One of the well-known evolutionary algorithms inspired by biological evolution is genetic algorithm (GA) that is employed as a robust and global optimization tool to search for the best or near-optimal solution with the search space. In this paper, this algorithm is used to solve unequalsized machines (or intra-cell) layout problems considering pick-up and drop-off (input/output) points. Such p...

In this paper, we consider a flow shop scheduling problem with availability constraints (FSSPAC) for the objective of minimizing the makespan. In such a problem, machines are not continuously available for processing jobs due to preventive maintenance activities. We proposed a mixed-integer linear programming (MILP) model for this problem which can generate non-permutation schedules. Furthermor...

In this paper, a novel algorithm for the load flow analysis problem in an islanded microgrid is proposed. The problem is modeled without any slack bus by considering the steady state frequency as one of the load flow variables. To model different control modes of DGs, such as droop, PV and PQ, in an islanded microgrid, a new formula for load flow equations is proposed. A hybrid optimization alg...

this study is focused on developing an integrated optimization-simulation based genetic algorithm model (iosga) to develop the operational policies for a multi-purpose reservoir system. the objective function of the optimization model is considered to be a linear function of reliability (rel), resiliency (res), and vulnerability (vul) of the river-reservoir system. genetic algorithm (ga) is emp...

Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-a...

 This paper considers a bi-objective scheduling problem in a flexible manufacturing cell (FMC) which minimizes the maximum completion time (i.e., makespan) and maximum tardiness simultaneously. A new mathematical model is considered to reflect all aspect of the manufacturing cell. This type of scheduling problem is known to be NP-hard. To cope with the complexity of such a hard problem, a genet...

In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...