Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one.
Finally, the structural system design optimization problem is formulated as a multi-objective one to be solved by a genetic algorithm based approach.
As discussed in Section 2, the problem under consideration is a cross-layer multi-objective one.
Studies evaluating the approaches show that the multi-objective one presents better solutions.
The KH is used for solving single-objective optimization problem while ALO is used to solve multi-objective one.
In this paper, we present experimental results comparing both strategies with three different approaches: the traditional one, a simple GA-based, and a multi-objective one.
The strategies were evaluated with real systems and three approaches (and seven algorithms): (1) the traditional one, based on Tarjan's algorithm, (2) the GA-based one, implemented with three different configurations of weights, and (3) the multi-objective one, implemented with three MOEAs: NSGA-II, PAES, and SPEA2.
An overview is presented on existing multi-objective, one-layer and two-layer architectures that incorporate dynamic real-time optimisation for optimal process performance.
Moreover, springback minimization problems are typically multi-objective ones because springback reduction may conflict with other goals in stamping design such as thinning reduction.
With respect to this question, we can also observe if a strategy influences in the performance of the algorithms, mainly the multi-objective ones.
Future studies for cash management in single machine scheduling can be focused on integrating the objective function of this paper with the traditional ones via multi objective scheduling approaches.
