Robust assignment of customer orders with uncertain configurations in a production network for aircraft manufacturing

Production of multi-variant products in a network requires the assignment of customer orders to locations and periods. This is a highly complex planning task, as requirements of procurement, production, distribution, and sales have to be considered. Providing customers with the flexibility of configuring their ordered products after order assignment further increases the complexity of the planning task by taking uncertainty into account. Therefore, a robust optimisation model, using scenarios representing potential customer-specific order configurations, is introduced. By providing enough flexibility to handle maximum work overload caused by the potential order configurations at locations, a robust assignment of orders can be guaranteed in order to avoid undesirable situations causing delays and additional costs. Therefore, the mid-term adjustments of the flexibility limits are enabled by the changeability of workforce supply by making use of external workers. An industrial application of the model in manufacturing of the Airbus A320 Family of aircrafts is presented. The costs for offering configuration flexibility to customers are quantified by the expected value of perfect information. The explicit consideration of configuration uncertainty through the use of scenarios is discussed based on the value of the stochastic solution in comparison to the results attained by simplistically using the expected value.     

Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine–part–worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.     

Fuzzy neural network based yield prediction model for semiconductor manufacturing system

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.     

Using a multi-agent system to optimise resource utilisation in multi-site manufacturing facilities

Due to cost economies and better serving the global market, many enterprises expanded their manufacturing environment from a localised, single-site facility to more globalised, multi-site facilities. In order to take advantage of operating multi-site facilities, it is vital to make optimisation decisions of resource utilisation as if these facilities situated across different geographical locations are one integrated facility and take into account of the extended multi-site constraints and variables. This paper proposes a multi-agent system, using its characteristics of autonomy and intelligence, to integrate process planning and production scheduling across different facilities, so as to secure the most efficient and cost-effective plan and schedule to meet the demand. A currency-based agent iterative bidding mechanism is developed to facilitate the co-ordination of agents to achieve the goal. A genetic algorithm is employed to tune the currency values for agent bidding. In this paper, a case study is used for simulation in order to demonstrate the effectiveness and performance of the proposed agent system.     

Optimal production control policy in unreliable batch processing manufacturing systems with transportation delay

This paper considers the problem of production planning of unreliable batch processing manufacturing systems. The finished goods are produced in lots, and are then transported to a storage area in order to continuously meet a constant demand rate. The main objective of this work is to jointly determine the optimal lot sizing and optimal production control policy that minimise the total expected cost of inventory/backlog and transportation, over an infinite time horizon. The decision variables are the lot sizing and the production rate. The problem is formulated with a stochastic dynamic programming model and the impulse control theory is applied to establish the Hamilton–Jacobi–Bellman (HJB) equations. Based on a numerical resolution of the HJB equations, it is shown that the optimal control policy is governed by a base stock policy for production rate control and economic lot size for batch processing. A thorough analysis and practical issues are addressed with a simulation-based approach. Thus, a combined discrete–continuous simulation model is developed to determine the optimal parameters of the proposed policy when the failure and repair times follow general distributions. The results are illustrated with numerical examples and confirmed through sensitivity analysis.     

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

Function-on-function regression for assessing production quality in industrial manufacturing

Key responses of manufacturing processes are often represented by spatially or time-ordered data known as functional data. In practice, these are usually treated by extracting one or few representative scalar features from them to be used in the following analysis, with the risk of discarding relevant information available in the whole profile and of drawing only partial conclusions. To avoid that, new and more sophisticated methods can be retrieved from the functional data analysis (FDA) literature. In this work, that represents a contribution in the direction of integrating FDA methods into the manufacturing field, the use of function-on-function linear regression modelling is proposed. The approach is based on a finite-dimensional approximation of the regression coefficient function by means of two sets of basis functions, and two roughness penalties to control the degree of smoothness of the final estimator. The potential of the proposed method is demonstrated by applying it to a real-life case study in powder bed fusion additive manufacturing for metals to predict the mechanical properties of an additively manufactured artefact, given the particle size distribution of the powder used for its production.     

Application and modeling of resource service trust-QoS evaluation in manufacturing grid system

Resource scheduling is the bottleneck in MGrid; the current research on resource scheduling strategy is mainly based on resource performance-QoS (quality of service), but the factor of trust-QoS is ignored, which will result in unreasonable and unpractical scheduling results. In order to enhance the validity and success rate of resource scheduling in a manufacturing grid (MGrid) system, provide high credible resource service abilities and results to the user, the concept of resource service trust-QoS is presented; trust-QoS was introduced into MGrid resource service scheduling and the important roles it plays emphasized. The trust problems existing in the resource service transaction between resource service demanders (RSD) and resource service providers (RSP) are put forward. The trust-QoS relationship model which is capable of capturing a comprehensive range of trust relationships which exist in the MGrid system is put forward. Then a two-layer resource service trust-QoS evaluation model is put forward, including an intra-domain trust-QoS evaluation model and an inter-domain trust-QoS evaluation model. The quantitative evaluating algorithms of trust-QoS degree value are proposed and described in detail, as well as the value of real-time and dynamic updating algorithms of trust-QoS degree. Finally, an application prototype, namely MBRSPP-MGrid, is developed. The experimental results of the case study show that the proposed models and algorithms are effective and useful.     

Relative impact of different ERP forms on manufacturing organisations: an exploratory analysis of a global manufacturing survey

There are many types of enterprise resource planning (ERP) systems, ranging from very large and very functional vendor products such as those provided by SAP and Oracle, through in-house systems, and smaller vendor products. Thus, there is a substantial range of enterprise computing support available for manufacturing organisations and their manufacturing planning and control. The Global Manufacturing Research Group (GMRG) has collected a systematic survey of manufacturing organisations around the world, providing a picture of manufacturing operations. We have taken GMRG data and organised it around seven levels of ERP functionality, and analysed these data to determine effectiveness in terms of how ERP systems are used in global manufacturing firms, their role in accomplishing manufacturing planning and control, the relationship between ERP forms and data-management practices, the satisfaction firms have across ERP forms, and finally, relative perceived benefits and costs across ERP forms.     

A spread-sheet model for efficient production and scheduling of a manufacturing line/cell

In this paper we develop an efficient spread-sheet production planning/scheduling model for a resource-constraint production line or a manufacturing cell that produces several products but one at a time with significant changeover time and changeover cost. There are also management and physical constraints related to the operating hours, production capacity and amount of inventory allowed. The production line/cell supplies several products to customers who pull the products according to their own operating policy (working hours) that may be different from manufacture's operating hours. We also show several real-world applications and highlight the benefits and merits of the model.     

Design of cellular manufacturing system with quasi-dynamic dual resource using multi-objective GA

A new concept is presented in this paper of quasi-dynamic cell formation for the design of a cellular manufacturing system, based on analysing the fact that static and dynamic cell formation could not reflect the real situation of a modern cellular manufacturing system. Further, workforce resources are integrated into quasi-dynamic cell formation and thus a quasi-dynamic dual-resource cell-formation problem is proposed. For solving this problem, this paper first establishes a non-linear mixed integer programming model, where inter-cell and intra-cell material cost, machine relocation cost, worker operation time, loss in batch quality and worker salary are to be minimised. Then, a multi-objective GA is developed to solve this model. Finally, a real life case study is conducted to validate the proposed model and algorithm. The actual operation results show that the case enterprise significantly decreases its material handling cost and workforce number and obviously increases its product quality after carrying out the obtained scheme.     

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision’s potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.     

Competitive and sustainable manufacturing by means of ultra-efficient factories in urban surroundings

The rapid growth of the world population, the finiteness of resources on our planet Earth and the ongoing tendency towards urbanisation result in the need to question our actions and economic activities, also for industrial enterprises. A paradigm shift to decouple growth and resource utilisation is needed. By means of ultra-efficiency, factories are targeted, whose positive impact to their surrounding is optimised instead of only minimising negative influences. In the contribution, the concept of ultra-efficient factories and first tools for their implementation are introduced. Therefore, the term ultra-efficient factory is defined based on the state of the art. On this definition, spheres of activity for ultra-efficient factories under consideration of urban surroundings are determined. For the spheres of activity, criteria to assess the according maturity of companies are identified and aggregated into a comprehensive maturity model. The maturity model enables the determination of potentials for advancements of companies. To support the industrial application of the theoretic work, industrial best practices are determined and a visual tool is developed.     

焊缝金属奥氏体分解温度预测研究

根据182组实测焊缝金属奥氏体分解温度值,分别采用线性回归方法、非线性回归方法和人工神经网络技术建立了奥氏体分解温度的预测公式或模型。结果表明:线性回归公式难以准确体现各因素与奥氏体分解温度之间的关系,引入Mo指数和ln(t8/3)函数,预测精度有所提高;考虑了各因素之间交互作用的神经网络模型预测精度高于线性和非线性回归公式的,更适合于奥氏体分解温度预测研究。     

A framework for assessing the use of lean production practices in manufacturing cells

This study introduces a framework for assessing the use of lean production (LP) practices in manufacturing cells (MCs). The development of the framework included four stages: (a) defining LP practices applicable to MC, based on criteria such as the inclusion of practices that workers could observe, interact with and use on a daily basis; (b) defining attributes for each practice, emphasising the dimensions which were typical of their implementation in LP environments; (c) defining a set of evidence and sources of evidence for assessing the existence of each attribute–the sources of evidence included direct observations, analysis of documents, interviews and a feedback meeting to validate the assessment results with company representatives; (d) drawing up a model of the relationships among the LP practices, based on a survey with LP experts. This model supports the identification of improvement opportunities in MC performance based on the analysis of their interfaces. A case study of an MC from an automobile parts supplier is presented to illustrate the application of the framework.     

Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies

The adoption of Industry 4.0 technologies has been deemed as a strategy to increase product quality and make manufacturing processes more efficient. However, the way that these technologies are integrated into existing production systems and which processes they can support is still under investigation. Thus, this paper aims to examine the relationship between lean production (LP) practices and the implementation of Industry 4.0 in Brazilian manufacturing companies. To achieve that we use data from a survey carried out with 110 companies of different sizes and sectors, at different stages of LP implementation. Data collected were analysed by means of multivariate analysis. Our findings indicate that LP practices are positively associated with Industry 4.0 technologies and their concurrent implementation leads to larger performance improvements. Further, the contextual variables investigated do matter to this association, although not all aspects matter to the same extent and effect.     

Parts-mix analysis in recycling-oriented manufacturing systems for multiple-item production

One solution to environmental problems is to construct a recycling-oriented manufacturing system, in which waste products after consumption are collected, and reusable parts in the collected products are employed to produce new products. A parts-mix in the manufacturing system for multiple-item production is analysed by taking into consideration the merits and demerits of the usage of common parts over multiple products and special parts utilized for individual products. The parts-mix problem is formulated to minimize the life-cycle cost, which consists of production costs and recycling costs. The property of the optimal parts-mix is clarified, and a decision procedure to solve the parts-mix is developed. The procedure decides whether the common parts are effective or not.     

On the stability and bullwhip effect of a production and inventory control system

This paper focuses on the discrete-time automatic pipeline, inventory and order-based production control system (APIOBPCS), a well-established production and inventory control model. The feedback mechanism within the replenishment rule enables the model to mitigate the bullwhip effect, but introduces a stability problem. In this research, a comprehensive stability analysis is conducted for arbitrary lead times using difference equation theory. On the basis of stability, a state space approach is advocated to analyse the impact of replenishment parameters, demand processes, and lead times on the robustness of the bullwhip effect. The stability results demonstrate that the production control system can easily be destabilised without incorporating the work-in-progress (WIP) feedback loop. Furthermore, it reveals that the stability problem for long lead times can be simplified with the stability condition independent of the lead time. The results obtained in this study provide useful guidelines for the selection of replenishment parameters to guarantee stability and mitigate the bullwhip effect.     

Influencing factors of synchronization in manufacturing systems

Manufacturing systems exhibit two types of synchronisation phenomena: logistics and physics. Previous research has established synchronisation measures for both types and has shown that they are related to the due date performance. However, there is a lack of knowledge about the factors triggering synchronisation emergence as well as a holistic understanding of synchronisation effects on logistics performance. Thus, this research aims to further explore the relation between synchronisation, its influencing factors and its effect on logistics performance. Based on a profound literature review, we derive first hypotheses on the cause-and-effect-relationships between structural and dynamic properties of a manufacturing system and the emergence of logistics and physics synchronisation as well as logistics performance. By conducting a discrete-event simulation study on diverse manufacturing system types (line, flow shop and job shop production), we are able to test these hypotheses. We conclude that manufacturing network architecture as a structural property as well as processing time variability and system workload as dynamic properties may be exploited for an advanced and synchronisation-oriented manufacturing system design.     

云制造环境下板材余料资源的服务匹配方法

在云制造环境下,针对中小型企业加工后的板材余料造成极大资源浪费的问题,提出了一种基于语义相似度算法同时融入QoS(quality of service,服务质量)信息的分层次资源描述模型.首先,建立板材余料和板材加工设备资源的本体模型;然后,基于语义相似度算法对板材余料和板材加工设备的各个属性参数进行匹配,得到初选服务...