A multi-plant tolerance allocation model for products manufactured in a multi-plant collaborative manufacturing environment

To produce an assembled product, the components need to be manufactured within the allocated tolerances such that the assembly operations can be performed to produce the final product. In a collaborative manufacturing environment, the components of a product may be manufactured at different plants distributed at various locations. There are different manufacturing operations with various process capabilities at the multiple plants. If the components are distributed to the manufacturing operations at the different plants, the working tolerances of the components may be allocated differently. In this research, a multi-plant tolerance allocation model is presented. At the first stage, given the blueprint dimensions and tolerances of a designed product, a cross-plant tolerance allocation is performed to determine the working tolerance of each of the components by considering all the feasible manufacturing operations of the available plants. The primary objective at the first stage is to maximize the cumulative sum of working tolerances. At the second stage, the components are assigned to the suitable plants to perform the required manufacturing operations. A mathematical programming model is presented to distribute the components to the suitable plants to achieve the objective of minimizing multi-plant manufacturing costs. As a result, the working tolerances can be allocated for the components with suitable manufacturing operations to achieve the tolerance objective. In addition, the manufacturing operations for the components can be performed at the suitable plants with optimized multi-plant manufacturing cost objectives. An example product is demonstrated and discussed.     

Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.     

Applying case-based reasoning in assembly sequence planning

Assembly sequence planning (ASP) is the foundation of the assembly process planning which plays a key role in the whole product life cycle. In this paper, a unique ASP reasoning method supported by the artificial intelligent technique of case-based reasoning (CBR) is proposed and developed. First, based on the previous ASP literatures review and the CBR characteristics analysis, the systematic architecture of the CBR based ASP is presented. Then, some key techniques including assembly case modelling, similar case retrieving, case based reasoning, and case base maintenance, etc., are explored thoroughly. To enhance the efficiency and quality of the reasoning process, genetic algorithm (GA) is designed and applied to automatically inferring of the reference assembly sequence. Finally, the corresponding software system with an engineering example is given to demonstrate the effectiveness of the CBR based ASP.     

A framework for collaborative planning and state-of-the-art

Inter-organizational supply chain management incurs the challenge to align the activities of all members which contribute to the value creation of a product or service offered to customers. In general, a supply chain faces the “problem” of information asymmetry, members having their own objectives and constraints which may be in conflict with those of the other members. Still, activities have to be aligned in such a way that the supply chain as a whole stays or becomes competitive while each member wins by cooperating. A number of collaborative planning schemes have been put forward in the last two decades with different assumptions and different areas of application. This paper intends to provide a framework and an overview on the state-of-the-art of collaborative planning. The criteria of the framework will allow us to position existing concepts and to identify areas where more research is needed. The focus of the literature reviewed here will be on model-based decision support at the operational planning level.     

Integrated assembly and disassembly sequence planning using a GA approach

In a product life cycle, an assembly sequence is required to produce a new product at the start, whereas a disassembly sequence is needed at the end. In typical assembly and disassembly sequence planning approaches, the two are performed as two independent tasks. In this way, a good assembly sequence may contradict the cost considerations in the disassembly sequence, and vice versa. In this research, an integrated assembly and disassembly sequence planning model is presented. First, an assembly precedence graph (APG) and a disassembly precedence graph (DPG) are modelled. The two graphs are transformed into an assembly precedence matrix (APM) and a disassembly precedence matrix (DPM). Second, a two-loop genetic algorithm (GA) method is applied to generate and evaluate the solutions. The outer loop of the GA method performs assembly sequence planning. In the inner loop, the reverse order of the assembly sequence solution is used as the initial solution for disassembly sequence planning. A cost objective by integrating the assembly costs and disassembly costs is formulated as the fitness function. The test results show that the developed method using the GA approach is suitable and efficient for the integrated assembly and disassembly sequence planning. Example products are demonstrated and discussed.     

Design for manufacturing: application of collaborative multidisciplinary decision-making methodology

Design for manufacturing is often difficult for mechanical parts, since significant manufacturing knowledge is required to adjust part designs for manufacturability. The traditional trial-and-error approach usually leads to expensive iterations and compromises the quality of the final design. The authors believe the appropriate way to handle product design for manufacturing problems is not to formulate a large design problem that exhaustively incorporates design and manufacturing issues, but to separate the design and manufacturing activities and provide support for collaboration between engineering teams. In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem. First, the compromise Decision Support Problem is used as a mathematical model of each engineering teams’ design decisions and as a medium for information exchange. Second, game-theoretic principles are employed to resolve couplings or interactions between the teams’ decisions. Third, design-capability indices are used to maintain design freedom at the early stages of product realization in order to accommodate unexpected downstream design changes. A plastic robot-arm design and manufacturing scenario is presented to demonstrate the application of this methodology and its effectiveness for solving a complex design for manufacturing problem in a streamlined manner, with minimal expensive iterations.     

Negotiation-based collaborative planning in divergent two-tier supply chains

Advanced Planning Systems are based on the principles of hierarchical planning, which—at least at the top level—grounds on centralized planning. However, central coordination requires access to all relevant information and the power to impose planning results on all organizational units. In consequence it can be realized only for parts of an inter-organizational supply chain, and the question arises whether there exist alternate ways to achieve coordination. In this paper we describe a non-hierarchical, negotiation-based process, which can be used to synchronize plans between independent partners of a two-tier supply chain consisting of one supplier and several buyers. Assuming that all partners generate plans based upon mathematical programming—as in most Advanced Planning Systems at the master planning level—we show how modified versions of these models can be utilized to support the negotiation process by evaluating given purchasing orders or supplies and by generating counter-proposals. Resulting is an iterative, negotiation-like scheme, which establishes and subsequently improves a consistent overall plan based on a limited exchange of information between the supply chain partners.     

Allocation of manufacturers through internet-based collaboration for distributed process planning

The pursuit of lower cost, shorter time-to-market, and better quality has led to a shift toward global production in today's competitive business environment. This shift however, forces manufacturing enterprises to have separate design houses and manufacturing facilities. In general, design houses are located in the same regions as customers to enable them to respond to the rapidly changing demands of customers. By contrast, manufacturing facilities can be placed in regions in which production costs are lower. However, this physical and logical separation between designers and manufacturers (or between upstream manufacturers and downstream manufacturers) raises various integration issues. The present paper addresses two of these issues: the framework for representing the data necessary to communicate requirements and objectives of the designer, and the methodology for utilizing such data to optimize the business objectives related to production cost and quality. The proposed representation, collaboration framework, and methodology will enable design houses and manufacturing facilities to realize the benefits of global production and to accommodate the management of loosely integrated supply chains.     

The fit of planning methods for the part manufacturing industry,a case study

This paper investigates the specific planning environment of an automotive part manufacturer and characterizes this environment based on variables regarding the product,its demand and the manufacturing process.The fit of different planning methods,namely material requirements planning,reorder point,cyclic planning,kanban,and heijunka to this specific planning environment is analyzed and evaluated by exploratory research.The results show that different product characteristics make it hard to apply a single planning method.None of the investigated methods show the ability to cover the complex requirements of a combined planning environment of two partly divergent product types with a deep value chain.     

Production planning optimisation for composite aerospace manufacturing

Composite materials proved highly successful for aerospace applications in the last decades, but increased cost pressure forces the composite industry to become more efficient. This requires new manufacturing technologies and optimised processes as raw material costs and labour costs are basically fixed when wanting to keep production on site. Probably the most defining process for aerospace composite production is manual layup of prepreg material with subsequent curing in an autoclave. From production planning view, this combination poses the challenge of transition from discrete layup manufacturing to batch curing processing with the restriction of limited allowed storage time of the prepreg material prior to cure. In this paper, a new approach for production order optimisation at the conjunction of discrete and batch processing is presented. The APOLLO named tool is designed to decrease throughput times, streamline production and increase autoclave utilisation in the composite aerospace industry.     

Discrete electromagnetism-like mechanism algorithm for assembly sequences planning

Assembly sequence planning (ASP) plays an important role in digital manufacturing. It is a combinatorial optimisation problem with strong constraints aiming to work out a specific sequence to assemble together all components of a product. The connector-based ASP, which uses the connector to simplify the complex assembly problem, is one of the most important and hardest types. In order to solve this problem effectively, a discrete electromagnetism-like mechanism (DEM) algorithm is proposed. A charge formula and a force formula are redefined in DEM algorithm. An adjacency list is applied to handle the precedence relationship and prevent infeasible solutions. Two movements based on path relinking are employed. Moreover, with two different guided mutations, the population diversity can be guaranteed. Five examples are used to test and evaluate the performance of DEM. The comparisons among the proposed DEM, traditional genetic algorithms (GAs), guided GAs, memetic algorithms and artificial immune systems show that DEM outperforms among these algorithms in terms of running time, computation accuracy, convergence speed and parameter robustness.     

Model for flexibility evaluation in manufacturing network strategic planning

In response to market pressures, manufacturers have adopted different approaches to provide flexibility regarding several aspects. In this paper, we suggest a model for the evaluation of the flexibility of the manufacturing supply chain, based on graph theory techniques. This model defines maximum excess demand that may be met using flexibility. Recourse to flexibility enablers is determined based on cost minimisation. Such enablers are volume flexibility, mix flexibility and safety stocks. The proposed model is solved using a two-step Mix Integer Linear Programme; the first step consists in defining maximum demand that may be met while the second step concerns minimising cost. The main benefit of our model is to deal with realistic problems in a rather short time. Therefore, it can be used in a wide ‘what-if’ design process. It means evaluating various contemplated flexibility configurations in multiple demand scenarios in order to choose the best option. It can be also used during operational supply chain planning in order to face to an unbalanced situation. This paper ends with a numerical example illustrating our model’s efficiency.     

Capacity planning with reconfigurable kits in semiconductor test manufacturing

To test an integrated circuit (IC) product, both a tester and qualified kits are simultaneously needed. One kit usually consists of six or seven components and is somewhat like a fixture. A remarkable amount of investment will be saved if one allows kit reconfigurations and purchase individual components instead of using entire kits. Unfortunately, this approach of the reconfigurable kit also increase exponentially the complexity of kit management and thus capacity planning due to intricate {product, tester, kit, component} qualification relationships. The paper describes the Automated Capacity Allocation System (ACAS) developed at Intel Shanghai for generating an optimal capacity and kit-allocation plan for a 9-week horizon. It performs optimization at the component level using Mixed-Integer Programming (MIP) technology. High Buffer Formulation and Tight Workload Formulation are introduced to determine the number of kits needed to guarantee a feasible capacity plan. Detailed analyses of parameter setting, objective prioritizing and formulation comparison were also conducted. With the implementation of the ACAS at Intel, we have already realized millions of dollars in savings from kit purchasing and about 90% man-hour savings in the capacity planning.     

Virtual fusion: a hybrid environment for improved commissioning in manufacturing systems

Efficiency and quality are major factors contributing to profits in manufacturing systems. Production downtime occurs during commissioning of a new system, adoption of new processes, system faults, or (un)planned maintenance; all of which result in reduced production and profit loss. Current techniques for evaluating change to a manufacturing system rely on simulation and modeling to verify processes, but ignore the physical interactions of the work parts on the system. Implementation techniques to evaluate commissioning focus on identifying issues with the cyber interfaces, ignoring the physical interfaces. To validate the cyber and physical interfaces simultaneously, physical work are sent through the system, resulting in significant costs from scrapped work parts and loss of production time. This research proposes a virtual fusion environment where the physical interfaces between a virtual work part and a manufacturing system can be investigated in real-time, on the physical system, without the expenses associated with physical work parts. The virtual environment includes a virtual fusion filter to monitor discrepancies between the physical and virtual systems, and generate a hybrid virtual-physical input signal to the system level controller for virtualisation of a work part onto a physical system. Experimental demonstrations validate the feasibility of the proposed approach.     

Weaving an agent-based Semantic Grid for distributed collaborative manufacturing

The development and maintenance of distributed collaborative manufacturing systems is faced with increasing challenges caused by the technical difficulties and ontological issues in distributed computing, resource integration and knowledge sharing over heterogeneous computing platforms. This paper presents an agent-based Semantic Grid for distributed manufacturing collaboration across ubiquitous virtual enterprises. A hierarchy of autonomous, adaptive agents forming a ‘super peer’ based peer-to-peer network is laid out in the Semantic Grid to provide, in an open, dynamic, loosely coupled and scalable manner, the service publication, discovery and reuse for management of applications that need to utilise Grid resources. The operation and experimental evaluation of the system are presented to validate the implementation of the proposed approach.     

More MILP models for integrated process planning and scheduling

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.     

Integrated planning for product module selection and assembly line design/reconfiguration

To take full advantage of product modularity, modular product design and assembly system design/sreconfiguration have to be simultaneously addressed. The emerging reconfigurable production and flexible assembly techniques have made such an integrated approach possible. As such, this paper proposes an integrated approach to product module selection and assembly line design/reconfiguration problems. It further suggests that quality loss functions be used in a generic sense to quantify non-comparable and possibly conflicting performance criteria involved in the integrated problem. The complexity of the problem precludes the use of commercial software for solving meaningful sized problems in polynomial time. A genetic algorithm is therefore developed to provide quick solutions. An example problem is solved to illustrate the application of the proposed approach. Based on 72 randomly generated test problems, ANOVA analysis is further carried out to investigate the effects of genetic algorithm parameters. The convergence behaviour of the search processes is also examined by solving large problems with different numbers of operations and product modules.     

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

The implementation of an activity-based costing collaborative planning system for semiconductor backend production

While the market turns to an environment with low profit margins for semiconductor backend operations, it is hard for an independent firm to survive today. Forming strategic alliances or integrating an enterprise's internal firms by means of collaborative planning/operations to gain competitive advantage is inevitable. This paper presents the development of an activity-based costing collaborative production planning system (ABC/CPPS) to help production planners to estimate the manufacturing profit of semiconductor backend turnkey (combined IC assembly and testing) operational services at the early stage of order release to production line in a collaborative context. The estimation is under the real constraints of production resources. A predicate/transition net (Pr/Tr net) is used to simulate and implement the activity-based costing (ABC) model with the dynamic characteristics of a production line incorporated. A financial measure, profit, is used to supplement and indicate the consequence of the planning result and link the view to the enterprise's financial vision.