Hybrid Flow-Shop In Make-To-Order Manufacturing Industry: A Case Study Of Eastern Fine Printing Vietnam

Abstract

Minimizing makespan, the total duration of a set of tasks or processes, is a critical challenge in operations research. Efficient scheduling is crucial for industries such as manufacturing, healthcare, transportation, and many others. Traditional mathematical models often face limitations in dealing with large-scale problems and might not be able to consider the complex real-world scenarios and constraints. To address these challenges, heuristic algorithms are often employed as an alternative solution. This study explores the application of heuristic algorithms to minimize makespan in real world optimization problems. The goal is to find efficient scheduling solutions that consider factors such as task durations, task dependencies, resource availability, and precedence relationships. The study focuses on the evaluation and comparison of different heuristic algorithms in tackling the makespan minimization problem. Instead of formal mathematical models, heuristic algorithms take a more flexible and adaptive approach to finding near-optimal solutions. By understanding and leveraging the characteristics of the problem domain, these algorithms aim to strike a balance between solution quality and computational efficiency. One commonly used heuristic algorithm is the Greedy Randomized Adaptive Search Procedure (GRASP). GRASP can be applied to various optimization problems, including makespan minimization, by iteratively constructing initial solutions and applying local search techniques to improve them. Another widely employed constructive heuristic is the Nawaz, Enscore, and Ham (NEH) algorithm. NEH constructs an initial schedule based on a priority rule and iteratively improves the schedule through insertion procedures. These heuristic algorithms can be used individually or in combination to tackle the makespan minimization problem. The study assesses both the solution quality and computational efficiency of the heuristic algorithms. The objective is to find near-optimal schedules with reduced makespan, considering the real-world constraints and variations. The experiments demonstrate the ability of heuristic algorithms to adapt to different industries and problem instances. In conclusion, this study highlights the significance of heuristic algorithms in solving the challenging problem of minimizing makespan in operations research. Through their flexibility and adaptability, heuristic algorithms can provide near-optimal solutions that consider real-world constraints. By exploring different heuristic algorithms, industries can find efficient scheduling solutions that enhance productivity and improve resource allocation in diverse contexts. This research contributes to the field of operations research by showcasing the potential of heuristic algorithms in tackling complex scheduling problems in real-world scenarios.