This paper discusses a special situation in project management in which an analyst wants to prioritize several independent activities to handle all them one after another, in such a way that there are no precedence relationships over the activities. As a novel idea, in this research, the notion is that the structure of prioritized activities is a linear arrangement, and therefore it could be taken into account as a Combinatorial Optimization problem. The paper formulates a mathematical model, develops a row-generation solving procedure, and reports the computational results for the problem instances of size up to 300 activities. The results demonstrate the applicability and efficiency of the proposed methodology.

Combinatorial Optimization is the procedure of optimizing an objective function over the discrete configuration space. A genetic algorithm (GA) has been applied successfully to solve various NP-complete Combinatorial Optimization problems. One of the most challenging problems in applying GA is selecting mutation operators and associated probabilities for each situation. GA uses just one type of mutation operator with a specified probability in the basic form. The mutation operator is often selected randomly in improved GAs that leverage several mutation operators. While an effective GA search occurs when the mutation type for each chromosome is selected according to mutant genes and the problem landscape. This paper proposes an adaptive genetic algorithm that uses Q-learning to learn the best mutation strategy for each chromosome. In the proposed method, the success history of the mutant in solving the problem is utilized for specifying the best mutation type. For evaluating adaptive genetic algorithm, we adopted the traveling salesman problem (TSP) as a well-known problem in the field of Optimization. The results of the adaptive genetic algorithm on five datasets show that this algorithm performs better than single mutation GAs up to 14% for average cases. It is also indicated that the proposed algorithm converges faster than single mutation GAs.

In this paper, we focus on the utilization of the feasible value constraint technique to address multiobjective Optimization problems (MOPs). It is attempted to overcome certain drawbacks associated with this method, such as restrictions on functions and weights, inflexibility in constraints, and challenges in assessing proper efficiency. To accomplish this, we propose an improved version of the feasible value constraint technique. Then, by incorporating approximate solutions, we establish connections between $\varepsilon$-(weakly, properly) efficient points in a general MOP and $\epsilon$-optimal solutions to the scalarization problem.

In this paper, the artificial neural network based Multi-Objective Optimization of twist extrusion process is carried out. The target purpose functions are equivalent plastic strain, strain distribution and extrusion force. The design variables are twistangle, friction factor and loading rate. The FEM model of the process is first created and used to create training cases for the ANN, and the well-trained ANN is used as a quick and exact model of the process. Then the Optimization of the design variables is conducted by an integrated genetic algorithm and ANN modelto create a set of optimal solutions (Pareto front). Leveldiagrams are then used to select the best solution from the Pareto front. Finally the response surface methodology has been used to study the interaction between the design parameters. The obtained results show that the best range of twist angle is from 0.7 to 45 degree, friction factor from 0.65 to 0.7 and loading rate from 6.5 to 7 mm/s. Also variables with the largest effect on the process are twist angle and friction factor.