Abstract :
Scheduling for a two-stage production system is one of the most common
problems in production management. In this production system, a number of
products is produced and each product is assembledfrom a set of parts. The parts
are produced in the first stage that is a fabrication stage and thenthey are
assembled in the second stage that usually is an assembly stage. In this article the
first stage assumed as a hybrid flow shopwith identical parallel machines and the
second stage will be an assemble work station. Twoobjective functionsare
considered that are minimizing the makespan, and minimizing the sum of
earliness andtardiness of products.At first the problem is defined and its
mathematical model is presented. Since the considered problem is NPhard,themulti-objective
genetic algorithm (MOGA) is used to solve this problem
in two phases. In the first phase the sequence of the products assembly is
determined and in the second phase the parts of each products is scheduled to be
fabricated. In each iteration of the proposed algorithm, the new population is
selected based on non-dominance rule and fitness value. To validate the
performance of the proposed algorithm, in terms of solution quality and diversity
level, various test problems are designed and the reliability of the proposed
algorithm is compared with two prominent multi-objective genetic algorithms,
i.e. WBGA, and NSGA-II. The computational results show that the performance
of the proposed algorithms is good in both efficiency and effectiveness criteria. In
small-sized problems, the number of non-dominance solution come out from the
two algorithms N-WBGA (the proposed algorithm) and NSGA-II are
approximately equal. Also more than 90% solution of algorithms N-WBGA and
NSGA-II are identical to the Pareto-optimal result. Also in medium problems,
two algorithms N-WBGA and NSGA-II have approximately an equal
performance and both of them are better than WBGA. But in large-sized
problems, N-WBGA presents the best results in all indicators.
