Abstract The number of automatic guided vehicles (AGVs) and the sequence workpieces transported from warehouse affect final processing completion time system. Therefore, this paper researches optimal configuration AGVs transport in workshop. Firstly, an optimization model is built with minimizing time. Secondly, Plant Simulation used to determine AGVs. Thirdly, linear uniform crossover, inversi...

The aim of lot sizing problems is to determine the periods where production takes place and the quantities to be produced in order to satisfy the customer demand while minimizing the total cost. Due to its importance on the efficiency of the production and inventory systems, Lot sizing problems are one of the most challenging production planning problems and have been studied for many years wit...

this study is focused on the development of a systematic computational approach which implements genetic algorithm (ga) to find the optimal rigorous kinetic models.a general kinetic model for hydrogenolysis of dibenzothiophene (dbt) based on langmuir-hinshelwood type has been obtained from open literature. this model consists of eight continuous parameters(e.g., arrhenus  and van't hoff paramet...

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 ...

microgrids are small-scale, low voltage (lv) power networks which employ renewable distribution energy resources (ders) with power electronic interfaces (peis). microgrids as single controlled units and active distribution networks require flexible control systems to ensure reliable and secure operation in different modes. these various operations of microgrid cause variations in voltage and fr...

The Genetic Algorithm (GA) has been used widely to solve an array of combinatorial optimization problems such as the traveling salesman problem, the job-shop scheduling problem, the quadratic assignment problem, and production planning problems. On the other hand, solving the problem of Preventive Maintenance (PM) scheduling for a group of non-identical machines using a GA is a relatively new t...

This paper presents a genetic algorithm (GA) for solving a generalized model of single-item resource-constrained aggregate production planning (APP) with linear cost functions. APP belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. We linearize a linear mixed-integer model of APP subject to hiring...

This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA a...

Proper scheduling of tasks leads to optimum using of time and resources, in order to obtaining best result. One of the most important and complicated scheduling problems is open shop scheduling problem. There are n jobs in open shop scheduling problem which should be processed by m machines. Purpose of scheduling open shop problem is attaining to a suitable order of processing jobs by specified...

We present a √ e/( √ e−1)-approximation algorithm for the nonpreemptive scheduling problem to minimize the total weighted completion time of jobs on a single machine subject to release dates and precedence constraints. The previously best known approximation algorithm dates back to 1997; its performance guarantee can be made arbitrarily close to the Euler constant e [18].