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.

OPTIMIZATION of inventory costs is the most important goal in industries. But in many models, the constraints are considered simple and relaxed. Some actual con-straints are to consider the COMBINATORIAL production and purchase models in multi-products environment. The purpose of this article is to improve the e ciency of inventory management and  nd the economic order quantity and economic production quantity that can minimize the cost of inventory and customer satisfac-tion. In this study, the models with these targets in COMBINATORIAL production and purchase systems with the assumption the warehouse and budget constraints are proposed. Since a long time for solving the problem with an exact method is required, we develop a genetic algorithm.

OPTIMIZATION methods are one of the strongest tools for managing time and decreasing unnecessary costs in operational issues. The purpose of OPTIMIZATION, regarding constraints and requirements, is to find an appropriate and acceptable solution to a problem. Since; most of the COMBINATORIAL OPTIMIZATION problems, such as Travelling Salesman Problem (TSP) and different types of Vehicle Routing Problem (VRP), are subcategories of NP-Hard class, expert recommendations are toward solving these kinds of problems by metaheuristic algorithms such as Artificial Bee Colony (ABC) algorithm instead of exact solving methodologies.In this paper, a comprehensive study was conducted on the background of Artificial Bee Colony algorithms and the results of its application on various transportation problems. The formulation of Vehicle Routing Problem and its constraints was also discussed. The results show that the ABC algorithm has a significant power to improve solving various problems. As an intuitive summary, one can refer to Szeto et al. (2010) who proposed an ABC algorithm for solving the Capacitated Vehicle Routing Problem in which the mean percentage improvement of the average results of all test instances was 4.16% and the best percentage improvement was 3.53%. Further, a Hybrid ABC algorithm was designed by Zhang et al. (2014) for one of the latest Vehicle Routing Problems. They implemented the algorithm for Environmental Vehicle Routing Problem which outperforms the original ABC algorithm by 5% on average. Therefore, it can be concluded that the Artificial Bee Colony algorithm is very successful in improving the results of this kind of experiment.In completion of the above-mentioned, the results of the proposed ABC algorithm by this study for solving Travelling Salesman Problem and Vehicle Routing Problem with Simultaneous Pickup and Delivery confirmed the expressed idea. As a result, the assumed algorithm improved instances of TSP about 1.03% and 8.88% which were named gr120 and gr202, respectively. It also enhanced the CMT1X and CMT3X instances in VRP-SPD about 0.41% and 1.31%, respectively. This certificates the quality, high capacity and preference of the ABC algorithm.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex OPTIMIZATION problem that guarantees the global optimal solution. The convex OPTIMIZATION problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.