Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.

Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide the job's production schedule. However, this task is not simple, often requiring complex software tools and specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was developed to explore production scheduling performance measures to help managers in decision-making related to job attribution under three simulations of parallel machine scenarios. Five important production scheduling performance measures were considered (makespan, tardiness and earliness times, number of tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization, Multi-objective Grey Wolf Algorithm, and Non-dominated Sorting Genetic Algorithm II), and the set of optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analyzed through an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed methodology provided more robust solutions by combining different bio-inspired multi-objective techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results and support for choosing the final optimum solution.

Reliability issues are most important types of optimization problems and they are used in communication, transportation and electrical systems. This paper presents two mathematical models to solve the k-out-of-n redundancy problem where there are two objectives: maximization of reliability and minimization of cost subject to two constraints. Constraints are associated with weight and volume. In addition, strategy of redundancy is intended and ready to go cold and the components of the systems are also identical, because the model is to solve the complex models of the genetic algorithm (GA) and simulated annealing (SA). The proposed study uses NSGAII and MOPSO to solve the proposed studies and compare them using TOPSIS method.

Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.

The economic lot scheduling problem (ELSP) is a challenge between sequencing and lot sizing. In this problem, several products must be produced on a single machine in a cyclical production pattern and the primary goal is to minimize the total setup and holding expenditures. Since time affects the value of money, it is necessary to take into account the time value of money when gradual payment is the case. In this paper, a new ELSP model with the consideration of the time value of money is considered. The proposed model of this paper is formulated as a nonlinear mixed integer model and a hybrid GA is presented to solve the resulted model for large-scale problems. The proposed method is solved for some benchmark problems for large-scale problems.