U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

Poontana Sresracoo,Nuchsara Kriengkorakot,Krit Chantarasamai,Preecha Kriengkorakot

doi:10.3390/mca23040079

Abstract

The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

Highlights

Nowadays, the degree of competition in many industries is very high
It can be seen that the differential evolution (DE) algorithm DE/Best/2 to Exponential Crossover 1 was the method for the optimal solution search when finding the number of workstations and minimizing the solution search time
The DE algorithm DE/Best/2 to Exponential Crossover 1 was compared with other metaheuristic methods in order to determine the efficiency of the optimal solution search

Summary

Introduction

The degree of competition in many industries is very high. organizations that respond quickly to changes in their customers’ needs, require less effort to control the storage of their inventory, and spend less time in production will certainly achieve business advantages over their competitors. Organizations need to show continuous improvement and development of their products’ values in order to respond to the needs of their customers by reducing costs and improving product quality during the production process. This has resulted in changes to the production system, such as the change from the push system to the pull system, which has reduced the volume of each batch size produced. The changes include replacing the traditional production layout of straight lines with U-shaped production lines or U-lines. There are many interesting areas under the topic of U-shaped assembly line balancing that require further research studies [1]

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Conclusion