عنوان مقاله [English]
A production line consists of machines connected in series and separated by
buffer capacity. Each part is required to be processed on each machine during a
time called the service or process time. Material flow may be disrupted by
machine failure or by differences between the service times of the stations.
The inclusion of buffers increases the average production rate of the line by
limiting the propagation of distributions, but at an additional cost of capital
investment, floor space of the line and inventory. On the other hand, the
inclusion of parallel machines in a station increases its reliability and
results in higher production rate. Determining buffer size and number of
parallel machines in a station is a challenging problem. This paper formulates
the problem of determining the optimal (or near optimal) number of machines and
buffer capacities in failure-prone production and assembly lines to optimize
production rate. This paper also provides a methodology to solve this problem.
The objective is to maximize production rate with minimum machine purchase cost
and minimum total buffer size (A multi-objective formulation). The majority of
solution methods assume that the process times, time between failures and
repair times, are deterministic or exponentially distributed. This paper
relaxes these restrictions by proposing a simulation based methodology that can
consider general distribution functions for all parameters of production lines.
Considering the large number of factors in such problems (machines and buffers
of each station), we first use a two level fractional factorial design to
determine the more significant factors, and second, use a response surface
design to build a response surface metamodel as a production rate estimator,
based on different configurations of buffer capacity and number of machines. We
use the Lp-metric method as one of the powerful methods for multi-objective
problem solving that generates different solutions based on objective weights.
Finally, we use a genetic algorithm combined with the lines search method to
solve the multi objective model and to determine the optimal (or near optimal)
number of machines and buffer capacities in each station.