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The current research aims to identify new combined genetic algorithm methods to solve complex problems. The researcher has analyzed the results and findings of the previous researchers using a systematic reviewing approach and has identified the effective factors by implementing the 7 steps of Sandelowski and Barroso’s method. Among 4320 articles, 54 articles were selected based on the CASP method. In this manner, in order to evaluate reliability and quality control, the Kappa index was used, and its value was deemed to be in high compatibility regarding the identified factors. The results of the analysis of the collected data in ATLAS TI software led to the identification of 9 categories and 33 primary codes of new combined genetic algorithm methods to solve complex problems. Based on the coding, 9 categories, and 33 initial codes were identified. The identified categories are layout design, supply network, programming, Anticipation, inventory control, information security, imaging, medical imaging and wireless network. [ABSTRACT FROM AUTHOR]

The hub location problems involve locating facilities and designing hub networks to minimize the total cost of transportation (as a function of distance) between hubs, establishing facilities and demand management. In this paper, we consider the capacitated cluster hub location problem because of its wide range of applications in real-world cases, especially in transportation and telecommunication networks. In this regard, a mathematical model is presented to address this problem under capacity constraints imposed on hubs and transportation lines. Then, a new hybrid algorithm based on simulated annealing and ant colony optimization is proposed to solve the presented problem. Finally, the computational experiments demonstrate that the proposed heuristic algorithm is both effective and efficient. [ABSTRACT FROM PUBLISHER]

A genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem. [ABSTRACT FROM AUTHOR]

Single-machine capacitated lot-sizing and scheduling problem (SMCLSP) is one of the most famous fields of research in scheduling area. What makes this problem particularly difficult to solve in many real situations is large and sequence-dependent setups and also multiple criterions that must be considered. In this paper, such a problem is formulated and a hybrid simulated annealing for this problem is presented. Also, lower bounds are obtained from solving the problem relaxation, and they are compared with the optimal solutions to estimate the goodness of them. As decision criteria, all of important costs imposed by the solution are considered and also infeasible sequences are mentioned. There is a ceiling value for the manufacturer's cost of each job and according to it, some sequences between jobs are maybe infeasible. It showed the superiority of hybrid simulated annealing (HSA) compared with one of the efficient recent heuristics. The average deviation of the HSA from the corresponding optimal solution for small-size problems ranges from 0% to 10% and from the corresponding lower bound ranges for large-size problems ranges from 11% to 21%. The presented HSA is capable to solve large instances that are mostly compatible with the real-world problems. [ABSTRACT FROM AUTHOR]