Design and Implementation of a Service-Oriented Architecture for the Optimization of Industrial Applications

Girbea, Alina; Suciu, Constantin; Nechifor, Septimiu; Sisak, F.

doi:10.1109/tii.2013.2253112

Cited by 92 publications

(39 citation statements)

References 29 publications

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“…The transition aims at modeling the check of the worker t 2 The transition aims at modeling the check of the material t 3 The transition aims at modeling requesting the worker t 4 The transition aims at modeling requesting the material t 5 The transition aims at modeling the machine processing and publishing a logistics task t 6 The transition aims at modeling the quality detection t 7 The transition aims at modeling the selection of the AGV t 8 The transition aims at modeling the temporary storage t 9 The transition aims at modeling the selected AGV going to the start point t 10 The transition aims at modeling the reprocessing or the scarp t 11 The transition aims at modeling the rejection of the AGV t 12 The transition aims at modeling picking pallet and loading t 13 The transition aims at modeling the selected AGV going to the destination t 14 The transition aims at modeling the unloading Table 2. Places in Figure 3.…”

Section: Transitionmentioning

confidence: 99%

“…A token in this place represents a production task p 2 A token in this place represents a worker p 3 A token in this place represents a material p 4 A token in this place represents the attendance of the worker p 5 A token in this place represents enough materials p 6 A token in this place represents the absence of the worker p 7 A token in this place represents insufficient materials p 8 A token in this place represents the sataus information of a machine p 9 A token in this place represents the sataus information of an AGV p 10 A token in this place represents the finish of the machine processing p 11 A token in this place represents the request to the logistics task p 12 A token in this place represents the detection result p 13 A token in this place represents the qualified WIP p 14 A token in this place represents the nearest and available AGV p 15 A token in this place represents the unqualified WIP p 16 A token in this place represents the remote or unavailable AGV p 17 A token in this place represents an out-buffer p 18 A token in this place represents the selected logistics task p 19 A token in this place represents the correct start point p 20 A token in this place represents the transport time p 21 A token in this place represents the transport to the destination p 22 A token in this place represents the correct destination p 23 A token in this place represents the end and next cycle Figure 3 shows the TCPN model of the self-adaptive collaboration method. This model refers to the basic cycle of production-logistics systems, which consists of twenty-three places and fourteen transitions.…”

Section: Place Description Of Placesmentioning

confidence: 99%

“…However, due to external and internal fluctuations, the machining environment changes dynamically and the optimal process plan and schedule becomes less efficient or even infeasible. In order to solve the aforementioned problems, many research efforts have been made by using the advanced technologies, such as Internet of Things (IoT) [2], cloud computing (CC) [3], cyber-physical systems (CPS) [4], and service-oriented technologies (SOT) [5]. Based on these types of research and technology, real-time and multi-source manufacturing data and logistics data are easier to access than ever before [6].…”

Section: Introductionmentioning

confidence: 99%

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A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

et al. 2017

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Abstract:Complex and customized manufacturing requires a high level of collaboration between production and logistics in a flexible production system. With the widespread use of Internet of Things technology in manufacturing, a great amount of real-time and multi-source manufacturing data and logistics data is created, that can be used to perform production-logistics collaboration. To solve the aforementioned problems, this paper proposes a timed colored Petri net simulation-based self-adaptive collaboration method for Internet of Things-enabled production-logistics systems. The method combines the schedule of token sequences in the timed colored Petri net with real-time status of key production and logistics equipment. The key equipment is made 'smart' to actively publish or request logistics tasks. An integrated framework based on a cloud service platform is introduced to provide the basis for self-adaptive collaboration of production-logistics systems. A simulation experiment is conducted by using colored Petri nets (CPN) Tools to validate the performance and applicability of the proposed method. Computational experiments demonstrate that the proposed method outperforms the event-driven method in terms of reductions of waiting time, makespan, and electricity consumption. This proposed method is also applicable to other manufacturing systems to implement production-logistics collaboration.

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Section: Transitionmentioning

confidence: 99%

Section: Place Description Of Placesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

et al. 2017

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“…At the same time, numerous technologies like cloud computing [6,7], web technologies [8], service-oriented technologies [9,10], and Internet of Things (IoT) [11,12] are quickly combined with CMfg to enable intelligent manufacturing resource perception and on-demand manufacturing cloud service scheduling. Service selection and scheduling aims to optimize the execution routes of manufacturing tasks, which is a vital issue in the CMfg system.…”

Section: Introductionmentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Wang

Kang

et al. 2015

Int J Adv Manuf Technol

147

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Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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“…Girbea et al [21] use an FMS as an environment to research production scheduling and systems integration. In [22], the application of the open source Arduino microcontroller in FMS is analysed.…”

mentioning

confidence: 99%

Integration of Sensor and Actuator Networks and the SCADA System to Promote the Migration of the Legacy Flexible Manufacturing System towards the Industry 4.0 Concept

González

2018

JSAN

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Abstract:Networks of sensors and actuators in automated manufacturing processes are implemented using industrial fieldbuses, where automation units and supervisory systems are also connected to exchange operational information. In the context of the incoming fourth industrial revolution, called Industry 4.0, the management of legacy facilities is a paramount issue to deal with. This paper presents a solution to enhance the connectivity of a legacy Flexible Manufacturing System, which constitutes the first step in the adoption of the Industry 4.0 concept. Such a system includes the fieldbus PROcess FIeld BUS (PROFIBUS) around which sensors, actuators, and controllers are interconnected. In order to establish effective communication between the sensors and actuators network and a supervisory system, a hardware and software approach including Ethernet connectivity is implemented. This work is envisioned to contribute to the migration of legacy systems towards the challenging Industry 4.0 framework. The experimental results prove the proper operation of the FMS and the feasibility of the proposal.

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Design and Implementation of a Service-Oriented Architecture for the Optimization of Industrial Applications

Cited by 92 publications

References 29 publications

A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Integration of Sensor and Actuator Networks and the SCADA System to Promote the Migration of the Legacy Flexible Manufacturing System towards the Industry 4.0 Concept

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