Stopwatch Time Study Method

Oct 13, 2025 by talynt.org 84 views

Optimization of Door Leaves Production Scheduling at PT. Mahogany Lestari with the Simulated Annealing Method

Introduction

In today's fast-paced manufacturing industry, companies like PT. Mahogany Lestari face significant challenges in meeting customer demand while maintaining high production efficiency. As a leading manufacturer of high-quality door leaf products, PT. Mahogany Lestari has been struggling to optimize its production scheduling to meet the increasing demand for its 8P colonial model door leaf product, which is in high demand for exports to various countries. The main challenge faced by the company is to ensure that all orders are met in accordance with the designated due date, which involves optimal management of resources such as processing time at 12 work stations, production lines, and the number of orders that come.

The Problem of Production Scheduling

Production scheduling is a critical aspect of manufacturing that involves planning and coordinating the production process to meet customer demand. In the case of PT. Mahogany Lestari, the company's production scheduling process is complex and involves multiple variables such as processing time, production lines, and the number of orders. The company's current production scheduling method, which is based on the Least Slack Time method, has been found to be inefficient and unable to meet the increasing demand for its products.

The Simulated Annealing Method

To address the problem of production scheduling, this research proposes the use of the Simulated Annealing (SA) method, which is an optimal solution search algorithm inspired by the metal cooling process. The SA method randomly explores various combinations of work sequences (jobs) with the aim of finding solutions that produce the smallest MAKESPAN (total time completion of all work). The SA method has been found to be effective in solving complex optimization problems and has been widely used in various fields such as logistics, finance, and engineering.

The Research Methodology

This research uses a combination of three methods to optimize production scheduling at PT. Mahogany Lestari:

Stopwatch Time Study Method: This method is used to measure the process time at each work station accurately.
Least Slack Time Method: This method is used to determine the sequence of actual work based on the remaining time before the due date.
Simulated Annealing Method: This method is used to determine the optimal proposal work sequence.

Research Results

This study produced a better scheduling solution to the actual scheduling with the Least Slack Time method. The SA method is able to find the optimal sequence of work, namely Napoleon 6P-Carolina 6p-colonial 6p-colonial 8P 8P 4P. These results are obtained after random iteration 30 times in each decrease in temperature until it reaches the condition steady state, where no more solutions are found that produce a smaller MAKESPAN. Condition steady state achieved at a temperature of 180.5.

Advantages of the Simulated Annealing Method

The SA method has several advantages that make it an effective solution for production scheduling problems:

Produce optimal solutions: The SA method is able to find optimal solutions by exploring various combinations of random work sequences.
Flexible: The SA method can be applied to various types of production scheduling problems with appropriate modifications.
Does not require complete information: The SA method does not require complete information about the function of the destination and its limitations, so that it can be applied to complex problems.

Conclusion

This study shows that the use of the Simulated Annealing method in scheduling the production of doors at PT. Mahogany Lestari can increase production efficiency and assist companies in meeting customer demand more optimally. The application of the SA method can help the company minimize MAKESPAN, reduce waiting time, and increase customer satisfaction.

Recommendation

Based on the results of this study, the following recommendations are made:

Apply the SA method for production scheduling at PT. Mahogany Lestari as a whole, not only for 8P Colonial products.
Utilizing information technology to help the simulation process and implementation of the SA method.
Conduct periodic evaluation of the results of the application of the SA method to identify the potential improvement and make the required adjustments.

With the application of the Simulated Annealing method, PT. Mahogany Lestari can continue to improve its production performance and maintain its position as a leading manufacturing company in the field of door leaf production.
Frequently Asked Questions (FAQs) about the Simulated Annealing Method in Production Scheduling

Introduction

The Simulated Annealing (SA) method has been widely used in various fields such as logistics, finance, and engineering to solve complex optimization problems. In the context of production scheduling, the SA method has been found to be effective in optimizing production efficiency and meeting customer demand. However, there are still many questions and concerns about the SA method and its application in production scheduling. In this article, we will address some of the frequently asked questions (FAQs) about the SA method in production scheduling.

Q1: What is the Simulated Annealing method?

A1: The Simulated Annealing (SA) method is an optimal solution search algorithm inspired by the metal cooling process. It randomly explores various combinations of work sequences (jobs) with the aim of finding solutions that produce the smallest MAKESPAN (total time completion of all work).

Q2: How does the SA method work?

A2: The SA method works by iteratively exploring various combinations of work sequences (jobs) and evaluating their performance based on the MAKESPAN. The method starts with an initial solution and then randomly generates new solutions by perturbing the current solution. The new solution is then evaluated and compared to the current solution. If the new solution is better, it is accepted as the new current solution. If the new solution is worse, it is accepted with a probability that decreases as the temperature decreases.

Q3: What are the advantages of the SA method?

A3: The SA method has several advantages that make it an effective solution for production scheduling problems:

Produce optimal solutions: The SA method is able to find optimal solutions by exploring various combinations of random work sequences.
Flexible: The SA method can be applied to various types of production scheduling problems with appropriate modifications.
Does not require complete information: The SA method does not require complete information about the function of the destination and its limitations, so that it can be applied to complex problems.

Q4: What are the limitations of the SA method?

A4: The SA method has several limitations that need to be considered:

Computational complexity: The SA method can be computationally intensive, especially for large-scale production scheduling problems.
Convergence: The SA method may not always converge to the optimal solution, especially if the temperature is not properly controlled.
Parameter tuning: The SA method requires careful parameter tuning to achieve good results.

Q5: How can the SA method be applied in production scheduling?

A5: The SA method can be applied in production scheduling by:

Defining the production scheduling problem: The production scheduling problem needs to be clearly defined, including the objectives, constraints, and variables.
Implementing the SA method: The SA method needs to be implemented, including the selection of the initial solution, the perturbation scheme, and the temperature control.
Evaluating the results: The results of the SA method need to be evaluated to determine the effectiveness of the method in solving the production scheduling problem.

Q6: What are the benefits of using the SA method in production scheduling?

A6: The benefits of using the SA method in production scheduling include:

Improved production efficiency: The SA method can help to optimize production efficiency by finding the optimal sequence of work.
Reduced waiting time: The SA method can help to reduce waiting time by minimizing the MAKESPAN.
Increased customer satisfaction: The SA method can help to increase customer satisfaction by meeting customer demand more optimally.

Conclusion

The Simulated Annealing (SA) method is a powerful tool for solving complex optimization problems in production scheduling. While the SA method has several advantages, it also has some limitations that need to be considered. By understanding the SA method and its application in production scheduling, companies can improve their production efficiency, reduce waiting time, and increase customer satisfaction.