The hybrid metaheuristics algorithms (HMHAs) have gained a considerable attention for their capability to solve difficult problems in different fields of science. This chapter introduces some applications of HMHAs in solving inventory theory problems. Three basic inventory problems, joint replenishment EOQ problem, newsboy problem, and stochastic review problem, in certain and uncertain environ...

A metaheuristic is an intelligent, iterative process that guides a search and can be applied towards optimization problems, such as the Traveling Salesman Problem. Two well studied techniques for solving optimization problems are Genetic Algorithms and Ant Colony Systems. However, each metaheuristic has different strengths and weaknesses. Genetic Algorithms are able to quickly find near optimal...

In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metahe...

pumps are among the early instruments invented by humans to be taken advantage of in the use of water utilities. design and operation problem of pumping stations is a type of mixed integer non linear programming, in which gradient based optimization methods are not efficient for solving complex problems of this type. in this regard, such evolutionary optimization algorithms as genetic algorithm...

This paper considers a no-wait multi product flowshop scheduling problem with sequence dependent setup times. Lot streaming divide the lots of products into portions called sublots in order to reduce the lead times and work-in-process, and increase the machine utilization rates. The objective is to minimize the makespan. To clarify the system, mathematical model of the problem is presented. Sin...

In combinatorial optimization, near-optimal algorithms such as metaheuristics allow to iteratively solve in a reasonable time NP-hard complex problems. Two main categories of metaheuristics are distinguished: population-based metaheuristics (P-metaheuristics) and solution-based metaheuristics (S-metaheuristics). P-metaheuristics are population-oriented as they manage a whole population of solut...

This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm ...

The foundations for evolutionary algorithms (EAs) were established in the end of the 60’s [1, 2] (EAs) and strengthened in the beginning of the 70’s [3, 4]. EAs appeared as an alternative to the exact or approximate optimization methods whose application to many real problems were not acceptable in terms of performance. When applied to real problems, EAs provide a valuable relation between qual...