Ali Vatankhah Barenji

Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line

Abstract The success of a flexible assembly line (FAL) depends on efficacious scheduling and control architecture. However, the scheduling and control architectures currently employed in FALs lack the flexibility and reconfiguration capacity to manage disturbances when they occur. Consequently, the system performance rapidly degrades when the system operation is interrupted. The objective of this study is to examine the potential enhancement of FAL performance through the use of a radio-frequency-identification-enabled multi-agent scheduling and control system (RFID-enabled MASCS). A simulation test platform is developed for the examination of an RFID-enabled MASCS in a FAL, and several system performance measures are considered in the simulation test platform. The results indicate that the RFID-enabled MASCS can increase the uptime productivity and production rate of a FAL. A real case-study test is performed, and a 22% decrease in lead time along with significant improvements in other system performance indicators are observed, especially when a series of disturbances occur within the examined assembly machine.

A framework for structural modelling of an RFID-enabled intelligent distributed manufacturing control system

A modern manufacturing facility typically contains several distributed control systems, such as machining stations, assembly stations, and material handling and storage systems. Integrating Radio Frequency Identification (RFID) technology into these control systems provides a basis for monitoring and configuring their components in real-time. With the right structural modelling, it is then possible to evaluate designs and translate them into new operational applications almost immediately. This paper proposes an architecture for the structural modelling of an intelligent distributed control system for a manufacturing facility, by utilising RFID technology. Emphasis is placed on a requirements analysis of the manufacturing system, the design of RFID-enabled intelligent distributed control systems using Unified Modelling Language (UML) diagrams, and the use of efficient algorithms and tools for the implementation of these systems.

Toward a framework for intra-enterprise competency modeling

The purpose of this paper is to propose a framework for intra-enterprise competency modeling. A case study to illustrate the adopted concepts and developed framework is discussed. Based on the proposed framework, emphasis is placed on investigating the capability models in different departments of the enterprise with the goal of enhancing enterprise decision making process.

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

Improving multi-agent manufacturing control system by indirect communication based on ant agents

Manufacturing control systems are facing difficulty on adopting agility in today’s dynamic environment. Much research has been conducted on the use of multi-agent systems to facilitate this transition. In our previous work, a radio frequency identification–enabled multi-agent-based manufacturing control system was introduced for flexible manufacturing systems. In that control system, a direct coordination mechanism coordinates the agents. However, the decision-making response of the control system to disturbances was low and caused the agents to spend more time on processing messages than doing the actual job. Thus, in this article, we propose an indirect coordination mechanism based on ant colony intelligence aiming to improve the self-organization and processing time of the system. The new system utilizes the stigmergy mechanism for cooperation among the agents. Furthermore, ant agents are created by the existing agents to enable communication among them. The proposed new mechanism was implemented in a flexible assembly line. The simulation results of a case study considering shop floor problems indicates, for most performance measures, that the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with indirect coordination mechanism performs better in comparison with the radio frequency identification–enabled multi-agent-based manufacturing control system integrated with direct coordination mechanism.Manufacturing control systems are facing difficulty on adopting agility in today’s dynamic environment. Much research has been conducted on the use of multi-agent systems to facilitate this transit...

Evaluation of product recyclability at the product design phase: a time-series forecasting methodology

Nowadays, the society is paying more and more attention to the issues that plague the environment. There is a growing need for enterprises to design and manufacture environmentally friendly products, in order to take up the responsibility of sustainable development of the society. The entire product life cycle consists of design, manufacturing, usage and recycling phases. These phases involve various economic and environmental factors that affect the recyclability of products. Previous researchers mainly focused on the single phase of the product’s life cycle. Moreover, they did not take into account the dynamic change of the recyclable material’s cost. In this paper, a time-series forecasting methodology is proposed to evaluate the product’s recyclability at the product design phase. It considers various economic and environmental factors of different stages of the product’s life cycle. In addition, a time-series forecasting method is utilised for predicting the cost of the recycled material at the product’s end-of-life. To demonstrate the effectiveness of this methodology, a case study of a cylinder engine design was presented. The results showed that the proposed methodology was fully capable of providing decision support to the designers by accessing the recyclability of the product at the design phase.

Modeling mechanical properties of FSW thick pure copper plates and optimizing it utilizing artificial intelligence techniques

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.

Quantifying the advantage of a kitting system using Petri nets: a case study in Turkey, modeling, analysis, and insights

In case a paradigm is introduced to improve the operation of manufacturing systems, it is important to anticipate the performance of the system before expensive and time-consuming implementation. In this study, we provide the use of Petri nets in order to quantify the possible advantage of kitting feeding method in an assembly line through a case study. In particular, Petri nets have been developed through the integration of the resource-oriented and process-oriented modeling approaches and then a detailed quantitative analysis of the current and proposed system have been performed. The results and the analysis obtained from the Petri nets are used to give an idea about the anticipated performance of the proposed system before a possible implementation. Therefore, results can easily be used to aid decision-making.