Mixed-model assembly line sequencing problems

The paper develops a comprehensive classification of mixed-model assembly lines from which four categories of model sequencing are derived. Each category aims at satisfying one or both of two objective criteria, the one minimizing the overall line length, and the other minimizing the thruput time. Approaches for solving the sequencing problem in each category are presented. The paper suggests a design strategy that can be followed by designers of mixed-model assembly lines. Specific topics requiring further research are also defined.     

Optimal mixed-model sequencing for balanced assembly lines

This paper describes an algorithm for solving optimally, the mixed-model sequencing problem when assembly line stations are balanced for each model. An optimal sequence is obtained with the minimization of the overall assembly line length for zero station idle time.The algorithm incorporates two basic steps. The first involves a search procedure that generates all cycle sequences; i.e. sequences having identical ‘start’ and ‘finish’ positions and whose work content can be executed within a defined station length. The second step uses integer programming (IP) to determine the number and combination of the various cycle sequences, such that the production demand is satisfied.     

Optimizing the number of stations in assembly lines under learning for limited production

This paper considers finding the optimal number of stations for an assembly line producing a limited quantity of a new product under learning conditions. This type of production characterizes heavy products (such as airplanes and communication systems) and science-based industries (e.g. laser cutting devices and special equipment for hospitals). These products are manufactured in assembly line fashion in small batches of a few hundred. Since the products are assumed to be totally new to the workers, the learning phenomenon is significant (i.e. task times decrease from cycle to cycle as experience is gained). Therefore, standard balancing methods are no longer applicable. Determining the number of stations has a large effect on the production rate while the actual assignment of tasks to stations helps to fine tune the cycle time. Thus, the number of stations can be regarded as a strategic decision variable . This paper discusses two ways for determining the optimal number of stations, namely as a cost minimization problem and as a profit maximization problem.