SCHEDULE EXECUTION IN HOLONIC MANUFACTURING SYSTEMS Luc Bongaerts, Paul Valckenaers, Hendrik Van Brussel, Patrick Peeters Accepted for the 29th CIRP International Seminar on Manufacturing Systems, May 11-13, 1997, Osaka University, Japan
SCHEDULE EXECUTION IN HOLONIC MANUFACTURING SYSTEMS Luc Bongaerts, Paul Valckenaers, Hendrik Van Brussel, Patrick Peeters Katholieke Universiteit Leuven - Mechanical Engineering Department Celestijnenlaan 300B, B-3001 Leuven, Belgium e-mail:
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ABSTRACT: To bridge the gap between advanced scheduling research and industrial practice, high performance scheduling needs to be combined with reactivity to disturbances. This paper presents an architecture for shop floor control (SFC) that builds upon the concepts of holonic manufacturing and combines the advantages of traditional hierarchical control with the benefits of distributed control. The architecture supports the use of several control strategies, like hierarchical control, heterarchical control and other holonic control strategies. These intermediate strategies are based on a tight co-operation between scheduler and SFC holon. Keywords: Holonic manufacturing, shop floor control, holonic architecture, reactive scheduling 1. INTRODUCTION It is widely understood that there exists a wide gap between research and industrial practice in scheduling and shop floor control (SFC) (Parunak, 1991). During several years, the calculation of near-optimal schedules, preferably on-line, has been the primary challenge for researchers. However, in industry, high performance schedulers currently are hardly used, due to the lack of schedule robustness. When a near-optimal schedule is executed on the shop floor, several disturbances occur. It turns out that the performance of a schedule is very sensible to these disturbances and it is difficult to execute that schedule (Parunak, 1991). Investigations in industry have show that 20% to 30% of the work is done on other equipment than originally planned (Lampkemeyer et al., 1991). Several approaches aim to bridge this gap, based on reactive scheduling, heterarchical control or industrial practice. Researchers focusing on reactive scheduling concentrate on the use of fast scheduling algorithms. Some more intelligent approaches adapt the schedule to the feedback of the shop floor (Kimemia and Gerschwin, 1981; Bispo et al., 1992; Karsiti et al., 1992; Luh and Czerwinsky, 1994). Since many of these approaches are built according to a strict multi-level hierarchy (Bauer et al., 1991), they turn out to be very rigid and fail to react properly to all disturbances. Other researchers bypass the problems of reactive scheduling, at the cost of lower performance, e.g., by using
heterarchical control (Dilts et al. 1991). In industrial practice, people use a combination of an MRP or MRP-II system, custom-made or self-developed software, human supervision, leitstand schedulers, control software for warehousing and/or paper based techniques to control the shop floor. These solutions work, but are sub-optimal, expensive and very hard to change (Chryssolouris, 1992). In general, there is a trade-off between the performance that a scheduler can obtain and the reactivity of the system to disturbances. Moreover, this trade-off depends on the specific manufacturing system and its state. The selection of a hierarchical or heterarchical control architecture reflects this trade-off. The evolution of the SFCS (SFC System) to comply with the changing company needs is hardly supported and very expensive. The adaptation of the SFCS to the situation on the shop floor is not supported either. This paper presents a new approach to execute a given schedule, based on the concepts of holonic manufacturing. A holonic manufacturing system is a highly decentralised manufacturing system, consisting of autonomous, cooperating agents, called 'holons', and a hierarchical structure, called 'holarchy', which imposes rules upon the holons (Valckenaers et al., 1994a; Bongaerts et al., 1996). The HMS-architecture strives for easy (self)-configuration, easy extension and modification of the manufacturing control system, and for more flexibility and a larger decision space for higher control levels. Using these concepts, section 2 describes the application of the holonic manufacturing paradigm to the development of an architecture for SFC. This architecture focuses on schedule execution. Section 3 elaborates how this architecture can be used to obtain holonic behaviour. It shows that hierarchical control and heterarchical behaviour are both possible in a holonic system. Moreover, intermediate control scenarios can be applied and can yield additional benefits in certain situations. Section 4 describes the implementation. The conclusion is that for holonic manufacturing, the architecture is not fixed during the life cycle of a manufacturing system, but is dynamically adapted throughout the life cycle of the manufacturing system.
2. A HOLONIC SFC ARCHITECTURE One of the major tasks of a shop floor control system (SFCS) is the execution of a given schedule. Meanwhile it has to perform resource management, monitoring and take care of disturbances. Since the manufacturing model used by the scheduler often has only a limited amount of detail, the schedule cannot contain all decisions that have to be made. Therefore the SFCS has to make additional resource allocation decisions. E.g., while schedulers sometimes neglect transport and/or set-up, the SFCS should maintain consistency in the shop with respect to transport and setup. 2.1 Requirements A good architecture for SFC should offer the benefits of hierarchical as well as heterarchical control (Dilts, 1991). Hierarchical control yields benefits on the controllability and observability of the shop floor, while heterarchical control achieves a higher robustness against disturbances. To incorporate both advantages in one architecture, the architecture should be flexible with respect to the kind of control structure. A holonic system shall be able to work as well in a rather hierarchical as in a rather heterarchical environment. When only minor disturbances occur on the shop floor, traditional hierarchical systems obtain a high performance. If disturbances are frequent and resources are abundant and homogeneous, heterarchical control works well. As such, the correct selection of a control architecture (hierarchical or heterarchical) depends on the type of manufacturing system and its current situation. Moreover, the architecture should allow a whole range of intermediate behaviours to make a better compromise between optimisation and robustness. To obtain a flexible architecture, the individual components (the holons) themselves should be able to work as well under hierarchical as under heterarchical control, as under intermediate forms of control. It should be a straightforward task to adapt an existing holon to different forms of control. These concepts have been translated into the following requirements: • A multi-agent architecture is essential to allow autonomous reaction to disturbances (Koestler, 1989). • Flexible hierarchies. The type of control hierarchy to be chosen shall not be defined by the SFC architecture on beforehand. On the contrary, it shall be possible to change the type of control (hierarchical / heterarchical / advice / ...) during operation. • Multiple hierarchies (Valckenaers et al., 1996). Additional mechanisms, like negotiation, are required to account for the fact that a holon is subject to commands from several other holons. • Plug-&-play compatibility. • Self-configureability. Since an entire spectrum of control architectures is possible, the architecture shall support its own evolution, both on long term, to adapt to the evolution of the system, as on short term, to adapt to the situation on the shop floor.
2.2 General Overview of the Architecture The architecture we present here, is focused around the resource allocation aspect of a SFC system (Fig. 1). It conforms to the holonic reference architecture described in (Bongaerts et al., 1996), but other aspects of the SFC problem, like process planning, process control and monitoring are not considered here. On the lowest level, order holons and resource holons (like workstations and the transport system) can co-operate to co-ordinate all work that needs to be done on their own. This is accomplished by negotiation protocols as known in heterarchical control. Do note that Fig. 1 does not show these interactions. However, on a higher level, a SFC holon co-ordinates the work of orders and resources, similar to the job of a foreman in a factory. Since orders and resources are able to perform all work by themselves, they would not really need the SFC holon, but the existence of the SFC holon enables co-ordination on a global scale. On the upper level, a scheduler co-operates with the SFC holon to combine global performance optimisation with reaction to disturbances. The scheduler holon is continuously calculating the near-optimal global schedule for the future. Meanwhile, the SFC holon takes care of disturbances that occur when executing the current schedule. The scheduler can focus on optimising, without repetitive interrupts from shop floor disturbances. On the other hand, the SFC holon can follow a global schedule, and at the same time react to disturbances. As such, the SFC can react a lot faster to disturbances than a reactive scheduler could. In other words, the scheduler has a lot more time available for rescheduling than in typical reactive scheduling systems. Basically, SFC is performed by the co-operation of the order holons, the resource holons and the SFC holon. When new holons appear in the system, they exchange information about themselves with the other holons (dashed arrows in figure 1). During operation, resources like a workstation and a transport system notify the SFC holon when they have spare production capacity. The order holons request production capacity via the ‘Request Manufacturing’ interface. The function of the SFC holon is to match the requests of the orders with the offers of the resources on a certain instance of time. The SFC holon therefore uses the schedules it receives regularly from the
Fig. 1: Holonic SFC architecture.
scheduler (or from several schedulers (Valckenaers et al., 1996)). Due to disturbances, the SFC holon has to take decisions on its own to execute the schedule. Therefore it has to combine the information given by the schedules with the most recent feedback it gets from the resources and the orders. The decisions of the SFC holon are then sent to resources and orders, which use this information as advice for their own decisions. Within this basic framework, feed forward can improve the overall performance. Orders and resources are not obliged to wait with their requests and offers until the operation is executable or the resource is free. This enables the holons to foresee future events and consider the consequences of it. This will be used to have, e.g., an idle workstation waiting for an important order, even when work is available. This can also be used to have a workstation setting up for a certain operation when the order has not arrived in the workstation yet. 2.3. Holon Description 2.3.1 Generic holon description A holon is an autonomous and co-operative building block of a manufacturing system for transforming, transporting, storing and/or validating information and physical objects. The holon consists of an information processing part and often a physical processing part (Valckenaers et al., 1994). In this architecture, it implies that a holon is/contains an agent in a multi-agent system. In other words, every holon contains a continuously running computer process, able to control its own hardware devices and software tasks. As such, it is connected via a communication network with all other holons. Holons use the network to exchange messages. An event notification mechanism further enhances the flexibility of the communication protocol. Holons need a model of the holons they co-operate with, like presented in (Bongaerts et al., 1996). Via registration messages, holons dynamically exchange this information with each other, such that new holons can be added in runtime. 2.3.2 Workstation holon Under heterarchical control, the workstation shall communicate its availability to all interested order holons, and select via negotiation with the orders which operation it will execute. In a hierarchical framework, the workstation holon shall inform other holons about its status and perform operations for them. It informs the SFC holon when it is available, occupied, or unavailable in the (near) future. When its status changes, it notifies this event to the SFC holon. Under hierarchical control, the workstation shall execute the operations as requested by the SFC holon. Under intermediate forms of control (‘other’ holonic control paradigms), the SFC holon initially takes the decision and the order and workstation holons execute the task. The workstation sends its status to all interested holons. The SFC holon, considering all available information, will
allocate a workstation to a task. It will inform both the workstation and the order requesting that task. The order holon and the workstation holon will then autonomously settle the details of the task to be executed. This includes setting up the workstation, co-ordinating their activities in time, loading the necessary auxiliary resources and exchanging information like NC-programs. However, the workstation has the possibility to reschedule the tasks. This can be done to account for small deviations in the predicted processing time; to react to resource breakdowns; or to optimise set-ups that have not been foreseen in sufficient detail on higher levels. The degree to which this autonomy is allowed is determined in the holarchy configuration. 2.3.3 Transport system holon The transport system holon also is a resource holon, but has some aspects different from a workstation holon. It often is a multi-holon system by itself. Also, it often is not modelled in sufficient detail by all other holons. First, the transport system holon often is a complex multi-holon system itself. Except for Bussmann (1994), van Acker (1996) and Malewicz (1996), this problem up to now has got little attention in the research community. The transport system holon shields the internal transportation issues from other holons. The clients (order and SFC holon) will ask for transportation from one workstation to another one, but will not know the route taken or the AGVs used. Hence, it is not trivial to define its remaining capacity. In our implementation, we use the estimated arrival time of the order to its goal position to convey the transport capacity. If performance is to be taken into account, it is not trivial to convey the commands either. In our implementation, we not only ask for transport between two stations, but we also add (relaxable) time or sequence constraints. Since the transport system is already complex by itself, often it will not or not in sufficient detail be scheduled by the scheduling holon itself. This means activities have to be executed which are not explicitly scheduled. Since the route of an order is highly dependent of the scheduling decisions, the order holon cannot foresee the transportation issues either. Therefore, the SFC holon should provide the necessary data to the order holon such that the order holon can negotiate in a proper way with the transport system holon. 2.3.4 Order holons From an architectural point of view, it is essential that an order is an autonomous entity (a holon), since it allows non-hierarchical forms of control, like heterarchical control. Under heterarchical control, the order itself takes care of resource allocation. It therefore negotiates with resource holons and with other order holons to obtain the needed resources for its operations, and starts them. Heterarchical control allows working without SFC and without scheduler.
Under hierarchical control - as we define it in our architecture - the order requests resources for every operation to the SFC holon and the SFC holon will then match order and resources. Then order and resource holons will autonomously negotiate about the further finishing of their task. The order requests resources only if previous tasks have been executed. However, it can also do pre-requests to ask for resource reservations. This enables the execution of inserted-idletime schedules. Under intermediate, holonic forms of control, the order holon receives commands from the SFC holon, but can override these decisions if estimated necessary. The order holon also contains its own information: the client information and order deadlines; the precedence constraints; the handle to the product information and the process related state information. 2.3.5 Scheduling holons A scheduling holon performs optimisation of the resource allocation process, since the SFC holon, being an on-line control system, cannot perform optimisation in an appropriate way. As a consequence, the scheduler holon has more time to perform the optimisation process. Usually however, the scheduler only has a simplified model of the manufacturing system. It therefore needs to co-operate intensively with the SFC holon (see infra). Often, different aspects of the manufacturing process are optimised by different schedulers, for instance, one for the overall shop floor, one for maintenance, and one for the transportation system. Moreover, these scheduler holons are reactive schedulers that continuously improve their schedule. They require a reactive encapsulation of the scheduling algorithm, that reads feedback, adapts the scheduling problem according to the feedback, performs scheduling, using the results of the previous schedule and sends the schedule to the SFC holon and continues its work. Such a reactive scheduler can work periodically or on an event-driven basis. 2.3.6 Shop floor control holon Figure 1 presents an operational specification of the SFC holon. Four functions can be derived from it: holon registration, holon interfacing, the resource allocation decisions, and monitoring. The SFC holon receives registration messages from other holons it needs to co-operate with. These messages transfer a model of the sender holon, which the SFC holon uses to take the correct decisions. The SFC holon communicates concurrently and asynchronously with the resource holons, the order holons and the scheduler holon to accept work requests, manufacturing requests and new schedules. The core functionality of the SFC holon (with respect to schedule execution) is the allocation of resources to activities requested by the order holons. The decisions taken by the shop floor organiser are based on the schedule and the current manufacturing situation. How this decision is to be made, depends on control strategy chosen, as shown in
section 3. Once a decision is made, it is communicated to the resources as well as to the order holons. These resource and order holons will from then on autonomously perform the activities requested by the SFC holon. When finished, they report back to the interface agents to continue their work. The monitoring function of the SFC holon traces the manufacturing data and feeds this information back to the scheduler holons. 3. HOLONIC SCHEDULE EXECUTION The strengths of the presented architecture is its support for different strategies to execute a schedule. The selection of an appropriate control strategy depends on the amount of disturbances occurring and the significance of scheduling opportunities available. This section shows how the organiser in the SFC holon (Fig. 1) can work for different control strategies, and how different strategies can be combined in that architecture. These control strategies define the kind of co-operation between scheduler holons and the SFC holon, and also the co-operation between the SFC holon and the order and resource holons. 3.1. Hierarchical Control The architecture presented in this paper supports hierarchical control. Using this control strategy, the SFC holon precisely follows the sequence of tasks prescribed by the scheduler. When necessary, the SFC holon waits for the necessary resources to become available or for an operator intervention to restore the system such that it can continue according to this prescribed schedule. Therefore, the workstation holons and order holons are obliged to report to the SFC holon and wait until they get permission to continue. This permission will be given when the requested activity is the next activity scheduled on both the workstation schedule and the order schedule. In hierarchical control, the scheduler must consider all aspects of the manufacturing system for optimisation of the production, since hierarchical control only allows for one scheduler. Aspects that are neglected, e.g., transportation times, are not scheduled at all. Then, the order holon itself should autonomously organise transportation to the correct workstation. If the amount of disturbances is low, hierarchical control follows a given schedule and provides good performance. It also yields a quite predictable performance. However, the performance is very dependent on the scheduler. Moreover, if disturbances occur more frequently, the performance drops rapidly because the SFC holon cannot react to it without consulting the scheduler. Numerical results of this behaviour are presented in (Valckenaers et al., 1994b). 3.2. Heterarchical Control The architecture presented in this paper also supports heterarchical control (Duffie and Prabhu, 1994). Using this control strategy, the order holons and the resource holons take all resource allocation decisions by negotiation. Or-
ders select a workstation for a specific operation by requesting an offer. The workstation with the best offer is selected (for instance the one that can finish the operation first). The offer given by the workstation is based on local information only. Simultaneously, the orders bid for a good timeframe in which their operation will be executed. As such, each workstation obtains a sequence of operations. If for instance a workstation goes down, the orders will automatically select a new workstation to do the job. When the amount of disturbances occurring on the shop floor is high, a heterarchical system reacts very well to disturbances and keeps the performance on an acceptable level. An example of this strategy is described in (Valckenaers et al., 1994b) and compared with hierarchical control. When perturbations occur, the system uses alternatives and adapts automatically to the new conditions, as expected, and if the amount of perturbations are sufficient, heterarchical control outperforms hierarchical control. Furthermore, heterarchical control is easy to implement and to be made operational. However, there also are some drawbacks on heterarchical control. Its excellent reactivity is paid for in terms of lower overall performance. Since any available schedules are totally ignored, there is no global optimisation. Moreover, under specific circumstances, the system can reach 'trashing', i.e. a situation where the responses to a perturbation induce bigger disturbances in the system. 3.3. Hybrid Control Strategies based on Heuristics While conventional architectures support only hierarchical or heterarchical control, this architecture supports both — and many more. Intermediate control strategies can yield a compromise between performance and robustness against disturbances, which outperforms both hierarchical and heterarchical control in many situations. According to these control strategies, the SFC holon co-operates with the scheduler holon in a way that goes beyond a simple master-slave relationship. The SFC holon considers the schedules as advice, which it will follow in principle. However, when the scheduling advice becomes unfeasible or clearly sub-optimal, the SFC holon autonomously adapts to the new situation and tries to approximate the advised schedule as good as possible. Two hybrid control strategies that were implemented, divide the decision power over scheduler and SFC. In the first one, the SFC holon strictly obeys the sequence given by the scheduler, until disturbances occur. If the next operation scheduled on a workstation would start later than scheduled, the SFC holon has the authority to change the schedule as it pleases. In the second one, the schedule is cut in two parts, according to an instant in time: the near future part of the schedule and the further future part. The SFC holon is responsible for the execution of the near future part and takes decisions about the operations of this part. The reactive scheduler maintains control of the further future part. It continuously reads the feedback of shop floor data and adapts its schedule to the new situation. Periodically, the scheduler holon releases a new part of its
schedule to the SFC holon, giving full control over these tasks to the SFC holon. The SFC holon executes these tasks under heterarchical control. These two strategies yield a better compromise between performance optimisation and reactivity for our testbed flexible-assembly system. The experiments we therefore conducted (Valckenaers et al., 1994b), were performed using another architecture (Valckenaers, 1993). Although we could implement these strategies on this older nonholonic architecture, it needed a lot of time and expertise to do so. In the new architecture, implementing, simulating and testing a new strategy should be possible within a few days. 3.4. Holonic Control Strategy based on Perturbation Analysis The heuristic hybrid control strategies presented above yield a good performance under disturbances, but they leave room for further improvements. Their main weakness is that they adapt their behaviour (obey schedule or take autonomous actions) for the entire system as a whole. Better is to calculate for each individual operation the consequences of a decision to follow the schedule or not. If disturbances occur, it is easier to execute the schedule if additional information about the sensitivity of the schedule performance to disturbances is available. Executing a schedule is difficult, because local decisions affect the overall performance in a non-trivial way. Whether to swap two operations or to keep the scheduled sequence, depends on the effect of this decision on the global performance. The effect of these decisions can be estimated if the sensitivity analysis has already been done. Bongaerts (1995) addresses this issue in more detail. To use a holonic control strategy based on perturbation analysis, the SFC holon explicitly considers the effect of local deviations from the schedule to the global performance. This is done by the use of derivatives of the objective function to local decision variables. Bongaerts (1997) describes in detail how this is accomplished. Briefly, the algorithm works as follows: 1. The schedule is represented as a graph, where nodes represent the operations and the arrows represent the precedence constraints defined by the order and by the scheduling sequencing decisions. 2. When a decision is to be taken, the schedule is split in two parts, one to be rescheduled (local decisions), and another part for the operations, scheduled later in the future. This results in a cut graph. 3. The arrows that have been cut, represent bounds on the start time of the destination node operation ("earliest start times"). The global performance can be expressed in function of these earliest start times. It is possible (Bongaerts, 1997) to calculate the partial derivatives of the global performance to these earliest start times offline. 4. For each alternative decision, the effect on the earliest start times is calculated. Then, the effect on the global
performance is estimated using the partial derivatives. The alternative with the best performance is selected. The resulting behaviour can be called holonic, because it combines autonomous behaviour (reacting to disturbances) with co-operative behaviour (obeying the schedule). 4. IMPLEMENTATION This architecture has been implemented for the KULeuven Flexible Assembly System (Valckenaers et al., 1995) on OS/2, using a separate process for each holon, and using parallel threads within one holon. Several threads within one program can access the same datastructure, protected by semaphores if needed. An asynchronous ASCII based message passing protocol has been defined, based on TCP/IP. The protocol enables sending messages between any pair of holons, running on a computer on the Internet. A name server and event notification mechanism are foreseen, but not yet implemented. The SFC holon, called PHOCS (PMA Holonic On-line Control System), contains a message processing thread, an interactive user interface thread, a decision support thread and a control thread. While the interactive user interface, together with the decision support thread enables manual control of the production, the control thread implements one of the control strategies as described above. An additional control strategy can easily be added to the software. The decisions to be taken (by the human or by the control thread) are allocation of pallets to orders; allocation of workstations to operations; transport and set-up commands; and start times. PHOCS is built on an object-based datastructure usable for scheduling and controlling an FMS-like manufacturing system. Order holons, workstation holons and the transport system holon (Malewicz, 1996) can be implemented as software stubs or they can use real or simulated (on Arena®) devices. The reactive scheduler PARSIFAL is described in (Valckenaers et al., 1995). 5. CONCLUSIONS This paper has presented a holonic architecture for SFC. It consists of a set of autonomous building blocks, called holons, like order holons, workstation holons, a transport system holon, a SFC holon and scheduler holons. An important aspect of this architecture is that it supports the use of several control strategies. As such, the control architecture used by the system can be modified without changing the software and even at runtime. This enables the reuse of the control software in different environments. Besides hierarchical and heterarchical control, the architecture allows several other holonic control strategies, heuristic ones as well as a perturbation analysis based one. The selection of an appropriate control strategy depends on the needed robustness against disturbances and the needed opportunities for performance optimisation.
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