• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.59-65
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• 2010

This paper proposes a modified heuristic mixed model assembly line (MMAL) balancing algorithm that provides consistent station assignments on a model by model basis as well as on a station by station. Basically, some of single model line balancing techniques are modified and incorporated to be fit into the MMAL. The proposed algorithm is based on N.T. Thomopoulos' [8] method and supplemented with several well proven single model line balancing techniques proposed in the literature until recently. Hoffman's precedence matrix [2] is used to indicate the ordering relations among tasks. Arcus' Rule IX [1] is applied to generate rapidly a fairly large number of feasible solutions. Consequently, this proposed algorithm reduces the fluctuations in operation times among the models as well as the stations and the balance delays. A numerical example shows that the proposed algorithm can provide a good feasible solution in a relatively short time and generate relatively better solutions comparing to other three existing methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.737-740
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• 1996

This paper suggests a boltzman machine neural network model to determine model input sequences in line balancing process of mixed model assembly line. We first present a proper energy function, next determine the value of parameters using simulation process.

• IE interfaces
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• v.9 no.2
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• pp.39-45
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• 1996

This paper investigates the problems of grouping N products on an assembly line with an objective of maximizing the option grouping rate. Before developing a mixed model grouping algorithm, simulation studies are committed for developing operating rules and evaluating the layout production systems. A mixed model grouping algorithm is suggested and it is applied to the color selection lane in automobile production system, which reveals a high mixed model grouping rate.

• IE interfaces
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• v.19 no.1
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• pp.34-42
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• 2006

This paper presents a scheme for a hierarchical production scheduling and control system for a refrigerator factory with mixed model assembly lines. The setting of the factory is as follows. There are three mixed-model assembly lines called main line A, B and C and two batch lines that supply parts to the main lines. For each of the main lines, three work-centers are dedicated to them. The sub-lines and work-centers produce parts in batch type. An incoming production order from the master planner is characterized by its product type, amount, and due date. Under this situation, the proposed scheme has several features to schedule and control the above mentioned factory; 1) select the starting time and the place (assembly line) for an order processing, 2) devise a way to control orders to be processed as scheduled, and 3) reschedule orders when something unexpected happen. Finally, this paper provides a case study where the proposed scheme is applied to.

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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3863-3869
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• 2009

In recent years, globalization of markets and increased consumer sophistication in automotive industry have led to an increase in the variety of products and a consequent increase in the number of variants of any given product line that a manufacturer must supply. Also, the modularity trend of automotive production systems greatly affects on the production systems of automotive parts suppliers. These typically requires the implementation of cost efficient, flexible production systems, so called mixed-model assembly lines. We studied the problems of mixed-model assembly and assembly error-preventing techniques, and developed the error-preventing monitoring system using RFID in the mixed-model automotive parts assembly line. Especially, this study aimed to solve the problems and supplement the existing systems for preventing assembly errors, by developing a monitoring system using RFID to minimize the issues currently occurring in the field of automotive parts assembly, such as losses from incorrect installation and omission.

• IE interfaces
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• v.17 no.3
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• pp.282-293
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• 2004

Line Balancing is the problem to assign tasks to stations while satisfying some managerial viewpoints. Most researches about the Mixed-Model Line Balancing problems are focused on the minimizing the total processing time or the number of workstations. Independently, some research reports consider the balance issues of the physical workloads on the assembly line. In this paper, we are presenting a new mathematical model to accomplish the line balance considering both the processing time and the workloads at the same time. To this, end, we propose an zero-one integer program problem, and we use the Chebyshev Goal Programming approach as the solution method. Some computational test runs are performed to compare the pay-offs between the processing time and the workloads. And, the test results show us that the reliable balanced work schedules can be obtained through the proposed model.

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

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

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