Distributed intelligent control of exceptions in reconfigurable manufacturing systems

In order to react to the continuous and unpredictable changes in product demand, in product variety, and in process technologies, reconfigurable manufacturing systems allow quick adjustment of production capacity and functionality by rearranging or changing their modular components. In this kind of system, operation management issues, such as exception handling policies, become more complex since correct reconfiguration strategies have to be selected. This paper explores the potential of the reconfigurability feature to be a basis for the development of new strategies to handle out-of-the-ordinary events in the production process; in particular, maintaining production flow when machine breakdowns occur. Decisions regarding how to deal with exceptions to the production process are complex and depend on the manufacturing system configuration and on many performance and economic variables. The authors propose agent-based manufacturing control for exception handling because of its ability to be very agile, as well as being reactive and efficient. Manufacturing agents, while working to pursue their specific goals, achieve the global target of the system. Complex decisions can be made due to the synergy arising from the agents' internal reasoning and the negotiation process among these agents. The adopted negotiation mechanism is based on the contract-net protocol, while different strategies have been designed for the internal reasoning. The authors demonstrate that, under certain conditions, an agent's internal strategies based on fuzzy reasoning improve the global performance of the system. The proposed control model has been tested on a discrete event simulation test-bed.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation.     

Coordination activities of human planners during rescheduling: case analysis and event handling procedure

This paper addresses the process of event handling and rescheduling in manufacturing practice. Firms are confronted with many diverse events, such as new or changed orders, machine breakdowns, and material shortages. These events influence the feasibility and optimality of schedules, and thus induce rescheduling. In many manufacturing firms, schedules are created by several human planners. Coordination between them is needed to respond to events adequately. In this paper, the practice of coordination during event handling is analysed by an extensive case study. The study shows that human planners spend much time in communicating events and in negotiating rescheduling solutions. Because many events demand a quick response, the possibilities for coordination are restricted by time constraints. The paper proposes a procedure to structure the event handling process. This procedure helps a scheduler to select an appropriate response to an event by evaluating its influence on schedule feasibility and the time available for coordination and rescheduling. The use of the procedure in the case company has led to improved rescheduling performance through a reduction of scheduler interactions and increasing coordination efficiency. The procedure contributes to traditional planning frameworks and paradigms, and supports the conscious selection and use of rescheduling methods in manufacturing practice.     

Solving the machine-loading problem in a flexible manufacturing system using a combinatorial auction-based approach

The next generation manufacturing system is conceived to be intelligent enough to take decisions and automatically adjust itself to situations such as variations in production demand and machine breakdowns. The manufacturing control system must have the intelligence to ensure real time operational control by interacting with different manufacturing subsystems. One of the prominent methodologies to deal with the problem of distributed manufacturing systems is the auction-based heuristic control strategy in which various entities bid themselves, accept bids and make selection amongst bids. The present paper addresses the flexible manufacturing system machine-loading problem where job selection and operation allocation on machines are to be performed such that there is a minimization of system unbalance and a maximization of throughput. The methodology of winner determination using the combinatorial auction process is employed to solve the flexible manufacturing system machine-loading problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way to enhance the efficiency of allocating the assets. The performance of the proposed approach is tested on 10 sample problems and the results thus obtained are compared with the existing models in the literature.     

A mathematical programming model for reconfiguration of flexible manufacturing cells

In flexible manufacturing cells, changes in demand size, product mix, part variety, existing routings and set-up/operation times may require reconfiguration of the cells. In this study, an approach is developed to decide when and how such a reconfiguration should be carried out for existing cells. This study considers reconfiguration in terms of changing part routings, adding a new machine type in a cell, duplicating an existing machine type, removing an existing machine from a cell and transferring a machine to another cell. The study also shows the total number of tools of each type on each machine located in each cell after reconfiguration. To make the optimal reconfiguration decisions, a mathematical programming model to minimize the total reconfiguration cost is developed. The developed model considers the lower and upper bounds on machine utilizations and the time limits on machine cycle times to decide when to reconfigure the system.     

Performance evaluation of an auction-based manufacturing system using coloured Petri nets

The next generation of manufacturing systems is assumed to be intelligent enough to make decisions and automatically adjust to variations in production demand, shop-floor breakdowns etc. Auction-based manufacturing is a control strategy in which various intelligent entities in the manufacturing system bid themselves, accept bids and make selections among the bids available based on a heuristic. This paper deals with the simulation modelling and performance evaluation of a push-type auction (negotiation) based manufacturing system embedded in a pulltype production system using coloured Petri nets. Three different models of an auction-based manufacturing system have been discussed. This methodology helps in developing systems for real-time control, anticipation of deadlocks, and evaluation of various performance metrics like machine utilization, automated guided vehicle (AGV) utilization, waiting times, work in process (WIP) etc. Various decision-making rules were identified for the real-time control of auction-based manufacturing systems.     

云制造环境下制造设备云服务异常处理模型

保障云制造环境下制造活动的顺利开展,提高云服务的质量,提出一种制造设备云服务异常处理模型。该模型在对制造设备云服务可能产生的异常进行分类和抽象的基础上,通过监控逻辑监控和捕获产生的异常,通过处理逻辑对异常进行处理;给出了7种异常的处理流程,设计了仿真实验,实验结果验证了模型的可用性和有效性。     

Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems

The primary objective of this paper is to compare five rescheduling strategies according to their effectiveness in reducing entropic-related complexity arising from machine breakdowns in manufacturing systems. Entropic-related complexity is the expected amount of information required to describe the state of the system. Previous case studies carried out by the authors have guided computer simulations, which were carried out in Arena 5.0 in combination with MS Excel. Simulation performance is measured by: (1) entropic-related complexity measures, which quantify: (a) the complexity associated with the information content of schedules, and (b) the complexity associated with the variations between schedules; and (2) mean flow time. The results highlight two main points: (a) the importance of reducing unbalanced machine workloads by using the least utilised machine to process the jobs affected by machine breakdowns, and (b) low disruption strategies are effective at reducing entropic-related complexity; this means that applying rescheduling strategies in order to manage complexity can be beneficial up to a point, which, in low disruption strategies, is included in their threshold conditions. The contribution of this paper is two-fold. First, it extends the application of entropic-related complexity to every schedule generated through rescheduling, whereas previous work only applied it to the original schedule. Second, recommendations are proposed to schedulers for improving their rescheduling practice in the face of machine breakdowns. Those recommendations vary according to the manufacturing organisations’ product type and scheduling objectives. Further work includes: (a) preparing a detailed workbook to measure entropic-related complexity at shop-floor level; and (b) extending the analysis to other types of disturbances, such as customer changes.     

Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs)

A Reconfigurable Manufacturing System (RMS) is a new paradigm that focuses on manufacturing a high variety of products at the same system. Having specified a design strategy for an RMS as the first design step at the tactical level, products must be grouped to identify and allocate corresponding manufacturing facilities. An interface between market and manufacturing called reconfiguration link is presented to specify and arrange products for manufacturing. The reconfiguration link incorporates the tasks of determining the products in the production range, grouping them into families and selecting the appropriate family at each configuration stage. The proposed approach of (re)configuring products before manufacturing facilitates assigning product families to the required manufacturing facilities in terms of (re)configuring manufacturing systems. This paper contributes an overall approach of grouping products into families based on operational similarities, when machines are still not identified. Since the problem of product family selection consists of quantitative and qualitative objectives, the Analytical Hierarchical Process (AHP) is then used while considering both market and manufacturing requirements. The AHP model is verified in an industrial case study through using Expert Choice software. The solutions take advantage of monitoring sensitivity analysis while changing the priorities of manufacturing and/or market criteria. The concept of the proposed model is generic in structure and applicable to many firms. However, the model must be adapted according to the specific nature of the company under study. For instance, product family choices may differ from one company to another because of the available technology and the volume and type of existing products in the production range.     

Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions

In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.     

Dynamic co-ordinated scheduling in the supply chain considering flexible routes

In Supply chain (SC) environments, schedules inevitably experience various unexpected disruptions. In these cases, rescheduling is essential to minimise the negative impact on the performance of the system. In this study, a hybrid rescheduling technique is developed for solving coordinated manufacturing tasks scheduling problems with due date changes and machine breakdowns. According to the dynamic features of this problem, a strategy combined with event and periodic driven methods is proposed to improve the stability and robustness of manufacturing performance in a coordinated SC. Moreover, an application case is introduced to test and evaluate the effect of different initialisations in two types of disruption scenarios. The experimental results show that the proposed rescheduling technique has good effectiveness and efficiency in the coordinated manufacturing environment.     

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.     

Optimization-based parts-machines matching in flexible manufacturing systems

A new dynamic scheduling strategy, Parts-Machines Matching (PMM), is developed and tested in simulated flexible manufacturing systems. This strategy is aimed to achieve globally optimal matching between parts and machines by a semi-qualitative optimization algorithm, originally developed for the Stable Marriage Problem. Global and Partial implementations of PMM are presented and compared with other conventional part-flow rules. They are found to achieve better shop performance than conventional rules, in terms of system throughput, robustness against travel time uncertainties, and recovery from machine breakdowns. The prospect of bringing about system-wide optimization-based performance improvements into bidding schemes makes the proposed framework very significant.     

Incorporating dynamism in traditional machine loading problem: an AI-based optimisation approach

Machine breakdowns have been recognised in flexible manufacturing systems (FMS) as the most undesirable characteristic adversely affecting the overall efficiency. In order to ameliorate product quality and productivity of FMS, it is necessary to analyse, as well as to minimise the effect of breakdowns on the objective measures of various decision problems. This paper addresses the machine loading problem of FMS with a view to maximise the throughput and minimise the system unbalance and makespan. Moreover, insufficient work has been done in the domain of machine loading problem that considers effect of breakdowns. This motivation resulted in a potential model in this paper that minimises the effect of breakdowns so that profitability can be augmented. The present work employs an on-line machine monitoring scheme and an off-line machine monitoring scheme in conjunction with reloading of part types to cope with the breakdowns. The proposed model bears similarity with the dynamic environment of FMS, hence, termed as the dynamic machine loading problem. Furthermore, to examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on different dataset from previous literature has been investigated with application of intelligence techniques such as genetic algorithms (GA), simulated annealing (SA) and artificial immune systems (AIS). The results incurred under breakdowns validate the robustness of the developed model for dynamic ambient of FMS.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Reliability analysis of a flexible manufacturing cell

In this study, mathematical models are developed to study and compare the operations of a fully reliable and an unreliable flexible manufacturing cell (FMC), each with a flexible machine, a loading/unloading robot, and a pallet handling system. The operation times, loading/unloading times, and material handling times by the pallet are assumed to be random. The operation of the reliable cell is compared to that of an unreliable cell with respect to utilization of the cell components, including the machine, robot, and pallet handling system. The unreliable cell is assumed to operate under random (machine and robot) failures with constant failure rates for the machine and the robot. The pallet handling system is assumed to be completely reliable.     

工作流系统异常处理实现方法

随着业务过程的不断复杂和多变,工作流管理系统需要提供一套有效的异常处理机制应对由此而带来的各类异常情况。在分析工作流系统异常的基础上,结合高级编程语言的异常处理方法、工作流任务的事务特征和组织层次的异常处理协调三个方面,提出了一种工作流系统异常处理的实现方法。     

Performance evaluation for tandem multi-factory supply chains: an approximate solution

Predicting the performance of multistage manufacturing systems is usually viewed as challenging because of unexpected machine breakdowns, random processing times, uncertain inter-factory transportation times, etc. In this paper, the authors formulate an approximate model for the tandem manufacturing systems, where the inventory in each buffer is monitored based on the (s, Q) discipline. This model divides a multistage system into a series of primitive line segments, each of which is characterised by a continuous time discrete state Markov process. The model may be applied in two types of systems: (1) tandem flow lines with batch processing and (2) multi-factory manufacturing supply chain, where inter-factory material transportation is required. Based on the model, a number of commonly used performance measures, including throughput, inventory, transportation frequency, etc., can be estimated. These estimates may enable manufacturers to evaluate the performance of the systems, and hence improve the management of production and inventory.     

Intelligent dispatching for flexible manufacturing

A decision rule for real-time dispatching of parts, each of which may have alternative processing possibilities, has been developed and tested in a simulated flexible manufacturing system. A part, upon completion of an operation, is not routed to a specific machine, but is, in effect, sent to a general queue. Thus, a machine has a global option for choosing parts which in turn may be processed on alternative machines. For effective use of the system's routeing flexibility under these circumstances, the machine needs an intelligent part-selection strategy (rather than shallow heuristics represented by the conventional dispatching rules) that takes into account the current state and trends of the system. The proposed intelligent reasoning procedure has been found to achieve better shop performance than some of the popular dispatching rules, the improved performance being due to the ability to respond to changing circumstances.