Luc Bongaerts, Hendrik Van Brussel, Paul Valckenaers, Patrick Peeters
Reactive Scheduling in Holonic Manufacturing Systems: Architecture, Dynamic Model and Co-operation Strategy
Proceedings of ASI 97 (Advanced Summer Institute of the Network of Excellence on Intelligent Control and Integrated Manufacturing Systems), Budapest, 14-17 July 1997
Reference PMA: PMA 97P60
Affiliation: Katholieke Universiteit Leuven Mechanical Engineering Department Celestijnenlaan 300B B-3001 Leuven, Belgium Tel: 32-16-32 24 80 ; fax: 32-16-32 29 87 e-mail:
[email protected] http://www.mech.kuleuven.ac.be/pma/pma.html
0
Reactive Scheduling in Holonic Manufacturing Systems: Architecture, Dynamic Model and Co-operation Strategy Luc Bongaerts, Hendrik Van Brussel, Paul Valckenaers, Patrick Peeters Katholieke Universiteit Leuven - Mechanical Engineering Department Celestijnenlaan 300B - B-3001 Leuven, Belgium - Tel: 32-16-32 24 80 ; fax: 32-16-32 29 87 e-mail:
[email protected] - http://www.mech.kuleuven.ac.be/pma/pma.html Keywords: reactive scheduling; holonic manufacturing systems (HMS), intelligent manufacturing systems (IMS), CIM, simultaneous scheduling and schedule execution. Abstract : This paper presents an architecture for reactive scheduling that enables concurrent scheduling and schedule execution. When disturbances occur, the system reacts to them at several levels: an on-line shop floor control system reacts immediately, and the reactive scheduler responds more slowly, but with a higher response time. This approach enables a good combination of performance optimisation and reaction to disturbances. A Petri net models the co-operation strategy.
1. Introduction 1.1 Problem formulation The manufacturing world has come under more and more pressure. Customers demand shorter lead times and a higher product variety without making concessions on product price or quality. To remain competitive, a manufacturing system needs to react adequately to perturbations of its environment (e.g. rush orders) and uncertainties in the manufacturing process (e.g. defects, delays, and variable yields). Research on reactive scheduling has tried to cope with this problem. Using feedback and fast rescheduling algorithms, researchers aim to maintain scheduled performance while disturbances occur. However, the need for fast reaction to disturbances enforces them to limit calculation time for rescheduling and to restrict themselves to simple heuristics which are far from optimal. This paper presents an architecture, a dynamic model and a control strategy to combine good reactivity with high performance. Shop floor control is performed by an on-line control system and a reactive scheduler that both react to disturbances. The on-line control system reacts to disturbances immediately, using the existing schedule. The reactive scheduler does not react as fast, but uses this larger time span to adapt the existing schedule to optimise global performance. Both subsystems co-operate to complement each others abilities.
1.2 Literature Survey This literature survey will show that, up to now, few researchers have considered concurrent scheduling and schedule execution. Traditionally, the scheduling problem was considered to be static and deterministic, what led to the development of off-line scheduling algorithms. Lateron, research was focus on dynamic scheduling, on stochastic considerations [KMT95], on reactive scheduling, on industrial solutions and on distributed systems. This paper will show that concurrently performing reactive scheduling in combination with on-line distributed control can give better results. Dynamic scheduling. Several researchers nowadays focuses on dynamic scheduling: orders come in continuously, and production continues meanwhile. Therefore a lot of researchers [CFY92, HL93, OSF+88, Pro89, SF89, SSR+91] find a solution in dispatching rules or other very fast algorithms, in order not to keep the resources waiting while scheduling (rescheduling). Some researchers investigate the basic aspects of the problem by looking at simple scheduling problems, like the one-machine scheduling problem or the parallel machine scheduling problem [OU95]. [OU95] also uses the solution for this simple problem as a module in a job scheduling context with machine set-up times. Kimemia and Gerschwin [KG90, KG81] solve an explicit dynamic formulation of the flow shop scheduling problem, by the use of continuous decision variables instead of discrete ones. They consider the scheduling problem as a control problem. For larger scale and more complex problems, dynamic schedulers are often based on simulation: [NL95] uses a simulation based approach for an assembly line balancing problem, and [GB95] for improving MRP systems. Stochastic considerations in scheduling. Since processes in manufacturing never are completely deterministic, and the effect of stochasticity on scheduling performance can be quite significant, several authors explicitly consider stochasticity using queuing models, simulations, or other probabilistic models. [KMT95] combines queuing models with static capacity planning algorithms for static planning problems. [NL95] and [GB95] (see supra) also use simu1
lation to consider stochasticity. [WG95] explicitly includes the stochastic aspects in the objective function and use simulation and genetic algorithms for the optimisation. Reactive scheduling. In industrial situations, the use of high performance schedulers has often been questioned because of the difficulties encountered during schedule execution. Even dynamic scheduling and stochastic considerations do not completely solve the problem. Due to disturbances in production, the schedule tends to be invalidated quite quickly (low schedule robustness). As such, the predicted performance of the schedule is hard to obtain in reality. Therefore, research is currently focused on reactive scheduling [SK94]. This includes dynamic scheduling and encompasses the ways in which scheduling systems can react to disturbances in manufacturing (rather than including the statistics in the calculations). All stochastic aspects in the environment can be considered as disturbances: breakdowns, unexpected variations in processing times, failed operations and scrap, but also modelling inaccuracy. Van Dyke Parunak [Par91] discusses some structural solutions to this problem, like the introduction of buffers and more reliable machines. Operational solutions to deal with disturbances are rescheduling, deferred commitment and tweaking. One of the most used approaches to obtain reactive scheduling is rescheduling: restarting the scheduling process from scratch, given the new information regarding the status of the manufacturing system. Except for research on dynamic scheduling, also Luh [HL93] explicitly refers to the dynamic nature of the scheduling problem. Other researchers focus on a rolling scheduling horizon and/or a filter to keep the complexity of the problem under control [BSH92]. Deferred commitment scheduling [Par91, FK85, SW85] delays detailed decisions as late as possible to minimise exposure to the unexpected. As such, the risk of disturbances is considerably lower. Moreover, deferring commitment also increases flexibility [Val93]. Tweaking refers to techniques for correcting an existing schedule as the world deviates from expectations without complete rescheduling. Methods used for tweaking are perturbation analysis [Cas93] and turnpike theory (finding the easiest way to return to the existing schedule instead of rescheduling) [Par91]. Some researchers present additional data that can be used to respond to disturbances, like Luh [CL93], who proposes to use Lagrangian Multipliers to make rescheduling decisions. Several approaches consider and combine feedback and ‘feed forward’, like simulation techniques, knowledge bases, and performance forecasts [Ban93, KCM92]. Most approaches are based on a static formulation of the scheduling problem and only few people [VVB+94] really focus on the control problem of how to autonomously execute a schedule, once it is made, by a control system that performs real time monitoring and control of the manufacturing resources (trajectory planning versus control to follow a trajectory). Some people focus on the controller aspects, and present the use of Petri nets to build a controller that can do without detailed scheduling [LD92, ZK91]. Although research on discrete event system theory is certainly important to provide a mathematical basis for control of manufacturing systems, the application of these theories to realworld systems (e.g. the design of a FMS-controller) is not very likely in the near future for non-repetitive production. Some researchers have also explicitly considered the architectures needed for reactive scheduling. [Sch96] distinguishes predictive production scheduling and reactive production scheduling (also called on-line control). In this architecture, he applies case based reasoning techniques, but he does not allow continuous operation of both predictive and reactive scheduling, such that the usable algorithms are limited to the very fast ones. Proactive scheduling [SM95] explicitly considers reaction to disturbances as soon as they are known, and before they can invalidate the schedule. Industry. Industry often uses simple but robust tools for the resource allocation problem, like leitstand schedulers [LSD91], human involvement and self-developed software, often in combination with an MRP or MRP II system. Many factories use MRP or MRP-II systems to create a medium range schedule. The drawbacks of MRP are already widely discussed in several papers [Euw96]. The major disadvantages of MRP are its rigidity; the oversimplified model of the manufacturing system and its capacity; the tremendous amount of data that have to be entered in the Bill Of Materials and the lack of feedback from the shop floor. Some commercial shop floor control software is reviewed in [CTV93]. However, industry hardly uses the advanced schedulers resulting from the research community, because of several reasons [Par91]: The main reasons are the computational complexity of the scheduling problem and the sensitivity of a schedule to disturbances. Due to the computational complexity, it is difficult to provide a near-optimal schedule online. Due to the disturbances that occur on the shop floor, it is difficult to execute a given schedule by a shop floor control (SFC) system. Investigations in industry have shown that 20% to 30% of work is done on other equipment than was originally planned [LSD91]. Distributed approaches. Several new shop floor control approaches are based on distributed control, like heterarchical control, the fractal factory, bionic and genetic manufacturing and holonic manufacturing. Distributed approaches [DBW91, DP94] tend to be very reactive to disturbances, without explicit rescheduling. However, this reactivity is often paid for by a less efficiently used production system. In the fractal factory [War92], a robust and decentralised manufacturing system is based mostly on human involvement. In bionic [Oki93] and biological manufacturing systems [UV97], self-organising manufacturing systems are developed on the basis of distributed systems and a variety of modern computational techniques, like genetic algorithms, neural networks, artificial life, etc. [IO94] developed random manufacturing systems, based on autonomous machines, dynamic machine grouping, tender-based task allo-
2
cation and a reward and penalty driven shop floor control algorithm. [CY95] developed a distributed algorithm for dynamic scheduling, based on machine learning using simulation and genetic algorithms. Holonic manufacturing [VVB+94, STY97, MSM+92] is one of these distributed approaches, but distinguishes itself from the other distributed approaches by introducing hierarchy in the system. Compared to the traditional hierarchical systems, this hierarchy should be flexible, dynamic and reconfigurable. For resource allocation in holonic manufacturing, it means that totally distributed algorithms (for instance based on a market mechanism) are supplemented with a reactive scheduler, as described in [BVV+97] and in this paper. Other approaches to resource allocation in holonic manufacturing are based on the contract net protocol [RS96] and on a market mechanism [MKV+96]. Conclusion of literature survey. A lot of research has already been done on the reactive scheduling problem. Various problems (on stochasticity and dynamic scheduling) have already been addressed, using concepts like reactive scheduling and/or distributed control. Few of these researchers however consider the possibility for concurrent scheduling and schedule execution and thereby neglect opportunities for optimisation. This paper explicitly proposes such a concurrent approach and considers its consequences. It is based on the concepts of holonic manufacturing, because this concept enables the coexistence of autonomous distributed agents (“holons”) with a reactive scheduler. While the reactive scheduler is calculating an updated schedule, the autonomous agents execute the existing schedule.
1.3 Paper overview Section 2 proposes an architecture that enables concurrent scheduling and schedule execution. Section 3 describes a model of the co-operation between a reactive scheduler and an on-line SFC system. This model describes the cooperation strategy and identifies which parameters can be tuned. Section 4 describes how the reactive scheduler and the on-line SFC system use this model to tune their behaviour to obtain optimal performance. Section 5 and 6 describe the software design and implementation.
2. Architecture The proposed architecture consists of several modules: Orders, workstations, an on-line shop floor control (SFC) system and a reactive scheduler (Fig. 1) [BVV+97]. These modules are all autonomous entities, or “holons”. On the lowest level, resource allocation is achieved by the negotiation of order and workstation holons. This negotiation schema is co-ordinated by advice from the on-line SFC holon and the scheduler. The on-line SFC holon has to react to disturbances in real time. Therefore, it is not able to optimise the resource allocations because of the computational complexity of this problem. Hence, it is guided by the scheduler holon, that reacts to disturbances on a periodical and an event based basis. This paper focuses on the co-operation between the scheduler and the on-line SFC system. The main issue in this architecture is that all holons (scheduler, on-line SFC, workstation and order) work in parallel. The scheduler is calculating continuously, and meanwhile, the on-line SFC holon is controlling the system. Meanwhile, all workstation holons are continuously controlling their own workstation (implying scheduling, execution and monitoring). Also, all order holons are continuously scheduling, executing and monitoring the operations of the orders. Because all holons work in parallel, the system can react immediately to disturbances while still being able to optimise its schedule. If a disturbance occurs (for instance, if a tool breaks), the workstation will take immediate action (for instance, it will stop the operation, and start another operation with another tool on a next order). The on-line SFC holon will investigate if this delay would cause delays on other orders or on other workstations. It wil also check whether these delays would alter its decisions with respect to the sequence of operations or the allocation of workstations to operations. The on-line SFC holon takes this decision almost as fast as the workstation holon, but considering effects on the global scope. The reactive scheduler itself will also react to this disturbance, but will need some time to adapt its schedule. When ready, it will provide the on-line SFC holon with the new schedule. This parallel operation of different holons causes some problems that do not occur in traditional systems (with sequential scheduling and execution). First, the scheduler needs a model of the behaviour of the on-line SFC holon to know the state of the system at the time the schedule is ready. Second, the on-line SFC system needs a model of the Figure 1: A holonic architecture for scheduling and on- behaviour of the reactive scheduler to know when a new schedule will be ready. Third, the on-line SFC system needs line SFC. 3
a special algorithm to react to disturbances while also obeying the global schedule. A fourth problem has already been addressed quite often in literature: The scheduler also needs an algorithm to adapt its schedule to a changing environment (reactive scheduling). This paper will not address reactive scheduling. The other problems are addressed in the following sections.
3. Dynamic Model Since the scheduler and the on-line shop floor control (SFC) system both operate in parallel, simultaneously and asynchronously, and since they are working on the same problem of resource allocation, they need a model of each other’s behaviour. The scheduler has to know at what time the on-line SFC system will start executing the new schedule, and what the state of the system will be at that time. It needs to know how much time it will takes to get feedback from the shop floor and reschedule. The on-line SFC system requires information on the global performances objectives. It should also know when the new schedule can be expected. Before this time, it has to decide autonomously how to react to disturbances. At the K.U.Leuven, a testbed holonic manufacturing system [Val94] is available with a reactive scheduler Parsifal (PMA Reactive Scheduler for Flexible Assembly Systems, Leuven) and an on-line SFC system Phocs (PMA Holonic On-line Control System). When disturbances occur, the system reacts to them on several levels. Immediate action is taken by orders, workstations, and on a system-wide level by the on-line SFC system (by co-ordination), while the scheduler takes more time to consider the consequences of these disturbances to the globally optimised schedule and adapts its schedule. The behaviour of both subsystems is modelled with a timed Petri net (TPN). Figure 2 shows the TPN for the K.U.Leuven testbed HMS. (The behaviour of the order and workstation holons is not modelled here.) When the scheduler is started (t8), the schedule is calculated, and eventually some additional information for Phocs is calculated (as explained in [BVV+97]). Parsifal sends the schedule to Phocs, reads feedback, uses this information and adapts its schedule. Meanwhile, after Phocs receives the schedule, it merges the existing schedule with the new one, and commands auxiliary operations (like transport and set-up) for the newly scheduled operations (p12). Time is modelled by imposing a minimum waiting time dpi in the places pi of the Petri net, such that the transitions still fire instantaneously. When all input places of a transition are enabled and all tokens have stayed in the input place for at least the minimum waiting time, the transition can fire — and will fire — immediately. Transitions t8 and t9 are controlled transitions, modelling the start-up of Parsifal and Phocs respectively. The model considers the time needed for the calculation of the schedule (dp2) and eventual additional advice (dp3), for sending the schedule (dp4 = dp9) and the feedback (dp5 = dp10), for changing the model according to the feedback (dp6) and for setting up the system according to the schedule (dp12). All other durations dpi are zero. This Petri-net is conflict-free, since it is an event graph if transitions t8 and t9 are omitted (and since these transitions are never enabled anymore after start-up). Place p11 is almost a dummy place, but it is needed to model the possibility to send a new schedule when Phocs is still setting up for the previous schedule. The reachability graph for this Petri net would be unbounded, since places p11 and p12 would be unbounded if the initial markings and the timing constraints were not considered. However, it is possible to construct a reachability graph for this Petri net because transition t6 will fire immediately when enabled and transition t7 will always fire after a time tp12. The firing time of any transition can be calculated recursively as follows:
t (t i , j ) =
max
(t( pi , j pi ) + d pi ) ,
pi ∈Input(ti )
where t(ti,j) is the j-th firing time of transition ti, Input(ti) is the set of input places of transition ti, t(pi,jpi) is the time the jpi-th token enters place pi, and dpi is the minimum duration a token has to stay in place pi. Since every place has only one (enabled) input transition (conflict-free Petri net), t(pi,jpi) is always obvious. Considering these definitions and deductions, and using the reachability graph, the model enables the calculation of the scheduling frequency, the reaction times (or response times) for the scheduler and the on-line SFC system and the delay for orders to enter the system, the start-up time, the time Phocs can start executing the schedule, the time Phocs will get an updated schedule, etc. For instance, the period which Parsifal schedules with, is: TPARSIFAL = d p2 + d p3 + d p 4 + d p5 + d p6 . The time Phocs can start executing the schedule, is: tt 7 = t t1 + d p2 + d p3 + d p 4 + d p12 . In other words, Parsifal needs to create a schedule that only starts dp2+dp3+dp4+dp12 later. Moreover, Parsifal can assume that all tasks, scheduled before time tt7 in the previous schedule, will be finished when the new schedule is active. The earliest time Phocs will get the next update of the schedule, is: t(t 7, j +1) = t(t1, j ) + d p2 + d p3 + d p4 + d p12 + TPARSIFAL . In other words, Parsifal needs to provide Phocs with a detailed schedule that spans at least until time t(t7, j+1). The response time of the reactive scheduler to a disturbance on the shop floor, can be calculated (in the worst case scenario the disturbance happened just after the feedback was given) as: 4
Figure 2: A timed Petri net models the co-operation between the scheduler and the on-line SFC system.
τ disturb = t(t 7, j +1) − t (t 4, j ) = TPARSIFAL + d p5 + d p6 + d p2 + d p3 + d p 4 + d p12 . Phocs itself, on the contrary, can react in a time of the same order of magnitude as dp12. Similarly, the time needed to include a new order is (in the worst case): τ newOrd = t (t 7, j +1) − t (t1, j ) = TPARSIFAL + d p2 + d p3 + d p 4 + d p12 , while again Phocs can react in a time of the same order of magnitude as dp12. The calculations performed above were performed for a simple case, with a periodically rescheduling Parsifal, and without considering the interaction between Phocs and the orders and workstations. It would of course be better to also include an event based reaction to disturbances. Using discrete event models like shown above, it is possible to evaluate different architectures for resource allocation using distributed control and assisted by a reactive scheduler. The next section shows how to use these results to optimise the co-operative behaviour of Phocs and Parsifal. An example is given for a situation where a machine tool breaks down. The machine itself decides to start another operation that does not need the tool, and tries to get this tool replaced. The order itself decides to have other operations of that order executed as scheduled, and tries to find another machine for the failed operation. The on-line SFC system assists this search, by looking for a free time slot for this operation on alternative machines (rescheduling other operations as well). The scheduler will adapt the schedule and - if the breakdown causes another machine to become a bottleneck - the scheduler will de-schedule less important operations on that new bottleneck.
4. Co-operation strategy For the co-operation between scheduler and on-line shop floor control (SFC) system, the dynamic model is used to develop a control strategy that considers the time delays in scheduling and schedule execution. The current implementation corresponds to the model shown in Figure 2. In this situation, a periodic rescheduling algorithm is used in the reactive scheduler, and Phocs behaves like a server, being always ready to receive a new schedule, but never asking for one itself. In this case, all formulas given in the previous section are valid. The structure of the reactive scheduler algorithm is fairly easy: a continuous loop of scheduling, sending receiving and adapting. The on-line control system has to perform three tasks in parallel: executing the existing schedule [BVV+97], setting up for a new schedule and (waiting for) communication with Parsifal. The disadvantage of this implementation is that the response time to important disturbances is too long.
5
A better implementation would use a combination of event based and periodic rescheduling. This implies that the scheduler should also perform several tasks in parallel, namely scheduling and waiting for feedback. Phocs should then monitor the system and decide what are important disturbances to feed back to the scheduler immediately — in other words, without waiting for the next periodical feed-back. For instance, a breakdown on a bottleneck station should be reported to Parsifal as soon as possible. Figure 3: system performance (tardiness) in function of Using dynamic models as described above, it is schedule calculation time. possible to tune the parameters of the co-operation. Using this model, a trade-off between reaction speed and (re)scheduling performance can be calculated, and as such, the optimal calculation time for the scheduler is determined. For instance, consider the graph in Figure 3, showing system performance as a function of the scheduler’s calculation time. In this example, the graph shows tardiness, a performance measure that has to be minimised. Since Phocs immediately takes decisions, it is equivalent to a scheduler with calculation time zero. The longer a scheduler can calculate, the better its performance — for static problems. For dynamic problems, the duration of the calculations themselves influence the tardiness, certainly if the system would not continue to work autonomously when Parsifal is calculating. However, if Parsifal and Phocs co-operate, the system performance will be better than if Parsifal would work alone. For calculation times that are not that big, the system performance should be almost as good as the static performance of Parsifal. Using such a graph, one can easily estimate the optimal calculation time for the scheduling algorithm.
5. Software design The design of the software is based on an object based architecture. The software for the scheduler and the on-line SFC system uses a common datastructure [Bon95]. The datastructure models a job-shop-like manufacturing system, with additional features like set-ups, transport times, alternative machines and precedence graphs to represent precedence constraints. Using inheritance, the data model is extended to accomodate for specific needs for scheduling or on-line control. The scheduling software is designed in a modular way. It consists of a scheduling environment part, which is independent of the algorithm used, and the scheduling algorithm itself, which can be replaced by any other algorithm. The scheduling environment provides: • A user interface for viewing the Gantt charts, entering new orders and analysing the results; • Communication facilities with the on-line SFC system; • The algorithm for co-operation with the on-line SFC system, including the necessary routines to incorporate the feedback in the existing schedule. The design for the on-line SFC system also consists of several modules: the datastructure, the user interface, the message protocol and the control logic. The datastructure described above is extended such that it can model the complete state of the manufacturing system. The user interface can browse all internal data, shows the state of the system, and enables the user to take all decisions manually and to analyse the results. When the user takes all decisions manually, the system can be used as an interactive (on-line) SFC system, similar to a leitstand scheduler. The message protocol defines the co-operation between Phocs and the scheduler, the orders, the workstations and the transport system. This message protocol includes the message formats to be used between the different holons, but is quite ad-hoc. A well defined message protocol is subject to further research. To define the control module, different types of decisions were identified (specifically for flexible assembly systems): allocation and de-allocation of pallets to (from) orders; allocation of workstations to operations; and the transport, set-up and start of operations (sequencing). The control logic is again implemented as flexibly as possible: The control algorithms can be easily (and on-line) replaced by other ones, and it is even possible to have different algorithms taking care of different types of decisions simultaneously.
6. Implementation The concepts developed for this research were implemented in Parsifal, the PMA Reactive Scheduler for Flexible Assembly Systems (Leuven), and Phocs, the PMA Holonic On-line Control System. Parsifal is developed originally as a DOS program in C++. Phocs is implemented as a multi-threaded C++ program on OS/2. (The transport system holon and the order and workstation holons were also written in C++ on OS/2, as different programs.) The communication protocol is based on ASCII messages over ethernet (TCP/IP). The software is tested on a prototype Flexible Assembly
6
System at K.U.Leuven [Val94]. The current implementation controls workstations running in an ARENA® simulation and the transport system running in an ARENA® simulation or in reality. The implemented scheduling algorithms are the following: • Dispatching with priority rules and machine selection rules (FIFO and EDD); • A Lagrangian Relaxation algorithm developed at the University of Connecticut [HL93]. In future, an additional scheduling algorithm will be based on a Simulated Annealing algorithm wrapped around generic dispatching rules. The implemented algorithms for the on-line SFC system are described elsewhere. They are based on: Hierarchical control [VVB+94] ; Heterarchical control [DP94 ,VVB+94]; Interactive control ; Heuristics [VVB+94] and Perturbation Analysis [BVV97b].
7. Conclusions In reactive scheduling, traditionally a trade-off is made between available calculation time for performance optimisation and required reaction time to disturbances. The main contribution of this paper is an architecture that allows a multi-level reaction to disturbances, where two different modules simultaneously react to these disturbances, each with their own strengths and weaknesses. The combination of rapid reaction to disturbances with high performance schedule execution is obtained via a co-operation schema that considers the dynamic aspects of the co-operation. Using a timed Petri net model, the control strategy for the reactive scheduler as well as for the on-line SFC system is described and their response times are calculated. In future work, we will extend the implementation to include event based rescheduling and more performant scheduling and schedule execution strategies [BVV97b]. We will also conduct experiments on simulated and real systems to numerically assess the improvements of our approach.
8. Acknowledgements This paper presents research results obtained through work sponsored by a specialisation grant of the Flemish Institute for Support of Scientific and Technological Research in Industry (IWT), by the Concerted Research Action (GOA) on holonic manufacturing, and by Belgian Programme on Interuniversity Poles of Attraction by the Belgian State, Prime Minister’s Office, Science Policy Programming. The scientific responsibility is assumed by its authors.
9. References [Ban93] Banks, Complexity Reduction: Interruption Analysis in Flexible Manufacturing Systems, Journal of Manufacturing Systems V.12 N.2 p.153. [Bon95] Bongaerts, L., Generic Scheduling Datastructure (Software Documentation), Tech. Rep. PMA95R44, K.U. Leuven. [BSH92] Bispo, C., Sentieiro, J.J.S., and Hibberd, R.D., "Adaptive scheduling for high volume shops," IEEE Transactions on Robotics and Automation, Vol. 8, N. 6, December 1992. [BVV+97] L. Bongaerts, P. Valckenaers, H. Van Brussel, P. Peeters, Schedule Execution in Holonic Manufacturing Systems, Proceedings of the 29th CIRP International Seminar on Manufacturing Systems, May 11-13, 1997, Osaka, Japan, pp. 209-214. [BVV97b] Luc Bongaerts, Hendrik Van Brussel, Paul Valckenaers, Schedule Execution using Perturbation Analysis, International Symposium on Non-linear dynamics in production processes and systems, Hannover, Germany, September 17-18, 1997. [Cas93] Cassandras, C.G., "Discrete Event Systems, Modelling and Performance Analysis," Irwin, Boston, 1993. [CFY92] Connors, Feigin, Yao, Scheduling Semi-Conductor Lines using a Fluid Network Model, Transactions on Robotics and Automation V.10 N.2 p.88. [CL93] Czerwinsky and Peter Luh, Scheduling Products with Bill of Material Using an Improved Lagrangian Relaxation Technique, Transactions on Robotics and Automation V.10 N.2 p.99. [CTV93] Jarik K. Chaar, Daniel Teichroew, Richard A. Volz, Developing Manufacturing Control Software: A Survey and Critique, International Journal of Flexible Manufacturing Systems, V. 5, N. 1, 1993, pp. 53-88. [CY95] C. Chiu, Y. Yih, A learning based methodology for dynamic scheduling in a distributed manufacturing system, International Journal of Production Research, V.33, N.11, November 1995, pp. 3217-3232. [DBW91] D.M. Dilts, N.P. Boyd, H.H.Whorms, The Evolution of Control Architectures for Automated Manufacturing Systems, Journal of Manufacturing Systems, Vol. 10, N. 1, 1991. [DP94] Duffie, Prabhu, Real Time Distributed Scheduling of Heterarchical Manufacturing, Journal of Manufacturing Systems V.13 N.2 p.94, 1994. [Euw96] M. Euwe, Planning systems in the next century (I), Proceedings of ASI 96 (Advanced Summer Institute of the Network of Excellence on Intelligent Control and Integrated Manufacturing Systems), Toulouse, 1996. [FK85] B.R.Fox and K.G.Kempf, Opportunistic Scheduling for Robotic Assembly, Proc. of the IEEE Int. Conf. on Automation and Robotics, 1985, pp.880-889.
7
[GB95] S.M. Gupta, and L. Brennan, MRP Systems under supply and process uncertainty in an integrated shop floor control environment, International Journal of Production Research, V.33, N.1, January 1995, pp. 201-220. [HL93] Hoitomt, D., Luh, P., "Scheduling the Dynamic Job Shop," 1993 IEEE Internat. Conference on Robotics and Automation, Atlanta, Ga, USA., May 1993. [IO94] K. Iwata, M. Onosato, “Random Manufacturing System: a New Concept of manufacturing systems for production to order”, Annals of the CIRP Vol. 43/1/1994, 1994, pp. 379-384. [KCM92] Karsiti, Cruz and Mulligan, Performance Forecasts as Feedback for Schedule Generation, Journal of Manufacturing Systems, V.11 N.4 p.326, 1992. [KG81] J. Kimemia and S. Gerschwin, An Algorithm for the Computer Control of Production in a Flexible Manufacturing System, Proc. IEEE, 1981, pp.628-633. [KG90] Kimemia and Gerschwin, An Algortihm for the Computer Control of Flexible Manufacturing Systems, IEE Transactions, V15, N.4, p.353. [KMT95] P.H. Koo, C.L. Moodie, J.J. Talavage, A Spreadsheet Model Approach For Integrating Static Capacity Planning And Stochastic Queuing Models, International Journal of Production Research, V.33, N.5, May 1995, pp. 1369- 1385. [LD92] Lee, DiCesare, Scheduling Flexible Manufacturing Systems Using Petri Nets and Heuristic Search, Transactions on Robotics and Automation V.10 N.2 p.123. [LSD91] U. Lampkemeyer, B.C. Schmidt, J. Detand, Flexplan, Knowledge based Planning and Control in Manufacturing Environments (Description of the Main Modules), Proc. of CIM-Europe 7th Annual Conference, Torino, Italy, 29-31 May, 1991. [MKV+96] A. Márkus, T. Kis, J. Váncza, and L. Monostori, A Market Approach to Holonic Manufacturing, CIRP Annals, Vol. 45/1, pp.433-436. [MSM+92] T. Moriwaki, N. Sugimura, Y Y. Martawirya, S. H. Wirjomartono, Production scheduling in autonomous distributed manufacturing systems, PED-Vol. 56, Quality Assurance Through Integration of Manufacturing Processes and Systems, ASME 1992. [NL95] M. M. Nkasu, K.H. Leung, A stochastic approach to assembly line balancing, International Journal of Production Research, V.33, N.4, April 1995, pp. 975-991. [Oki93] N. Okino, Bionic Manufacturing Systems, in “Flexible Manufacturing Systems, Past, Present, Future”, Ed. J Peklenik, CIRP, Faculty of Mechanical Engineering, Ljubljana 1993, pp. 73-95. [OSF+88] Ow, P.S., Smith, S.F., Fox, M.S., and A. Thirez, Reactive Plan Revision, Proceedings of the CAIA-88, pp.77-82, 1988. [OU95] I.M. Ovacik, R. Uzsoy, Rolling Horizon Procedures for Dynamic Parallel Machines Scheduling with sequence dependent set-up times, International Journal of Production Research, V.33, N.11, November 1995, pp. 3173-3192. [Par91] Van Dyke Parunak, H., "Characterising the Manufacturing Scheduling Problem," Journal of Manufacturing Systems, Vol 10, No 3, pp241-258, 1991. [Pro89] Prosser, P., "A Reactive Scheduling Agent," Planning, Scheduling, Reasoning about Actions, 1989, pp.1004-1009. [RS96] Carlos Ramos, Paulo Sousa, Scheduling Orders in Manufacturing Systems using a Holonic Approach, Proceedings of the European Workshop on Agent-Oriented Systems in Manufacturing, Ed. S. Albayrak, S. Bussmann, (Daimler-Benz AG and T.U. Berlin), Berlin, Germany, September 26-27, 1996, pp. 80-85. [Sch96] Guenter Schmidt, Case-Based Reasoning for Production Scheduling, Pre-prints of the Ninth International Working Seminar on Production Economics, February 19-23, 1996, Innsbruck, Austria. [SF89] N. Sadeh, M.S. Fox, Focus of attention in an activity based scheduler - Proc. NASA Conf. Space Telerobotics, NASA, Bethesda, 1989. [SK94] E. Szelke, R.M. Kerr, Knowledge Based Reactive Scheduling - State of the Art, Int. Journal of Production Planning and Control, Vol. 5, March-April, 1994, pp. 124-145. [SM95] E. Szelke, L. Monostori, Reactive and proactive scheduling with learning in reactive operation management, Proceedings of IFIP International Working Conference on Managing Concurrent Manufacturing to Improve Industrial Performance, Sept. 11-15, Seattle, Washington, USA, pp. 456-483. [SSR+91] N. Sadeh, K.P. Sycara, S.F. Roth, M.S. Fox, "Resource Allocation in Distributed Factory Scheduling" - IEEE Expert, Vol 6, No. 1, 1991, p. 29-40. [STY97] N. Sugimura, Y. Tanimizu, T. Yoshioka, A Study on Object-Oriented Modelling of Holonic Manufacturing System., Proceedings of the 29th CIRP International Seminar on Manufacturing Systems, May 11-13, 1997, Osaka, Japan, pp. 215-220. [SW85] M.J.P. Shaw and A.B. Whinston, Automatic Planning and Flexible Scheduling, a Knowledge Based Approach, Proc. of the IEEE Int. Conf. on Automation and Robotics, 1985, pp. 890-894. [UV97] K. Ueda, J. Vaario, The Biological Manufacturing System, Adaptation to Growing Complexity and Dynamics in Manufacturing Environment, Proceedings of the 29th CIRP International Seminar on Manufacturing Systems, May 11-13, 1997, Osaka University, Japan, pp. 39-44. [Val93] Valckenaers, P., "Flexibility for Integrated Production Automation", PhD. thesis K.U.Leuven, 1993. [Val94] P. Valckenaers, H. Van Brussel, L. Bongaerts, and F. Bonneville, Programming, Scheduling, and Control of Flexible Assembly Systems, Computers in Industry (special issue on CIMIA), V. 26, N. 3, pp. 209-218, August 1995.
8
[VVB+94] P. Valckenaers, H. Van Brussel, L.Bongaerts, J.Wyns, Results of the Holonic Control System Benchmark at the KULeuven, Proceedings of the CIMAT Conference (Computer Integrated Manufacturing and Automation Technology), 10-12 October 1994, Troy, NY, Rensselaer Polytechnic Institute pp.128-133. [War92] H-J. Warnecke, Die Fraktale Fabrik, Revolution der Unternehmenskultur. Springer, Berlin, 1992 (in German). [WG95] Mark Wellman, Douglas D. Gemmill, A Genetic Algorithm Approach to Optimisation of Asynchronous Automatic Assembly Systems, International Journal of Flexible Manufacturing Systems, V. 7, N. 1, 1995, pp. 27-46. [ZK91] Zeigler, Kim, Extending DEVS-Schema to Knowledge Based Simulation for Real-Time Event Based Control, Transactions on Robotics and Automation V9. p. 351.
9
