• Korean Management Science Review
• /
• v.11 no.1
• /
• pp.91-106
• /
• 1994

In mixed model assembly lines, products are assembled seqeuntially that have different combination of options specified by customers. In just in time (JIT) environment, production smoothing becomes an important issue for sub-lines which supply the necessary parts to each workstation of the assembly line. Another important issue is to avoid line stopping caused by work overload in workstations. To find a sequence which minimizes the costs associated with line stoppage and the option parts inventory level, a nonlinear mixed integer programming is formulated. Recognizing the limit of the Branch and Bound technique in large sized problems, a heuristic solution procedure is proposed. The performance of the heuristic is compared with the Branch and Bound technique through randomly generated test problems. The computational results indicate that on the average the heuristic solutions deviate approximately 3.6% from the optimal solutions.

• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.2
• /
• pp.251-260
• /
• 1998

This paper deals with a simulated annealing neural network model for determining sequences of models inputted into a mixed model assembly line. We first present a energy function fitting to our problem, next determine the value of the parameters of the energy function using convergence ratio and the number of searched feasible solution. Finally we compare our model NMS with the modified Thomopoulos model. The result of the comparison shows that NMS and Thomopoulos offer a similar output in the problems having good smoothness.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.365-368
• /
• 1996

The setting of the factory under this study is as the following. There are multiple assembly lines. Each line can handle any of given set of products. Furthermore each line is capable of assembling several products concurrently, i.e. mixed model assembly line. An incoming production order is characterized by its due-date, product type and quantity. Under this setting we first have to select the starting time and the place(assembly line) for an order to be processed. We also have to devise a way to control orders to be manufactured as scheduled. Finally there should be a mean to reschedule orders when something unexpected happens. This paper offers a scheme that provides the above mentioned necessities. It also provides a case where the scheme is applied.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.43
• /
• pp.1-16
• /
• 1997

This paper improves the autonomy for supplier's schedule and the flexibility of the final assembly line. The final assembly line is a single work station and each product taking different assembly time is considered. In the assembly schedule, the heuristic method based on the goal chasing method is used. Consequently, suppliers can independently determine their output rates and thus, change their workload pattern according to their needs and priorities. Moreover, this flexibility can help to avoid expensive final-assembly-line stoppages in case of sudden part supply disruptions. The sequencing method can be easily implemented into an existing just-in-time system. In addition, the mathematical model was formulated and the algorithm was explained through the flow chart. The numerical example was given and the efficiency of this method is shown through the analysis of computational results of that example.

• Journal of the Korea Society for Simulation
• /
• v.11 no.4
• /
• pp.69-80
• /
• 2002

This study deals with the productivity improvement on a flow production system with the consideration of line-balancing. In a flow production system, similar product models are produced on a same assembly line, the predefined process order and the limitation of total worker number. The system can be increased the work-in -process(WIP) inventory and the worker's idle time. In this study, the worker assignment model is developed to assign evenly workload of process to each product model in such a manner that each process has the different number of worker. This worker assignment model is the mathematical model that determines worker number in each process such that the idle time of processes is reduced and the utilization of worker is improved. We use a simulation technique to simulate the production line proposed by the mathematical model and apply real production line. With the result of simulation, this study analyzes the propriety of production line and proposes the alternatives of new production line

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.7
• /
• pp.1439-1445
• /
• 2009

This paper introduces a simulation model regarding the process analysis of a digital doorlock mixed assembly process. The objective of the simulation model is to evaluate the performance of various design aspects of process alternatives. To develop the simulation model, a time study is performed for each process. Next, by using ARENA, a simulation model is conducted based on the process analysis and the line balancing methodology. We found out several problems for mixed assembly process, and then suggest several alternatives to improve the system.

• Journal of the Korea Safety Management & Science
• /
• v.7 no.3
• /
• pp.85-96
• /
• 2005

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.

• Korean Management Science Review
• /
• v.13 no.1
• /
• pp.13-27
• /
• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Journal of Korean Institute of Industrial Engineers
• /
• v.17 no.1
• /
• pp.83-94
• /
• 1991

This study is concerned with the scheduling problem for mixed-model assembly lines in Just-In-Time(JIT) production systems. The most important goal of the scheduling for the mixed-model assembly line in JIT production systems is to keep a constant rate of usage for every part used by the systems. In this study, we develop two heuristic algorithms able to keep a constant rate of usage for every part used by the systems in the single-level and the multi-level. In the single-level, the new algorithm generates sequence schedule by backward tracking and prevents the destruction of sequence schedule which is the weakest point of Miltenburg's algorithms. The new algorithm gives better results in total variations than the Miltenburg's algorithms. In the multi-level, the new algorithm extends the concept of the single-level algorithm and shows more efficient results in total variations than Miltenburg and Sinnamon's algorithms.

• 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.