Under the influence of Industry 4.0, manufacturing resources (MRs) across regions and enterprises realize ubiquitous interconnection and real-time data acquisition via communication network and internet of things, which makes it possible for distributed MRs to provide appropriate production services according to changeable market demand. Therefore, social manufacturing (Social Mfg) system came into being. Under the Social Mfg environment, a flexible configuration method of distributed MRs is put forward in this paper, which is qualified to quickly respond to order demand and production disturbance. Firstly, superior manufacturing services are selected from the resource pool of the platform according to the types of subtasks. Secondly, high-quality service compositions capable of completing a complicated manufacturing task are obtained by NSGA-III algorithm, which meets the requirements of multi-objective optimization in terms of time, cost and quality. In addition, a MRs collaborative network model is constructed based on the historical cooperation data. The Louvain algorithm is employed to detect the manufacturing communities (MCs) from the collaborative network. Finally, the MCs are applied to provide the optimal service schemes for personalized demand at a fast speed, as well as realizing dynamic reconfiguration of MRs under abnormal disturbance. Experimental results demonstrate that the proposed method possesses significant performance in both efficiency and robustness, and makes great contributions to the development of Social Mfg in terms of resource configuration. (c) 2021 Elsevier B.V. All rights reserved.

为解决分布式多工厂生产调度问题,将其转化为分布式柔性车间调度问题,设计了基于二阶振荡的随机权重混合粒子群算法,以最小化、最大完工时间为目标,将柔性作业车间调度问题嵌套于分布式调度方式中进行求解,利用随机权重来平衡全局和局部搜索能力,运用学习因子的二阶振荡提高全局搜索能力,并通过算例仿真验证了该算法的有效性和优越性。

工程变更管理是解决产品设计问题的一种特殊形式,必须遵循许多规则才能满足产品变更的要求。由于工程变更对产品开发周期、成本有很大的影响,因此有必要提前预测设计变更并对其影响进行有效的评估。本文提出一种融合多评价参数过程仿真

具有组合加工约束的柔性作业车间调度问题是混流生产线中常见的任务排产问题。然而,传统车间调度方法均未将组合加工约束考虑进调度模型中,无法满足混线生产模式的现实情况。针对这一问题,分析了混流生产线的工艺状态模型。在此基础上,基于传统柔性作业车间调度问题,建立了具有组合加工约束的混线车间调度问题的数学模型。然后,针对组合加工约束,提出了一种改进的多段编码、交叉、变异的遗传算法。最后,将该算法应用于某航空航天研究所导弹结构件生产车间,验证了该方法的可行性和有效性。

工程变更管理是解决产品设计问题的一种特殊形式,必须遵循许多规则才能满足产品变更的要求。由于工程变更对产品开发周期、成本有很大的影响,因此有必要提前预测设计变更并对其影响进行有效的评估。本文提出一种融合多评价参数过程仿真的工程变更管理方法。首先,建立了变更传播模型,包括变更传播影响的数学模型、设计变更方案的的评价得分;然后介绍了基于蚁群算法的变更方案优化过程,并给出了一种自动获取最优变更方案的优化算法;最后,以带式输送机平台为例验证了该方法的有效性。结果表明,产品的变化需求可以由多个候选变更方案满足,并可根据方案的特性获得最优的结果。

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.
© 2020 Elsevier Ltd

With the increasing requirement for personalized customization service, discrete manufacturing workshop, as the parts processing unit in manufacturing system, is expected for more agile and fast adaptation to environment changes, dynamically handling production tasks according to resource conditions. Simultaneously, distributed artificial intelligence system (e.g. multiagent manufacturing system and the holonic manufacturing system) has been considered as an important approach for developing industrial applications to solve the problems of complexity, uncertainty, and dynamic in the modern manufacturing environment. But the lack of universality and the difficulty in deployment have restricted the use of distributed artificial intelligence in actual industrial sites. For this issue, a new concept of agent computing node is proposed in this paper to enable the realization of multiagent manufacturing system. Adaptation layer, information development layer, and intelligent analysis layer are investigated for standardizing the configuration mode of agent computing node. Cooperating agent computing node with the radio frequency identification-based dynamic recognition technology for workpiece machining process is presented in this paper, and a practical approach for multiagent manufacturing system is considered, which can apply the functions regarding to deployment of dynamic scheduling and plug-and-play. A laboratory discrete manufacturing workshop system is used as a case study to prove the feasibility of this approach. In addition, a verification in industry is carried out, and the result proves the universality of this approach.