Earticle

Companies are pursuing the management of small quantity batch production or JIT(Just-in-time) system for improving the delivery response and LOB(Line Balancing) in order to satisfy consumers’ increasing demands in the current global economic recession. And in order to improve the growth of production for reducing manufacturing cost, improvements of the Load Balancing have become an important reformation factor. Thus this paper is aimed at warehouse which procures materials on the assembly line in procurement logistics of automotive logistics and proceed with research on heuristic algorithm development which can increase the Load Balancing of workers. As a result of this study, when applied the primary target value, it was verified that the whole workers decreased from 28 to 24. Furthermore, when specified the secondary target value and applied algorithm once more, it was verified that the Load Balance Ratio was improved from 44.96% to 91.7%.

The efficiency of the purchasing and procurement logistics is important in automotive industry. The rationalization of production system is directly impact on productivity and quality. For this reason importance of logistics is high. Despite we are continuously making effort, our country are still below the level than developed country on logistics efficiency. Rising labor costs is an important factor in increasing logistics costs. So workforce reduction in logistics department is a large part. We deal with A-company inbound logistics, especially procurement logistics in automotive logistics as research object. So in this study we do research on work load balance about workers. We do research on 1,475 kinds of components in procurement process. We applied work load balance algorithm on chassis, final, sequence, trim warehouses workers. According to number of workers and average M/H, algorithm is applied in two ways. After applied work load balance algorithm we reduced numbers of workers from 28 to 20 and improved worker load balance rate from 47.1% to 93.7%

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

The purpose of a man-machine chart is improved utilization of a man or a machine. Improved utilization can mean less idle time, rebalanced idle time, or less idle time of an excessive component. In this study we improved the productivity of the assembly line in the electronics company using man-machine chart. The results are applicable for improving the effectiveness of line balancing problems in the electronics industry.

The paper presents an implementation procedure of assembly line for ABS motor, which is composed of four subassemblies-yoke, grommet, housing and armature. The characteristics of ABS motor and its assembly processes are analysed, and the automation possibility of each process is examined in order to decrease assembly time. The assembly machines and facilities are then selected for automatic assembly, and the layout of the selected facilities is determined. Finally, task allocation of each worker is achieved by assembly line balancing to increase assembly productivity and efficiency. The line efficiency is also analyzed using simulation.

Multiple U-typed production lines are increasingly accepted in modern manufacturing system for the flexibility to adjust to changes in demand. This paper considers multiple U line balancing with the objective of minimizing cycle time considering of moving time of workforce given the number of workstation. Like the traditional line balancing problem this problem is NP-hard. In this paper, we show how genetic algorithm can be used to solve multiple U line balancing. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators are also employed. Extensive computational experiments are carried out to show the performance of the proposed algorithm. The computational results show that the algorithm is promising in solution quality.

In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

In this thesis presents line balancing problems of two-sided and mixed model assembly line widely used in practical fields using genetic algorithm for reducing throughput time, cost of tools and fixtures and improving flexibility of assembly lines. Two-sided and mixed model assembly line is a special type of production line where variety of product similar in product characteristics are assembled in both sides. This thesis proposes the genetic algorithm adequate to each step in tow-sided and mixed model assembly line with suitable presentation, individual, evaluation function, selection and genetic parameter. To confirm proposed genetic algorithm, we apply to increase the number of tasks in case study. And for evaluation the performance of proposed genetic algorithm, we compare to existing algorithm of one-sided and mixed model assembly line. The results show that the algorithm is outstanding in the problems with a larger number of stations or larger number of tasks.

This study was conducted in order to enhance the productivity of a packaging line of a Kaoliang Spirit factory in China. With the use of comparative methodology using a computer-simulated technique for comparison of various alternative models, the packaging line selected as the test subject was able to display a relatively superior alternative to the existing model. Alternative 1 was formulated in accordance with the concept of line balancing and the re-allocation of workers with no significant changes in that it was done in consideration of the re-distribution of the work time that was already put into use, which was dictated by the company's regulations on line formulations; which states that the inspector should be separated from other processes to ensure the maintenance of a certain degree of product quality. Alternative.2 was formulated, all the conditions remaining the same except this time disregarding the company's regulations on line formulations-when put into comparison it proved to be the most productive alternative by showing the highest product output, decreased waiting time and low work-in-process level. The research showed that the company's regulation on line formulation proved to be that main cause which was holding down the level of productiveness of the packaging line of the Kaoliang Spirits factory the research selected.

This paper considers the design problem of assembly lines which are flow oriented production system suitable for mass production. Sine the installation of an assembly line is a midlong-term decision and usually requires large capital investments, it is important that such a system is optimally designed. The simple assembly line balancing problem(SALBP) is allocating tasks to workstations under the cycle time(sum of task times) constraint of each workstation and precedence constraints between tasks. In addition to the basic constraints of SALBP, the generalized assembly line balancing problem(GALBP) considers assignment restrictions such as incompatibilities between tasks, resource(operator) related workstation restriction, etc. The purpose of this paper is to introduce a new integer programming model for GABLP. We propose mild constraints and task-task/task-workstation relationship matrices. these are useful for presenting and solving GALBP as more realistic form.

Cart-pole 균형 문제는 유전자 알고리즘, 인공신경망, 강화학습 등을 이용한 제어 전략 분야의 표준 문제이다. 본 논문에서는 cart-pole 균형문제를 해결하기 위해 실시간 강화 학습을 이용한 접근 방법을 제안하였다. 본 논문의 목적은 cart-pole 균형 문제에서 OREL 학습 시스템의 학습 방법을 분석하는데 있다. 실험을 통해, 본 논문에서 제안한 OREL 학습 방법은 Q-학습보다 최적 값 함수에 더 빠르게 접근함을 알 수 있었다.

The cart-pole balancing problem is a pseudo-standard benchmark problem from the field of control methods including genetic algorithms, artificial neural networks, and reinforcement learning. In this paper, we propose a novel approach by using online reinforcement learning(OREL) to solve this cart-pole balancing problem. The objective is to analyze the learning method of the OREL learning system in the cart-pole balancing problem. Through experiment, we can see that approximate faster the optimal value-function than Q-learning.

본 논문은 NP-난제로 알려진 단일모델 교차혼합 조립라인 균형문제에 대해 휴리스틱 알고리즘을 제안하였다. 조립라인 균형문제는 주로 메타휴리스틱 방법들을 적용하고 있는 추세로 다항시간의 정확한 해를 찾는 알고리즘이 제안 되지 않고 있어 실무에 적용하기가 매우 어렵다. 제안된 알고리즘은 주어진 총 조립시간 W 와 순환시간 c에 대해 m∗ = ⌈ W/c ⌉의 최소 작업자수와 c∗ = ⌈ W/m∗ ⌉의 목표 순환시간을 구하고, i = 1,2, ⋯ , m∗ 개의 작업대에 Ti = c∗ ± α ≤ c 가 되도록 양방향의 공정들을 배정하는 양방향 군집방법을 적용하였다. 7개의 다양한 실험 데이터에 적 용한 결과 제안된 양방향 군집 알고리즘은 타 방법들과 동일한 성능을 보였다.

This paper suggests heuristic algorithm for single-model cross-over assembly line balancing problem that is a kind of NP-hard problem. The assembly line balance problem is mainly applied with metaheuristic methods, and no algorithm has been proposed to find the exact solution of polynomial time, making it very difficult to apply in practice. The proposed bi-directional clustering algorithm computes the minimum number of worker m∗ = ⌈ W/c ⌉ and goal cycle time c∗ = ⌈ W/m∗ ⌉ from the given total assembling time W and cycle time c. Then we assign each workstation i = 1,2, ⋯ , m∗ to Ti = c∗ ± α ≤ cusing bi-directional clustering method. For 7 experimental data, this bi-directional clustering algorithm same performance as other methods.

NP-난제로 알려진 양측 조립라인 균형문제는 주로 메타휴리스틱 방법들을 적용하여 해를 구하고 있다. 본 논문 은 총 작업완료시간 W와 순환시간 c 가 주어진 양측 조립라인의 선행순서도에서 좌측, 우측과 좌․우측 무관으로 공정들 을 분류하고, 좌측과 우측 각각에 대해 m∗ = ⌈ W/c ⌉개의 작업대에 Ti = c∗ ± α < c, c∗ = ⌈ W/m∗ ⌉이 되도 록 공정들을 할당하는 병렬군집 알고리즘을 제안하였다. 제안된 알고리즘을 4개의 실험데이터, 17개의 에 적용한 결 과, 기존의 메타휴리스틱 방법들에 비해 최소 작업대 수 m∗ 를 구하였으며, T max < c로 순환시간을 단축하였다. 또한, 제안된 알고리즘은 휴리스틱 방법임에도 불구하고, 조립라인 효율성의 극대화와 작업자간 작업시간 편차를 최소화시킬 수 있었다.

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with Ti = c∗ ± α < c, c∗ = ⌈ W/m∗ ⌉ such that m∗ = ⌈ W/c ⌉ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m∗ and can be reduce the cycle time to T max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

Although the product line produces a large volume of goods in a relatively short time, once the product line is established there are numerous problems that arise in connection with this product line. One of these problems is the problem of balancing operations or stations in terms of equal times and in terms of the times required to meet the desered rate of production. The objective of line balancing is minimizing the idle time on the line for all combinations of work stations subject to certain restrictions. In general, there are two types of line-balancing situations : (1) assembly line balancing and (2) fabrication line balancing. Two approaches to the assembly line balancing problem have been used. The first assumes a filed cycle time and find the optimum number of work stations. The second approach to the assembly line balancing problem assumes the number of work stations to be fixed and systematically coverages on a solution which minimizes the total delay time by minimizing the cycle time. Here the cycle time is determined by the longest station time. In this paper, by using the second approach method, a general mathematical model, problem solutions, and computer program for the assembly line balancing problem is presented. Data used is obtained from the company which has been confronted with many problems arising in connection with their assembly line.

Disassembly of discarded products takes place in the process of remanufacturing, recycling, and disposal. The disassembly lines have been taken as available choice for automated disassembly; therefore, it has become essential that it be designed and balanced to work efficiently. The multi-objective disassembly line balancing problem seeks to find a disassembly sequence which provides a feasible disassembly sequence, minimizes the number of workstations and idle time, and balances the line for the disassembly of post consumed product by considering the environment effects. This paper proposes a multi-criteria decision making technique based heuristic for assigning the disassembly tasks to the workstations. In the proposed heuristic, the PROMETHEE method is used for prioritizing the tasks to be assigned. The tasks are assigned to the disassembly workstations according to their priority rank and precedence relations. The proposed heuristic is illustrated with an example, and the results show that substantial improvement in the performance is achieved compared with other heuristics.

This paper presents a new integer formulation (Type III ALB) for a single model assembly line balancing problem. The objective of the formulation is to minimize the total idle time, which is defined as the product of the number of work stations and the cycle times minus the total work content. This formulation considers currently existing Type I (minimizing the number of work stations for a given cycle time) and type II (minimizing the cycle time for a given number of work stations) formulations as its special cases and provides the global minimum solutions of the cycle time and the number of work stations. This information would be of great value to line designers involved in designing new assembly lines and rebalancing old lines under flexible conditions. Solution methods based on combination of Type I and Type II approaches are also suggested and compared.

In general, the line balancing problem is defined as of finding an assignment of the given jobs to the workstations under the precedence constraints given to the set of jobs. Usually, the objective is either minimizing the cycle time under the given number of workstations or minimizing the number of workstations under the given cycle time. In this paper, we present a new type of an assembly line balancing problem which occurs in an electronics company manufacturing home appliances. The main difference of the problem compared to the general line balancing problem lies in the structure of the precedence given to the set of jobs. In the problem, the set of jobs is partitioned into two disjoint subjects. One is called the set of fixed jobs and the other, the set of floating jobs. The fixed jobs should be processed in the linear order and some pair of the jobs should not be assigned to the same workstations. Whereas, to each floating job, a set of ranges is given. The range is given in terms of two fixed jobs and it means that the floating job can be processed after the first job is processed and before the second job is processed. There can be more than one range associated to a floating job. We present a procedure to find an approximate solution to the problem. The procedure consists of two major parts. One is to find the assignment of the floating jobs under the given (feasible) assignment of the fixed jobs. The problem can be viewed as a constrained bin packing problem. The other is to find the assignment of the whole jobs under the given linear precedence on the set of the floating jobs. First problem is NP-hard and we devise a heuristic procedure to the problem based on the transportation problem and matching problem. The second problem can be solved in polynomial time by the shortest path method. The algorithm works in iterative manner. One step is composed of two phases. In the first phase, we solve the constrained bin packing problem. In the second phase, the shortest path problem is solved using the phase 1 result. The result of the phase 2 is used as an input to the phase 1 problem at the next step. We test the proposed algorithm on the set of real data found in the washing machine assembly line.

In traditional production lines, such as assembly lines, each worker is usually assigned to a particular fixed work, and decreasing the task to master the assigned work is valuated. However, when an imbalance exists between workers' speeds, if a worker delays the overall work in the production line, the production rate of the particular line will also decrease. To avoid this problem, the "Self-Balancing Production Line" was introduced. In this type of production line, each worker is assigned work dynamically, and when specific conditions are satisfied, production remains balanced. Characteristics of these lines that can be preempted at any place have already been analyzed by some researchers. A previous paper examined the situation in which only a single worker can process one machine and cannot preempt processing, and the improved policy of an ordinary selfbalancing production line, which specifies which stations workers can process and how workers can behave. This policy achieveda high production rate with only four stations and two workers (Buzacott, 2002). In that paper, worker processing stations and the behavior of a specific worker were limited, andthe paper focused only on specific stations and workers. Therefore, it is not applicable to any worker sequence. In this paper, we focus on other ways to decrease cycle time. In this kind of line, a worker processes at his or her speed. Therefore, if a worker is assigned stations according to his or her speed, the line can decrease cycle time. To do so, we relax the assumptions of this type of line and set a new condition. Under these conditions, we compare our results to the results of previous papers.