments, contributing to the broader discourse on uncertainty modelling in production systems. The comparison is shown by a numerical illustration. This study provides a comparison and evaluation of the scheduling problem which also helps the reader comprehend various environmental assumptions, system limitations, and useful heuristics for further research.