Abstract: Mixed model assembly lines (MMAL) are a type of
production line where a variety of product models similar in product
characteristics are assembled. The effective design of these lines
requires that schedule for assembling the different products is
determined. In this paper we tried to fit the sequencing problem with
the main characteristics of make to order (MTO) environment. The
problem solved in this paper is a multiple objective sequencing
problem in mixed model assembly lines sequencing using weighted
Sum Method (WSM) using GAMS software for small problem and
an effective GA for large scale problems because of the nature of
NP-hardness of our problem and vast time consume to find the
optimum solution in large problems. In this problem three practically
important objectives are minimizing: total utility work, keeping a
constant production rate variation, and minimizing earliness and
tardiness cost which consider the priority of each customer and
different due date which is a real situation in mixed model assembly
lines and it is the first time we consider different attribute to
prioritize the customers which help the company to reduce the cost of
earliness and tardiness. This mechanism is a way to apply an advance
available to promise (ATP) in mixed model assembly line sequencing
which is the main contribution of this paper.
Abstract: In the last decades to supply the various and different
demands of clients, a lot of manufacturers trend to use the mixedmodel
assembly line (MMAL) in their production lines, since this
policy make possible to assemble various and different models of the
equivalent goods on the same line with the MTO approach.
In this article, we determine the sequence of (MMAL) line, with
applying the kitting approach and planning of rest time for general
workers to reduce the wastages, increase the workers effectiveness
and apply the sector of lean production approach.
This Multi-objective sequencing problem solved in small size with
GAMS22.2 and PSO meta heuristic in 10 test problems and compare
their results together and conclude that their results are very similar
together, next we determine the important factors in computing the
cost, which improving them cost reduced. Since this problem, is NPhard
in large size, we use the particle swarm optimization (PSO)
meta-heuristic for solving it. In large size we define some test
problems to survey it-s performance and determine the important
factors in calculating the cost, that by change or improved them
production in minimum cost will be possible.