A Questioning Multi-Agent System for Manufacturing Planning

A Questioning Multi-Agent System for Manufacturing Planning K.-P. Keilmann Universität GH Essen Wirtschaftsinformatik der Produktionsunternehmen Altendorfer Str. 97-101 45143 Essen Wolfram Conen y Universität GH Essen Wirtschaftsinformatik und Software-Technik Altendorfer Str. 97-101 45143 Essen Abstract To improve mid-term planning, organizational research has developed the concept of hierarchical manufacturing planning. Higher level aggregated planning generates constraints which are used as input for lower level planning activities. This means that lower level planers accept poor performance of the plans as long as the given constraints can be satisfied. This behavior is economically not desirable. The questioning of given constraints generated by higher leveled instances distinct our approach of a questioning multi-agent system for manufacturing planning (QMAS) from others known in manufacturing and hierarchical planning. In QMAS autonomous, intelligent agents cooperate in order to improve performance of global plans even if the local performance of some agents plan is decreased. 1 Motivation Groups, resp. object principles come under increasing consideration in the reorganization of production structures in manufacturing industry. Coupled with stronger customer-orientation, the increasing number of models and variants requires the development of new production and organization structures [11, 15]. Recent Business Process Re-engineering practices, support the setup of independent operating, decentralized production units. Manufacturing planning and organization constitute the framework for distributed feedback control Phone: 0201/8100358, e-mail: keilmann@wi-inf.uni-essen.de y Phone: 0201/8100319, e-mail: conen@wi-inf.uni-essen.de systems from which self-organization and self- optimization are demanded. Recently, the work on planning and scheduling concepts concentrated on dealing with the hazards of production control, mainly on problems with resource allocation and breakdown planning [1, 13]. Within this context, a number of multi agent system for reactive scheduling have been proposed. Only a small number of publications exist describing multi-agent systems dealing with materials, time and capacity planning. Nevertheless, this planning stages strongly constrain the actual production situation and thus limit the potential success of shop-floorscheduling [4, 12, 18]. Existing medium-term plans define the framework for short-term scheduling which is generally restricted to the consideration of released orders. The optimization of schedules within this framework doesn t allow for overall optimization unless the short-term scheduling may request adaptations or changes of constraints [9]. Steps towards the design of a multi agent based mid- and short-term planning system that allows for this form of coordination between planning and within stages are described. We call this the Questioning Multi-Agent System (QMAS). 2 QMAS concepts In this chapter we stress out the roots, the main idea and the basic concept underlying QMAS. At the end a classification is given and some short statement is made how these concepts relate to holonic manufacturing systems. 1

2.1 Hierarchical Manufacturing Planning To improve mid-term planning, organizational research has developed the concept of hierarchical manufacturing planning [8, 16]. As in hierarchical planning, known in the artificial intelligence community [2, 3, 5], this concept is based on the idea of planning at multiple stages which are distinct from each other through different levels and types of aggregation. Aggregation in hierarchical manufacturing planning is done along the dimensions time, capacity, and material/parts. This aggregation allows higher level planning activities to operate with simplified models of the underlying manufacturing system. As a consequence a loss of information occurs which leads to generation of plans which are feasible at aggregated level but infeasible at lower levels. These plans establish constraints which are used as input for lower level planning activities. Planning at lower levels tries to generate plans which satisfy all given constraints however disastrous this may be. This means that lower level planers accept poor performance of the plans as long as the given constraints can be satisfied. This behavior is economically not desirable. 2.2 The Idea In our approach of a questioning multi-agent system every agent is responsible for one unit of production single machines, work units, manufacturing areas (halls) or even firms and for the generation of plans reflecting given constraints. Ideally, these plans should be optimal under a given performance measure. Constraints are given as so called environmental parameters production type, resources available, capacity and by the requirements formulated by higher level agents as part of the tasks which have to be scheduled. The latter are, for example amounts of parts/products to manufacture and time requirements. Instead of concentrating on the generation of optimal plans using exhaustive search the agents try to relax the given constraints due to cooperation with agents at the same level agents which can be identified by previous/next relationships or by the sequence of operations necessary to complete a task or with higher level agents if the agent has to plan the first or last task to be performed. The decomposition of tasks following the operation sequence is shown in figure 1. Tasks are splitted into operations and operations at one level (agent A) form tasks at the next lower level (agents B, C). Agent B task 1 task 1 op11 op12 oop13 op13 task 2 op11 op12 op12 op13 oop13 op14 op11 op21 op12op22 oop13 op23 task 3 Agent A Agent C op11 op31 op12 op32 oop13 op33 Figure 1: Task decomposition The questioning multi-agent system is aimed to be used in manufacturing areas where multiple production structures have to be taken into consideration. Current scheduling approaches make use of measures, e.g., mean tardiness or average flow-time [7], which do not allow comparisons between different areas of production type or even different tasks. To be able to improve cooperatively plans we establish a measure called net present transfer value [10] which concentrates on the cash-flows caused/determined by the plans in consideration, and thus allows for comparative evaluation of the measure. 2.2.1 The net present transfer value approach The net present transfer value of a schedule NPTV(S) results from discounting, with an interest rate r t,the difference of cash inflows I tk and cash outflows O tk of all periods k to be considered at the particular time of decision. NPTV(S) = TX KX t=1 k=1 (I tk, O tk ) (1 + r k ) t Cash flows are relevant if planning decision influence their total amount or the point of time they occur. Cash outflows which can be identified as potentially relevant could be payment for:

raw materials additional parts set-up usage of resources (machines) direct labour Sales profits and rewards are the only cash inflows. An interest rate is defined for each cash flow to be able to reflect the fact that some cash flows are in some cases more important than others. The net present value approach allows us the definition of a goal function for all agents: minimize the net present transfer value. Nevertheless local planning can be done using classical approaches plan evaluation using this approach is independent from local plan generation. 2.2.2 Local planning Local planning has to be done according to the underlying production type, the level of aggregation and the structure of the problem to be solved. In general, local planning tries to generate plans optimal with respect to the measure given. Local plan generation can be done using ordinary algorithms (like lot-sizing algorithms) or constraintdirected search (in the scheduling area) [6, 14, 20]. It has to be investigated if current algorithms and heuristics lead to acceptable results under the net present transfer value measure. Instead of concentrating on local optimization while satisfying all given constraints, a local plan is generated making extended use of (cashflow oriented) heuristics. This (feasible) plan is now optimized violating constraints. Concentrating on operations which hit their limits could be used as a heuristic in that planning stage. If some constraint violations offer a great improvement of local performance the agent tries to cooperate with other agents to relax these constraints. 2.2.3 Global planning plan coordination Every agent tries to optimize his locally plan. As we know the set of all local optimal plans does not necessarily represent a globally optimal plan. It follows that we have to optimize local plans even if this local modifications result in a loss of performance of other agents plans. To optimize the global plan agents try to change the constraints given by some higher level agent. Different to other distributed, hierarchical planning systems [5, 3, 2, 19] no (sub)plans are exchanged nor plan analysis has to be done. Agents ask others if specified constraints can be relaxed in order to achieve a local benefit of certain amount. The addressed agent checks if that is possible and calculates the local amount of loss of performance. It is possible that agents issue further requests in order to comply to the original request. If the local loss of performance is less than the benefit achievable by the requesting agent the specified constraints are altered. It is assumed that all agents behave cooperatively they even allow the worsening of their local performance if an appropriate incentive is offered which is some amount greater than the lost performance. 2.3 Classification The questioning multi agent environment can be defined as follows: The basic planning philosophy is based on the ideas of multi-agent and hierarchical manufacturing planning. Higher level agents generate plans at an aggregate level. These plans form constraints for the planning activities of lower level agents. Plan coordination is achieved by cooperation of agents at the same level. Constraints established by higher level agents are subject to change if corresponding agents accept. That means that plans of poor performance are no longer generated in order to satisfy constraints defined by planning instances which do not have a detailed view of the current manufacturing situation.

Local planning is treated as a constraint optimization problem with certain relaxable constraints. The planning process is supported by extended use of heuristics which reflect the underlying planning problem and manufacturing type. The use of heuristics limits the search space but does not guarantee that the optimal solution is obtained. Agents cooperate to achieve better global plans. The improvement of a local plan requires sometimes the adaptation of local plans of other agents. To assure that the global plan is improved by this alteration a performance measure has to be established which is able to evaluate plans from different manufacturing situations/areas. This measure is given by the net present transfer value. Agents behave economically. Defining and propagating the NPTV and the explicit transfer of compensation (incentives) lead to behavior interpretable as cooperative. An appropriate evaluation function/performance measure, the NPTV, is defined. Higher level agents have an aggregated view on lower level agents, whereas same level agents do not need to have any knowledge of each other despite that there exist some agents. No reasoning about other agents plans has to be done. Alterations of local plans are requested explicitly. 2.4 Related research One of the most interesting approaches in manufacturing planning, from our point of view, is the concept of holonic manufacturing systems (HMS) [17]. A HMS is defined as a holarchy of autonomous, intelligent and cooperative holons. Holons are stable structures which will be able to reconfigure quickly and easily in order to produce various products. The holarchy defines the basic rules of cooperation. Cooperation is defined as the process of establishing common plans. Research is HMS concentrates on translating the concepts of holons to manufacturing planning and control systems. Therefore questions like how to describe, identify or model holons, or how to integrate the HMS concept into manufacturing systems have to be answered. This paper describes some possibility how manufacturing planning can be done in systems consisting of autonomous, intelligent entities, where cooperation is ensured by establishing a measure that allows an comparable evaluation of replanning benefits and thus, for economic behavior. These entities can be holons or fractals as long as it is possible to describe their characteristics and behavior in a way suitable for hierarchical planning. That means that aggregation of characteristics and behavior is possible and basic functionality is stable. 3 Conclusion The development of the questioning multi agent system is still in its early phase. The evaluation of manufacturing plans of different levels of aggregation is implemented. We developed a evaluation prototype which is currently in test at a danish steel shipyard. The basic concepts of communication, planning and multi plan coordination are well understood. Open questions occur when describing the dynamic behavior of such a system. The behavior of QMAS depends on parameters set (time to spent on local optimization, depth of recursions allowed, number of open requests) and the structure of the underlying manufacturing environment (production type, cash flow structure). Unlike in traditional scheduling no representative problem descriptions exist which are usable as test cases in the area of cash flow oriented evaluation. Experiments have to be made to be able to present valid results and sound statements about characteristics and behavior of QMAS. Research also has to be done in the following areas: Which heuristics should be used to improve efficiency of local plan generation? As local planning makes use of heuristics to guide search (constraint optimization) we also have to investigate heuristics to define which kind of operations to concentrate on in order to achieve a significant improvement of the plan. Adaptive and reinforcement learning are subjects of interest in this area. How to deal with the multitude of different plans

considerable at each agent? Since no locking, as known in database systems, is possible modified 2(n)-phase commit protocols have to be investigated and tested. How to restrict the depth of recursion in such a system? The request of modification of one constraint can cause the addressed agent to propagate requests to others. Therefore, a reasonable relation between possible improvement and expenses, caused by communication overhead and time needed, should exist. The questioning of given constraints generated by higher leveled instances distinct this approach from other ones known in manufacturing and hierarchical planning. Planning instances are able to modify their planning environment by means of cooperation. No longer they have to make the best of the given constraints even if these constraints are generated using wrong figures and assumptions about the current manufacturing environment. There still remains the question if it is useful to improve the given manufacturing planning philosophy, used in current MRPII-based systems, which is known to be error-prone [4]. Wouldn t it be better to define a new planning philosophy independent of current MRPII-applications? From our point of view the questioning multi-agent system, together with other new paradigms, like holonic manufacturing and the fractal factory, could be seen as one first step towards new systems based on independent, self-organized manufacturing units. Both, HMS and QMAS, deal with the task of manufacturing planning based on autonomous, intelligent entities. Whereas QMAS concentrates on multi plan coordination with an background on economics, HMS is interested in the general definition of holonic systems and their usage on a technical background. Ideas of QMAS can be integrated into HMS in order to operationalize coordination. References [1] S. Bussman: A Multi-Agent Approach for Dynamic, Adaptive Scheduling of Material Flow, in: Modelling Autonomous Agents in a Multi-Agent World, Odense, Preproceedings, 1994, pp. 159 170 [2] D. D. Corkill: Hierarchical Planning in a Distributed Environment, Proceedings IJCAI, 1979, pp. 168 175 [3] K. S. Decker, Victor R. Lesser: Generalizing the Partial Global Planning Algorithm, International Journal of Intelligent Cooperative Information Systems, 1(2), pp. 319 346, 1992 [4] C. Dorninger, O. Janschek, E. Olearczick, H. Röhrenbacher: PPS Produktionsplanung und Steuerung: Konzepte, Methoden und Kritik, Ueberreuther, 1990 [5] E. H. Durfee, V. R. Lesser: Negotiating Task Decomposition and Allocating Using Partial Global Planning: L. Gasser, M.N. Huhns (eds.): Distributed Artificial Intelligence Volume II, Pitman, 1989, pp. 229 244 [6] Fox, M. S.: Constraint-Directed Search: A Case Study of Job-Shop Scheduling, Pitman, London, 1987 [7] S. French: Sequencinq and Scheduling, Ellis Horwood, 1982 [8] A. C. Hax, H.C. Meal: Hierarchical Integration of Production Planning and Scheduling: M. A. Geisler (ed.): Logistics, TIMS Studies in the Management Sciences, North Holland, 1975. pp. 53 69 [9]J.J.Kanet: Towards a better understanding of Lead-Times in MRP Systems, Journal of Operations Management 6, 1986, pp. 305 315 [10] J. J. Kanet: A New Performance Measurement Approach for Manufactoring Logistics Systems, 1992 [11] G. Keller, S. Kern: Dezentrale Inselstrukturen in Planung und Fertigung: August-Wilhelm Scheer (ed.): Fertigungssteuerung: Expertenwissen für die Praxis, Oldenbourg, 1991 pp. 105 128 [12] K. Kurbel: Produktionsplanung und -steuerung - Methodische Grundlagen von PPS-Systemen und Erweiterungen, Oldenbourg, 1993 [13] J. Müller (ed.): Verteilte Künstliche Intelligenz - Methoden und Anwendungen, BI.-Wiss.-Verlag, 1993,

[14] P. Prosser: Scheduling as a Constraint Satisfaction Problem: Theory and Praxis: Proceedings of the Workshop on Scheduling of Production Processes of the 10th European Conference on Artificial Intelligence, Vienna, 1992, pp. 7 15 [15] Thomas Ruffing: Die integrierte Auftragsabwicklung bei Fertigungsinseln Grobplanung, Feinplanung, Überwachung: August-Wilhelm Scheer (ed.): Fertigungssteuerung: Expertenwissen für die Praxis, Oldenbourg, 1991 pp. 65 86 [16] M. Steven: Hierarchische Produktionsplanung, 2nd edt., Physica, 1994 [17] P. Valckenaers, H. Van Brussel: IMS TC5: Holonic Manufacturing Systems Technical Overview, Katholieke Universiteit Leuven, [18] T. E. Vollmann, W. L. Berry,D. C. Whybark: Manufacturing Planning and Control Systems, 2nd. ed., Homewood, 1988 [19] F. von Martial: Coordinating Plans of Autonomous Agents, Springer, 1992 [20] Wallace, M.: Constraints in Planning, Scheduling and Placement Problems, Report CORE-93-6, ECRC, München, 1993