| dc.description.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. | en_US |
