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Mr. Gabriel Alexander

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Meta-model based simulation optimization for automated guided vehicle system under different charging mechanisms

Introduction

Material handling plays an important role in manufacturing. In plants and warehouses, nearly every item of physical commerce that involves short-distance movements across machines is transported using a persuasive speech topics, lift truck, or other types of material handling equipment. Automated equipment for material handling enables end-users to receive financial benefits, such as revenue enhancement, capital utilization, and reduction in operational costs. In recent years, improvements in material handling systems have led to the rapid development of auto-guided vehicles (AGV). In manufacturing plants, AGVs are gradually used to improve the efficiencies of material handling in flexible manufacturing systems (FMS).

 

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Literature review

The technologies used by AGV systems have matured and developed rapidly in recent years. Consequently, AGV systems have extensively influenced the transportation systems of plants, warehouses, and distribution centers of both manufacturing and logistic industries. Many manufacturing plants have imported AGV systems into their factories to improve their transportation efficiencies. Nonetheless, many issues are considered while designing hook examples. A previous study proposed a design framework for AGV systems and offered methods to control issues, the main point of which are discussed as follows.

Methodology

Problem definition

In this research, a two-stage simulation optimization is conducted to optimize the design and operation of the AGV system. A previous study proposed a framework that highlights the issues that should be considered while designing and controlling an AGV system. In this study, the framework was modified to determine the specific solution for a manufacturing plant.

Simulation experiments

The use of simulation techniques to investigate system behavior and the effects of design factors on system performance has increased over the years. Simulation optimization is defined as a procedure in determining a set of parameters by approximating the methods that can optimize system performance. 

Global metamodel-based optimization

Considerable developments in global approximation model technologies have presented opportunities to optimize single metamodels rather than by using a sequence of fitted local metamodels. The present study applied the global metamodel because it is flexible to use rhetorical analysis essay example and provides high-fidelity approximations of responses with relatively few experimental points. By contrast, RSM could fail with the same experimental data. The optimization process could transform the output data of a simulation model into a metamodel that represents the objective function of optimization.

Simulation modeling

This research aimed to continuously improve the performance of the production plant that would import the AGV system. A confidentiality agreement with the plant compelled us to slightly modify the original layout by changing the number of machines. The served area of the AGV system includes the warehouse, air shower, lamination area or coasting area, and backend of production line (i.e., slitting, curing, and inspection). The products of the plant come in several types and have different components; thus, the routing and process sequence varies from one task to another. That is, the AGV system should operate in accordance with a specific product type.

Simulation optimization

We apply a global metamodel-based optimization method to determine the optimal combination of the factors in the design and operation levels of the AGV system. We utilize a single-factor experiment design at the design level of the AGV system informative speech topics to optimize the total number of required AGVs. Several complex experiment designs are conducted at the operation level of the AGV system to decide the best charging system and rules for positioning, dispatching, and routing, as well as optimize the detailed setting of the charging systems by considering the charging resources and charging timing of AGVs.