Reactive fuzzy dispatching rule for automated guided vehicles

Benincasa, Antonio; Morandin, Orides; Kato, E.R.R.

doi:10.1109/icsmc.2003.1245673

Cited by 14 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this stage, the problem is to decide which of the AGVs shall be selected, since more than one may be available. This paper uses two dispatching rules to this problem: shortest travel time/distance (STT/D), presented by Benincasa et al (2003), and random vehicle (RV), presented by Egbelu and Tanchoco (1984).…”

Section: Production Scheduling Of Manufacturing Systemsmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Sanches

Rocha

Castoldi

et al. 2015

J Control Autom Electr Syst

View full text Add to dashboard Cite

The problem of production scheduling of manufacturing systems involves the system modeling task and the application of a technique to solve it. This kind of scheduling is characterized by the large number of possible solutions, where several researches have been using the genetic algorithms as a search method to solve this problem, since these algorithms have the capacity of globally exploring the search space and to find good solutions quickly. This paper proposes the use of adaptive genetic algorithm to solve this kind of scheduling problem. The aim of this paper was to obtain a good production schedule considering simultaneous use of machines and automated guided vehicles that minimize the makespan with low running time. The results of this paper were validated in large scenarios and compared with other approach. These results are presented and discussed in this paper.

show abstract

Section: Production Scheduling Of Manufacturing Systemsmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Sanches

Rocha

Castoldi

et al. 2015

J Control Autom Electr Syst

View full text Add to dashboard Cite

show abstract

“…In order to evaluate the proposed method, an application was developed for the fuzzy model defined in [5]. The following sections characterize the problem addressed and the model considered.…”

Section: Implementation Proposalmentioning

confidence: 99%

“…The aim of the fuzzy model proposed in [5] is to consider some input variables in order to obtain a decision in relation to the priority of service to workstations with pending resource requests. Thus, the priority value returned by the fuzzy system classifies each one of the AGVs in the manufacturing system making it possible to choose the best ranked AGV.…”

Section: B Definition Of Fuzzy Modelmentioning

confidence: 99%

“…As a way of evaluating, the proposal is applied to the basic rules of a fuzzy system for the problem of dispatching automated guided vehicles developed in [5] presented in Section IV. Afterwards, in Section V, the rule base generated by the proposal is applied to a hypothetical flexible manufacturing system and compared to the FIFO rule for dispatching and the original fuzzy rule base.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hierarchical fuzzy rule-based building model applied to a AGV dispatching system in an FMS

Morandin

Caridá

Kato³

et al. 2011

2011 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Excellence in manufacturing systems has been recognized as one of the main factors behind the success of industrial companies or production companies. New technology for manufacturing processes plays a significant role in this process. Achieving the potential of technological innovations in production, however, requires a wide range of management, as well as engineering issues related to the system. Material handling is a key component in reaching flexibility, manufacturing, dynamism and agility goals. The handling capacity of advanced materials is essential because without this ability of providing the material needed for the proper workstation at the right time and in the right amount, the whole plant will become "bogged down". This makes it less efficient and thus produces less profit and / or it operates at higher costs. This paper proposes a heuristic dispatching Automation Guided Vehicle (AGV) based on multiple attributes and fuzzy logic. Typically, the description of the rule base corresponds to all the possibilities of the connection between the input and output. The manual setting up of the rule base for a determined set of variables is not feasible when considering the efficiency the rules can offer in terms of performance during the inference process. It is also important to note that even for an expert in the field, the task of considering the quantitative value of the correct contribution where each rule affects the system becomes a difficult task. Taking this into account, this paper uses an approach to reduce the rules to a method of hierarchical fuzzy rules. As a means of evaluation, the proposed method is applied to a model based on fuzzy rules developed and used in AGV dispatching of a Flexible Manufacturing System (FMS).

show abstract

“…Benincasa et al [15] has presented a model for AGV dispatching based fuzzy systems. Many other papers have also proposed dispatching rules multi-attribute, for example: Kim et al [16], Tan and Tang [17], Jeong and Randhawa [18], Umashankar and Karthik [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model Using Coverability Tree from a Modeling in Petri Nets Applied in AGVs Dispatching

Caridá

Morandin

Cerioni

et al. 2014

2014 Brazilian Conference on Intelligent Systems

View full text Add to dashboard Cite

In the last years, the industries are applying automation techniques with the aim to increase their efficiency to remain competitive. Due to this fact, there is an intensive search for techniques and methods applied to manufacturing systems for improvements, whether in quality, service deadlines and / or increased production. Several studies and practical applications have indicated that the Automated Guided Vehicles (AGVs) are efficient for transport task in industries and warehouses. The management of these AGVs is the key to a transportation system that ensures the improvements envisioned by industries. One of the main problems encountered in the management of AGVs is the decision to dispatch. Some authors suggest that a weak point of dispatching rules, even the multi-attributes, is to consider only the values of variables in current time for decision-making. This paper proposes a prediction model in which one achieves an improvement in the optimization objective by reading from specific states of the factory in the near future. The proposal is based on the use of coverability tree from modeling in Petri nets with ability to provide important data for AGVs dispatch system. From these data, the dispatch system can take more assertive decisions to optimize the performance of Flexible Manufacturing Systems (FMS). The tests are performed using the software CPNtools, to model Petri nets, and Simio software to build the virtual environment, thereby having two different levels of abstraction. The validation is done through the analysis of scenarios that can happen in a production system, and with the use of the proposal of prediction is verified that in these scenarios is possible to extract important information.

show abstract

Reactive fuzzy dispatching rule for automated guided vehicles

Cited by 14 publications

References 20 publications

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

A hierarchical fuzzy rule-based building model applied to a AGV dispatching system in an FMS

Prediction Model Using Coverability Tree from a Modeling in Petri Nets Applied in AGVs Dispatching

Contact Info

Product

Resources

About