Towards intelligent manufacturing planning and control systems

In this paper, we review some well-known manufacturing planning and control (MPC) systems and models, and highlight both their advantages and major drawbacks. The analysis indicates that various important planning and control problems, as they arise in industry, are not properly addressed by current MPC systems. A well-known production system typology, illustrated by industrial examples, is briefly discussed to further highlight these planning and control problems. Next, we define a basic framework architecture for planning and control in both make-to-stock and make-to-order systems. The emphasis in this framework is on an integration of technological and logistics planning, and on an integration of capacity planning and materials coordination issues. In addition to this architecture, we further define an algorithmic framework that explicitly aims at the latter integration. To complete the architecture, we suggest a variety of procedures and algorithms to implement in the various modules.     

Modelling reconfigurable manufacturing systems with coloured timed Petri nets

Reconfigurable manufacturing systems (RMSs) have been acknowledged as a promising means of providing manufacturing companies with the required production capacities and capabilities. This is accomplished through reconfiguring system elements over time for a diverse set of individualised products often required in small quantities and with short delivery lead times. Recognising the importance of dynamic modelling and visualisation in decision-making support in RMSs and the limitations of current research, we propose in this paper to model RMSs with Petri net (PN) techniques with focus on the process of reconfiguring system elements while considering constraints and system performance. In view of the modelling challenges, including variety handling, production variation accommodation, machine selection, and constraint satisfaction, we develop a new formalism of coloured timed PNs. In conjunction with coloured tokens and timing in coloured and timed PNs, we also define a reconfiguration mechanism to meet modelling challenges. An application case from an electronics company producing mobile phone vibration motors is presented. Also reported are system analysis and application results, which show how the proposed formalism can be used in the reconfiguration decision making process.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

Value creation through design for scalability of reconfigurable manufacturing systems

Rapid and cost-effective scalability of the throughput of manufacturing systems is an invaluable feature for the management of manufacturing enterprises. System design for scalability allows the enterprise to build a manufacturing system to supply the current demand, and upgrade its throughput in the future, in a cost-effective manner, to meet possible higher market demand in a timely manner. To possess this capability, the manufacturing system must be designed at the outset for future expansions in its throughput to enable growths in supply exactly when needed by the market. A mathematical method that maximises the system throughput after reconfiguration is proposed, and an industrial case is presented to validate the method. The paper offers a set of principles for system design for scalability to guide designers of modern manufacturing systems.     

Integrated design approach for virtual production line-based reconfigurable manufacturing systems

Reconfigurable manufacturing systems (RMSs) have been recognized as a new manufacturing paradigm. In light of their enhanced flexibility and responsiveness, RMSs are considered to be mostly applicable to the very dynamic and unpredictable marketplaces of the near future. However, systematic approaches to the design and ramp-up of an RMS have not been well addressed. This paper presents a virtual production line-based (VPL) approach to the design and operation of a reconfigurable manufacturing system. Shop floor attributed finite-capacity automaton and VPL attributed finite-capacity automaton are proposed for modelling the control of an RMS, which leads to ease of control software development. Algorithms for balancing VPLs to maximize the productivity of an RMS are discussed. The results of simulation runs of the proposed methodology and algorithms applied to simplified back-end semiconductor manufacturing are provided.     

Optimal selection of module instances for modular products in reconfigurable manufacturing systems

A method for optimizing the variety of a modular products, manufactured in a Reconfigurable Manufacturing System, is proposed. The optimization is achieved through appropriately selecting the subsets of module instances from given sets. The problem is formulated as an integer nonlinear programming problem to find a trade-off between the quality loss due to modularity and the cost of reconfiguration for given sets of customer requirements. The proposed formulation is general in the sense that products can have any number of modules. The formulation is an extension to the available formulation that was developed for products with only two modules. Moreover, the current work addresses the effect of different order priorities, customer importance, and demands. The proposed method has been applied to a modular assembly problem and found to be efficient in determining optimum subsets of module instances.     

Delayed reconfigurable manufacturing system

Reconfigurable manufacturing systems (RMS) is a new manufacturing paradigm aiming at providing exactly functionality and capacity needed and exactly when needed. Reconfiguration is the main method to achieve this goal. But, the reconfiguration is an interruption to production activities causing production loss and system ramp-up problem and the ‘exact functionality’ may increase the reconfiguration efforts and aggravate the production loss and the ramp-up time. Therefore, a special RMS – delayed reconfigurable manufacturing system (D-RMS) is proposed to promote the practicality of RMS. Starting from the RMS built around part family with the characteristic of delayed differentiation, whose reconfiguration activities mainly occur in the latter stages of manufacturing system and the former stages have the potential to maintain partial production activities to reduce production loss during reconfiguration. Inspired from this, the basic structure of RMS is divided into two subsystems, subsystem 1 is capable of maintain partial production with a certain more functionality than needed, subsystem 2 reconfigure to provide exactly functionality and capacity of a specific part exactly when needed. And then, the benefits of D-RMS are analysed from inventory and ramp-up time aspects. Finally, a case study is presented to show the implementation process of D-RMS and validates the practicability of D-RMS.     

Investigation of the reconfigurable control system for an agile manufacturing cell

A new type of manufacturing cell, with characteristics of reconfigurability, reusability and scalability, needs to be developed. To achieve the agile reconfiguration of a manufacturing cell, the cell control system must be rapidly and efficiently generated or modified. In this paper, a multi-agent based architecture is defined that supports the design and implementation of highly reconfigurable control systems for agile manufacturing cells, which are comprised of resource agents (material processing agents, material handling agents, and material storage agents), a control agent, and an information agent, in order to reduce costs and increase the control system's agility with respect to the changing environment. Different agents in the cell control system can be organized dynamically, communicate with each other through messages, and cooperate with each other to perform flexibly the task in the cell control system. The structure of the agents is proposed and the message-passing between agents is discussed in detail.     

An intelligent cell control system for automated manufacturing

Cell control forms one level of a hierarchical approach to the control of automated manufacturing systems. This paper describes the application of the artificial intelligence techniques of blackboard and actor based systems for intelligent cell control in a framework termed Production Logistics and Timings Organizer (PLATO-Z). The blackboards required are described and the implementation is detailed. The implications of some practical considerations are also described.     

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.     

Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing

This paper deals with the design and loading of Cellular Reconfigurable Manufacturing Systems in the presence of alternative routing and multiple time periods. These systems consist of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools and Computer Numerical Control (CNC) machines. Each reconfigurable machine has a library of feasible auxiliary machine modules for achieving particular operational capabilities, while each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The proposed approach consists of two phases: the machine cell design phase which involves the grouping of machines into machine cells, and the cell loading phase that determines the routing mix and the tool and module allocation. In this paper, the cell design problem is modelled as an Integer Linear Programming formulation, considering the multiple process plans of each part type as if they were separate part types. Once the manufacturing cells are formed, a Mixed Integer Linear Programming model is developed for the cell loading problem, considering multi-period demands for the part types, and minimising transportation and holding costs while keeping the machine and cell utilisations in each period, and the system utilisation across periods, approximately balanced. An illustrative problem and experimental results are presented.     

Data envelopment analysis with multiple modes of functioning. Application to reconfigurable manufacturing systems

In principle, data envelopment analysis (DEA) does not consider the possibility, which can occur in practice, of a production system being able to operate in different modes of functioning. In this paper, a new DEA modelling approach is proposed in which the different modes of functioning are taken into account and included in the analysis. The observed input consumption and output production in each mode of functioning is used to derive a mode-specific technology. The overall DEA technology aggregates these mode-specific technologies according to their respective time allocations. The proposed model computes a target operating point for each mode of functioning so that the operation of the overall system is efficient. The proposed approach is applied to assess the technical, cost and allocative efficiency of a reconfigurable manufacturing system. The inputs considered are modules/tools usage, labour and energy consumption. The outputs are the number of units produced of each part type. The production possibility set is determined by previous observations of the system functioning, from which the best practices can be identified. Technical, cost and allocative efficiency scores can be computed. The proposed approach not only generates input cost savings but also lead time reductions.     

Switching mode control strategy in manufacturing execution systems

The paper describes a semi-heterarchical control solution for mixed planning and scheduling, routing and job execution in flexible manufacturing systems based on the paradigms of holonic manufacturing and product-driven automation. The main feature of the control solution is the bidirectional switching of the operating mode (scheduling, routing) between centralised and decentralised in the presence of perturbations to ensure as long as possible both global optimisation and agility to changes in batch orders, while featuring robustness to disturbances in the production environment. At the theoretical level, the control solution is described in terms of generic structural and dynamic models. The implementation is done using a multi-agent Java Agent Development Framework framework.     

Service-based manufacturing systems: modelling and control

In service-based manufacturing systems, functionalities are independently developed as services and a central engine orchestrates their integration. As industrial processes tend to be very large, and performance and productivity are expected to be maximised, there is a constant interest in providing (in-advance) quality guarantees for services interactions, which contrasts with the usual non-automated workflow design. This paper provides an alternative to enhance service orchestration capabilities using supervisory control techniques. Initially, each component (atomic and composite activities) belonging to an orchestration language is modelled as a state-machine. Then, activity models are properly combined and composed, reproducing orchestrated workflows. Finally, supervisory control is used to calculate an optimal version of the orchestrator. Practical implications of handling large state-spaces are discussed and examples are provided.     

Synthesis of control implementation for discrete manufacturing systems

The paper presents the concepts and steps required to synthesize a correct control implementation for discrete manufacturing systems, starting from Grafcet specifications. A formal framework implementing the synthesis steps is also presented and illustrated with an example of a drilling system.     

Scheduling manufacturing systems with work-in-process inventory control: single-part-type systems

A real-time algorithm is developed for scheduling single-part-type production lines with work-in-process inventory buffers. We consider three classes of activities: operations, failures and repairs, and starvation and blockage. The scheduling objectives are to keep the actual production close to the demand, the work-in-process (WIP) inventory level low, and the cycle time short. A three-level hierardhical controller is constructed to regulate the production. At the top level, we determine the desirable buffer sizes and the target production level for each operation. At the middle level is a production flow rate controller that recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. The production scheduling algorithm is evaluated by using computer simulations for a variety of cases. Compared with a transfer line policy, a significant improvement in system performance is observed.     

Scheduling manufacturing systems with work-in-process inventory control: Reentrant systems

In this paper, we propose a procedure for production flow control in reentrant manufacturing systems. The system under study consists ofN machines and producesM product types simultaneously. Each part goes through the system following a predefined process and may visit a machine many times. All machines are subject to random failures and need random repair times. The scheduling objectives are to keep the production close to demand and to keep the WIP inventory level and cycle times at low values. The model is motivated by semiconductor fabrication production. A three-level hierarchical controller is constructed to regulate the production. At the top level of this hierarchy, we perform capacity planning by selecting the desirable buffer sizes and the target production level for each operation. A production flow rate controller is at the middle level which recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. Comparison with alternative control is made through simulation and it shows that the control policy performs well.     

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.