Supporting rescheduling using CSP, RMS and POB—an example application

This paper introduces an innovative approach to the problem of rescheduling within manufacturing industry. An example of a manufacturing context that requires rescheduling capability is given (tyre production). The meaning of rescheduling, possible metrics for assessment of rescheduling and the advantages of applying the new techniques are reviewed. Of particular importance is the notion that the technology for providing rescheduling and explanation capabilities is to a large degree problem and context insensitive. The manner in which an original schedule has been created is irrelevant to the use of the technology described, allowing the advantages of the approach to be realized as an add-on facility to any existing scheduling system that fulfills a minimal set of requirements. These advantages are due to the use of a constraint based approach to new schedule creation used in tandem with dependency analysis techniques based on reason maintenance systems (de Kleer, 1986) and partial order backtracking (Ginsberg and McAllister, 1995; Spragg and Kelleher, 1996).     

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.     

Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops

In actual manufacturing processes, some unexpected disturbances, called as recessive disturbances (e.g., job set-up time variation and arrival time deviation), would gradually make the original production schedule obsolete. It is hard for production managers to perceive their presences. Thus, the impact of recessive disturbances can not be eliminated by rescheduling in time. On account of this, a rescheduling decision mechanism for recessive disturbances in RFID-driven job shops is proposed in this article, and a manifold learning method, which reduces the response time of manufacturing system, is applied in the mechanism to preprocess manufacturing data. The rescheduling decision mechanism is expected to answer the questions of whether to reschedule, when to reschedule, and which rescheduling method to be used. Firstly, RFID devices acquire the actual process completion time of all work in process (WIPs) at every WIP machining process completion time. Secondly, recessive disturbances are quantified to time accumulation error (TAE) which represents the difference between actual process completion time and planned process completion time. Lastly, according to the TAE and production managers’ experience, the rescheduling decision mechanism selects a proper rescheduling method to update or repair the original production schedule. The realization algorithms of rescheduling decision mechanism includes: (1) supervised locally linear embedding. (2) General regression neural network. (3) Least square-support vector Machine. Finally, a numerical experiment is used to demonstrate the implementation procedures of the rescheduling decision mechanism.     

A knowledge-based scheduling system for Emergency Departments

A knowledge-based reactive scheduling system is proposed to answer the requirements of Emergency Departments (EDs). The algorithm includes detailed patient priority, arrival time, flow time and doctor load. The main aim is to determine the patients who have higher priorities initially, and then minimize their waiting times. To achieve this aim, physicians and the other related workers can use an interactive system. In this study, we evaluated the existing system by comparing the proposed system. Also, reactive scheduling cases were evaluated for some items such as decreasing the number of doctors, changing durations and entering of an urgent patient to the system. All experiments were performed with proposed algorithm and right shift rescheduling approach.     

Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

Most semiconductor manufacturing systems (SMS) operate in a highly dynamic and unpredictable environment. The production rescheduling strategy addresses uncertainty and improves SMS performance. The rescheduling framework of SMS is presented as layered scheduling strategies with an optimization rescheduling decision mechanism. A fuzzy neural network (FNN) based rescheduling decision model is implemented which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to current system disturbances. The mapping between the input of FNN, such as disturbances, system state parameters, and the output of FNN, optimal rescheduling strategies, is constructed. An example of a semiconductor fabrication line in Shanghai is given. The experimental results demonstrate the effectiveness of proposed FNN-based rescheduling decision mechanism approach over the alternatives such as back-propagation neural network (BPNN) and multivariate regression (MR).     

基于损益云模型的制造车间重调度决策方法

针对制造车间重调度触发机制问题,建立了制造车间重调度损益函数,揭示了生产车间重调度过程损失及增益的变化规律.引入云理论测度重调度损益的不确定性,使用逆向云算法计算重调度增益云和损失云的数字特征,根据云形态预测重调度损益变化趋势.提出一种基于损益云模型的重调度决策方法以判断是否需要重调度,并利用最佳损益比甄选预调度方案以兼顾生产系统的稳定性和有效性.最后,通过实例验证了该方法的合理性和实用性.     

Simulation and knowledge based objects

A knowledge-based simulation model was developed by using a hypertext-like semi-object-oriented environment (SuperCard^TM). This environment enabled the building of a tightly coupled knowledge representation scheme and simulation algorithm. Our knowledge-based simulation model combines three different paradigms: production rules, an inference engine, and knowledge-based objects. The production rule paradigm is used as the basis for describing and coding the processes to be simulated. The knowledge-based objects are built as the active objects of the simulation. A knowledge-based object has a number of rules that support its behavior and an agenda that defines its goals. A knowledge-based object then proceeds in the simulation process (via the inference engine) until its goals are achieved. Since an inference engine with a knowledge base is built into the simulation process, such a system has the ability to reason about the behavior of the model. In addition, the knowledge-based simulation model has a friendly user interface. This enables changing the parameters of the simulation model without doing any programming. The inference engine in the knowledge-based simulation combines backward chaining, decision lattices, and links so as to produce an overall pattern of inference that is as efficient as possible.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems

Scheduling is a key component for performance guarantees in the case of distributed applications running in large scale heterogeneous environments. Another function of the scheduler in such system is the implementation of resilience mechanisms to cope with possible faults. In this case resilience is best approached using dedicated rescheduling mechanisms. The performance of rescheduling is very important in the context of large scale distributed systems and dynamic behavior. The paper proposes a generic rescheduling algorithm. The algorithm can use a wide variety of scheduling heuristics that can be selected by users in advance, depending on the system’s structure. The rescheduling component is designed as a middleware service that aims to increase the dependability of large scale distributed systems. The system was evaluated in a real-world implementation for a Grid system. The proposed approach supports fault tolerance and offers an improved mechanism for resource management. The evaluation of the proposed rescheduling algorithm was performed using modeling and simulation. We present experimental results confirming the performance and capabilities of the proposed rescheduling algorithm.     

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.     

A graphical interface for production and transportation system modeling: PTS

PTS is a graphical interface for production and transportation system modeling. It provides a means of creating an economic model of production and transportation activities in a system of plants and markets. The model created by PTS is translated into the GAMS language and is solved using that system. The results are then returned to PTS and are displayed graphically. PTS runs under Windows on IBM PCs and compatibles. PTS is used to (1) provide a graphical interface for knowledge-based model development systems, (2) provide a test-bed for studies of parallel model representations and (3) analyze methods of production and transportation model development which are simpler than the knowledge-based methods. The system is currently implemented for linear programming production and transportation problems. Also, the current implementation provides graphical and GAMS representations of the model.     

A knowledge-based decision support system for the management of parts and tools in FMS

Flexible manufacturing systems (FMS) are very complex systems with large part, tool, and information flows. The aim of this work is to develop a knowledge-based decision support system (KBDSS) for short-term scheduling in FMS strongly influenced by the tool management concept to provide a significant operational control tool for a wide range of machining cells, where a high level of flexibility is demanded, with benefits of more efficient cell utilization, greater tool flow control, and a dependable way of rapidly adjusting short-term production requirements. Development of a knowledge-based system to support the decision making process is justified by the inability of decision makers to diagnose efficiently many of the malfunctions that arise at machine, cell, and entire system levels during manufacturing. In this context, this paper proposes three knowledge-based models to ease the decision making process: an expert production scheduling system, a knowledge-based tool management decision support systems, and a tool management fault diagnosis system. The entire system has been created in a hierarchical manner and comprises more than 400 rules. The expert system (ES) was implemented in a commercial expert system shell, Knowledge Engineering System (KES) Production System (PS).     

A simulation-based scheduling system for real-time optimization and decision making support

This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.     

设备故障条件下柔性作业车间重调度方法

设备故障是生产实践中最为常见的一类不确定事件,它易对正常的生产计划造成影响。为了有效应对生产中的设备随机故障干扰,对设备随机故障条件下的柔性作业车间调度问题进行了研究,提出了一种基于组合策略的重调度方法。在重调度方法中,对重调度成本进行了系统分析并建立了重调度成本函数,设计了两种重调度策略,并结合免疫算法对遗传算法进行了改进,用于模型的求解计算。通过算例分析,验证了方法的可行性和有效性。实验结果表明,所提出方法能够更好地处理多种情况下的设备故障扰动。     

SmartGantt – An intelligent system for real time rescheduling based on relational reinforcement learning

With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.     

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

MRP rescheduling heuristics with capacity extension under deterministic demand

The multi-stage, multi-machine capacitated lot-sizing problem (MSMMCLSP) consists of scheduling the production of one product in a multi-machine production system with a multi-stage structure. Machine capacity is reserved for the production of the end item but can be extended by working overtime (overtime capacity). When a lot size is positive in a specific period, it can be loaded on all machines without exceeding the sum of the regular and overtime capacity limits. In previous research [Rong, C., Takahashi, K., & Morikawa, K. (2005). A new heuristic method for capacitated lot-sizing in multi-stage, multi-machine production systems. Journal of Japan Industrial Management Association, 56(5), in press], we have proposed a rescheduling heuristic to determine a feasible and sufficient solution for the MSMMCLSP with only regular capacity limits, in comparison with Franca et al.’s heuristic method. However, the mechanism of rescheduling in regular time has not optimum (or near optimum) performance for the MSMMCLSP with capacity extension. Preliminary examination revealed that overtime production also has situation-dependent advantages and disadvantages with the combination of various planning parameters. This study, therefore, develops an integrated heuristic that guarantees an optimal solution for general MSMMCLSP by combining the rescheduling mechanism and overtime production. We also evaluate the effectiveness of the proposed heuristics by using computational tests with various planning parameters.     

Towards the formal specification of an OPS5 production system architecture

The article presents a formal specification for many important aspects of the OPS5 production systems framework. the article illustrates how an abstract formal specification of a production system can be created and the benefits this provides to those involved in the development of knowledge-based systems. the formal specification is preceded by an informal specification of a production system upon which the formal model is based and the development is illustrated through the use of concrete examples. the notation used is that of “Z” (J. M. Spivey, The Z Notation, Prentice-Hall, Englewood Cliffs, NJ, 1990), a language based upon typed set theory. This language has been used to success in the specification of critical conventional software systems (I. Hayes, Technical Monograph PRG-46, Oxford University Computing Laboratory, Oxford, England, 1985) and which is formal enough to allow for the creation of rigorous specifications, yet is of a form that makes these specifications “readable.” the aim of the article is to show that formal techniques can be applied to areas of knowledge-based system development, thus promoting correctness, reliability, and understanding. © 1994 John Wiley & Sons, Inc.     

基于人机交互的炼钢连铸动态调度*

针对炼钢连铸动态调度问题,建立了问题的优化模型、设计了约束满足求解算法并分析了算法复杂度、开发了炼钢连铸动态调度的人机交互系统。当生产过程中的扰动事件发生时,系统能够通过人机交互并结合优化模型和多项式时间复杂度的算法获得可行且与原调度尽量一致的新调度方案,以确保动态调度前后整个生产过程的连续性和稳定性。     

Developing Knowledge-Based Intelligent Multimedia Tutoring Systems Using Semantic Content-Based Modelling

The complex nature of semantic video and audio content has made multimedia difficult to use within the traditional architecture of a knowledge-based intelligent tutoring system (ITS). This paper investigates how multimedia syntax and semantics may be integrated into the confines of the knowledge-based ITS architecture so as to develop a knowledge-based intelligent multimedia tutoring system (IMTS). The paper contributes a semantic content-based model that can be used for this purpose. Multimedia frames (m-frames) are used as the framework by which the syntactic and semantic information is represented within the model. The paper discusses how IMTSs may be developed using the model, and then demonstrates the use of our approach in the development of ARISTOTLE, an IMTS for teaching young children basic zoology.