• Journal of the Korea Safety Management & Science
• /
• v.6 no.2
• /
• pp.115-125
• /
• 2004

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed-model U-lines (MMULs). Balancing and sequencing problems are important for an efficient use of MMULs and are tightly related with each other. However, in almost all the existing researches on mixed-model production lines, the two problems have been considered separately. A genetic algorithm for balancing and sequencing in mixed-model U line is proposed. A presentation method and genetic operators are proposed. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that the proposed algorithm is promising in solution quality.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.26 no.4
• /
• pp.109-124
• /
• 2001

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed model assembly lines (MMALs). Line balancing and model sequencing are important for an efficient use of MMALs. The two problems of balancing and sequencing MMALs are tightly related with each other. However, In almost all the existing researches on mixed-model production lines, the two problems have been considered separately. In this research, an endosymbiotic evolutionary a1gorithm, which is a kind of coevolutionary a1gorithm, is adopted as a methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary a1gorithm to solving the integrated problem. Some evolutionary schemes are used In the a1gorithm to promote population diversity and search efficiency. The proposed a1gorithm is compared with the existing evolutionary algorithms in terms of solution quality and convergence speed. The experimental results confirm the effectiveness of our approach.

• IE interfaces
• /
• v.13 no.2
• /
• pp.204-210
• /
• 2000

This paper deals with the problem of mixed-model sequencing on an assembly line. In this sequencing problem we want to minimize the risk of the conveyor stoppage and the total utility work. This paper applies genetic algorithm to solve the mixed-model sequencing problem which is formulated as an integer programming. The solution we get from this algorithm is compared with the solution of Tsai(1995)'s.

• Proceedings of the Safety Management and Science Conference
• /
• 2008.11a
• /
• pp.35-39
• /
• 2008

This paper considers the sequencing of products in mixed model assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time, the number of utility workers, and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1118-1122
• /
• 2005

In order to keep production balance at a mixed-model assembly line and a painting line, large WIP(Work- In-Process) inventories are required between two lines. To increase the efficiency of line handling through reducing the inventories under this circumstance, this paper concerns with a sequencing problem for a mixed-model assembly line that includes a painting line where the uncertain elements regarding the defective products exist. Then, we formulate a new type of the sequencing problem minimizing the line stoppage time and the idle time with forecasting the supply time of the products from the painting line. Finally, we examine the effectiveness of the proposed sequencing through computer simulations.

• Industrial Engineering and Management Systems
• /
• v.15 no.1
• /
• pp.1-10
• /
• 2016

This paper presents a fast sequencing algorithm for a mixed model assembly line with multiple workstations which minimize the total utility work and idle time. We compare the proposed algorithms with another heuristic, the Tsai-based heuristic, for a sequencing problem that minimizes the total utility works. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. The Tsai-based heuristic performs best in terms of utility work, but the fast sequencing algorithm performs well for both utility work and idle time. However, the computational complexity of the fast sequencing algorithm is O (KN) while the Tsai-based algorithm is O (KNlogN). Actual computational time of the fast sequencing heuristic is 2-6 times faster than that of the Tsai-based heuristic.

• IE interfaces
• /
• v.10 no.2
• /
• pp.51-56
• /
• 1997

This paper concerns with the problem of mixed model assembly sequencing using neural net. In recent years, because of two characteristics of it, massive parallelism and learning capability, neural nets have emerged to solve the problems for which more conventional computational approaches have proven ineffective. This paper proposes a method using neural net that can consider line balancing and grouping problems simultaneously. In order to solve the mixed model assembly sequencing of the motor industry, this paper uses the modified ART1 algorithm.

• Journal of the Korea Safety Management & Science
• /
• v.9 no.4
• /
• pp.129-136
• /
• 2007

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.37 no.3
• /
• pp.39-55
• /
• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.35 no.3
• /
• pp.25-43
• /
• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.