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2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ai Zhang ◽  
Zhengmao Ju ◽  
Yiling Liu

This article takes the development and superiority of the human resource system under the IoT technology as the background and adopts the popular IoT architecture and the current latest technical theories to develop and improve the human resource management system. First, we introduce the establishment of the J2EE development environment and then introduce the realization of the three layers of the overall IoT architecture of the system, namely, the realization of the presentation layer, the realization of the layer, and the realization of the data and then the realization of the main modules of the system; finally, we explain the function test of some modules of the system. After that, we mainly analyze the requirements of the system, first the overall requirements analysis and then the function and nonfunctional requirements analysis of the system. On the basis of perfecting the service-oriented modeling of manufacturing resources, the integration and optimization of resources are carried out according to the requirements of manufacturing tasks, and the discovery mechanism and matching methods of manufacturing resource services are studied to reduce the solution space of resource allocation. Aiming at the problem of optimal utilization of manufacturing resources, this paper establishes a scientific and reasonable evaluation index system to reduce resource service costs and improve resource utilization. At the same time, after analyzing the problem of real-time distribution of resources in the manufacturing Internet of Things environment, the real-time information of resource distribution is designed. The interactive mechanism and a two-tier optimization method based on real-time information-driven resource distribution tasks are proposed. The simulation experiment results show that the optimization method proposed in this paper, compared with the traditional resource distribution method, not only reduces the carrying distance of human resource distribution but also reduces the empty load rate, thereby reducing the waste of human resources.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Peng Liu ◽  
Caiyun Liu ◽  
Xiaoling Wei

In the shared manufacturing environment, on the basis of in-depth analysis of the shared manufacturing process and the allocation process of manufacturing resources, a bilevel programming model for the optimal allocation of manufacturing resources considering the benefits of the shared manufacturing platform and the rights of consumers is established. In the bilevel programming model, the flexible indicators representing the interests of the platform are the upper-level optimization target of the model and the Quality of Service (QoS) indicators representing the interests of consumers are the lower-level optimization goal. The weights of the upper indicators are determined by Analytic Hierarchy Process (AHP) and Improved Order Relation Analysis (Improved G1) combination weighting method and the bilevel programming model is solved by the Improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II). Finally, the effectiveness of the model is validated by a numerical example.


Author(s):  
Geng Zhang ◽  
Chun-Hsien Chen ◽  
Bufan Liu ◽  
Xinyu Li ◽  
Zuoxu Wang

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Liu ◽  
Ying Zou

Sharing manufacturing is a new manufacturing mode based on sharing economy, which is one of the pillars of intelligent manufacturing. This paper proposes a two-sided matching model of shared manufacturing resources considering the psychological behavior of agents. We describe the definition of two-sided matching and introduce the concept of the cloud model. The preference information of agents is transformed to values according to the cloud model. We combine prospect theory and grey relational analysis to calculate the prospect values. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching agents is established. A numerical example for the matching of providers and demanders is provided to verify the feasibility and effectiveness of the model.


2021 ◽  
Vol 1 (4) ◽  
pp. 85-94
Author(s):  
Ліпич Л.Г. ◽  
Хілуха О.А. ◽  
Кушнір М.А.

Актуальність даної теми безпосередньо пов'язана з необхідністю підвищення рівня конкурентоспроможності українських підприємств, який у порівнянні з іноземними все ще залишається низьким. Мета статті - проаналізувати сучасні наукові розробки в області систем підтримки прийняття управлінських рішень на основі аналізу їх еволюційного розвитку. Встановлено, що розвиток ІТ - систем, які підтримують менеджмент, розпочався у 1950-1960-х роках. Найдавніші ІТ - системи називалися трансакційними або системами для обробки даних. Перші системи були простими: використовували лічильні та аналітичні машин. Вони базувалися на масових операціях, які супроводжувалися значними витратами та не високою надійністю. Ці системи використовувалися для розрахунків заробітної плати, управління матеріалами, виставлення рахунків, обліку, контролю за дебіторською та кредиторською заборгованістю, обліку робочого часу та його ефективності, а також обліку витрат виробництва. Злам 20-го та 21-го століть - це період динамічних змін у розвитку ІТ - систем, що підтримують управління, головним чином завдяки мережевим системам, корпоративним інтрамережам та системам управління знаннями. Обґрунтовано, що системи Business In-Intelligence (BI) є кульмінацією еволюції змін у сфері систем підтримки прийняття рішень та системної експертизи. Вони формують рішення що ґрунтуються на: статистиці та економетрії, операційних дослідженнях та штучному інтелекті. Генезис ІТ-систем дозволив визначити етапи розвитку інтегрованих систем управління, що розвивалися паралельно вищезгаданим поколінням систем та направлені на підтримку реалізації функцій управління. Спочатку це були системи планування вимог до матеріалів (ang. Material Requirements Planning, MRP I), створені в 1960 -х роках на основі моделі управління складськими запасами. (для виробничих підприємств), потім модель закритого циклу MRP (ang. Closed-Loop MRP) та системи планування виробничих ресурсів (ang. Manufacturing Resources Planning, MRP II). Іншою версією цих систем є системи планування ресурсів підприємства (ang. Enterprise Resources Planning, ERP), створені в 1990-х роках. Змінені функціональні наповнення цих систем призвели до появи таких версій, як ERP II, EERP (Extended ERP), @ERP, EAS (Enterprise Application Suite), eERP, IERP (Intelligent ERP ERP), ERP +, ERP III і характеризуються як нове покоління інтегрованих систем - ERP IV. Доведено, що поява нових версій систем є результатом зміни умов ведення бізнесу та можливостей, створених розвитком ІКТ.


2021 ◽  
Vol 132 ◽  
pp. 103511
Author(s):  
Yi Zhang ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Shipei Li ◽  
Qinwei Nie

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 208
Author(s):  
Zhuming Bi ◽  
Wen-Jun Zhang ◽  
Chong Wu ◽  
Chaomin Luo ◽  
Lida Xu

In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part.


Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5811
Author(s):  
Colin Reiff ◽  
Matthias Buser ◽  
Thomas Betten ◽  
Volkher Onuseit ◽  
Max Hoßfeld ◽  
...  

Process planning in manufacturing today focuses on optimizing the conflicting targets of cost, quality, and time. Due to increasing social awareness and subsequent governmental regulation, environmental impact becomes a fourth major aspect. Eventually, sustainability in manufacturing ensures future competitiveness. In this paper, a framework for the planning of sustainable manufacturing is proposed. It is based on the abstraction and generalization of manufacturing resources and part descriptions, which are matched and ranked using a multi-criteria decision analysis method. Manufacturing resources provide values for cost, quality, time and environmental impacts, which multiply with their usage within a manufacturing task for a specific part. The framework is validated with a detailed modeling of a laser machine as a resource revealing benefits and optimization potential of the underlying data model. Finally, the framework is applied to a use case of a flange part with two different manufacturing strategies, i.e., laser metal-wire deposition and conventional milling. The most influential parameters regarding the environmental impacts are the raw material input, the manufacturing energy consumption and the machine production itself. In general, the framework enabled the identification of non-predetermined manufacturing possibilities and the comprehensive comparison of production resources.


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