production planning

An intelligent interactive support system forshort- and mid-term planning in aproduction planning and control systemKarsten Poeck, Christian HestermannUniversität WürzburgLehrstuhl für Informatik VI (Künstliche Intelligenz)Allesgrundweg 12, D-97218 Gerbrunn, Germanye-mail: {poeck|hester}@informatik.uni-wuerzburg.deFax: ++ 49 931 70561 201IntroductionAlthough production planning and control systems were developed and used for about thirty years, a whole bunch of problems remains unresolved. Simple data management tasks – like retrieval and manipulation of master data concerning products, orders, staff, machines and stocks – are handled quite well. However the more complex task of creating or adapting production schedules is not yet satisfactorily solved in systems currently available on the market. Intelligent knowledge based systems supporting but not replacing the human planner, are a promising approach to solve this task. We propose a support system that covers the whole range from completely interactive scheduling and rescheduling to totally automatic plan generation. In one extreme the human planner may completely construct the schedule in an interactive graphical environment whereas the system only checks the production requirements. For automatic plan generation the system applies constrained based heuristic search and an anytime variant of heuristic repair.In addition to these well known but not satisfying approaches we offer intelligent golden means: The person planning interactively is supported by the system which generates suggestions like e.g. a suitable resource and time-slot for a chosen activity. The automatic planning is capable of recognising its limitations and consulting the user asking him for solutions for difficult or unsolvable situations, e.g. by weakening or relaxing constraints.Instead of reinventing the wheel and building a system from scratch we decided to closely integrate the support system with a commercially successful production planning system.In the following sections we define the problem of short term planning more precisely, sketch the problem solving methods and conclude with a discussion of the achieved results and of previous work.2Short term planningInput data to short term planning are rough work schedules and due-dates for the orders that should be manufactured within the next few days. Each order consists of several activities. Production requirements normally imply a total or partial order on the activities. Each activity may need severalresources like • one or more working places, • staff, • materials, and finally • one or more tools. The task of short term planning is to select resources and time-slots for all activities of the orders to be scheduled. Several constraints must or should be fulfilled in the final schedule:•The due-dates of the orders have to be respected.•The temporal dependencies between the activities of an order must be kept.•The maximal capacities of the resources may not be exceeded.•The shift schedule of the staff has to be taken into account.Often it is not possible to fulfil all constraints and the relatively best schedule is to be found.3.Generation and modification of schedulesA canonical approach to short term planning is the following algorithm.1)Select an activity to be scheduled.2)Select resources and corresponding time-slots for the activity.3)Test the constraints.4)If constraints do not hold, try a limited number of local repairs to the schedule.These steps have to be repeated until the whole schedule is completed. If after the initial generation of the schedule constraints do not hold, an optimisation with iterative local repairs as in step 4 may follow.All steps but the third may either be done interactively by the human planner or automatically by the support system.•For the interactive scheduling we have prototypically implemented a planning table that deliberately imitates standard graphical production activity control systems. The table shows the current schedule in form of Gantt-diagrams from a resource- or order-oriented point of view. The planner may perform local changes in this table. In a separate table he may select activities to be scheduled or select resources and time-slots for one specific activity. This selection may be supported by the system pointing to urgent activities, e.g. those using heavily loaded resources, or proposing a valid resource for a chosen activity. To allow for greater flexibility the planner may also change the scheduling specification at problem solving time.•For the automated schedule generation we have enhanced standard constrained based search techniques with heuristic scheduling knowledge. In addition to forward checking and lookahead,e.g. [Keng & Yung 89], we also take explicit scheduling knowledge for the selection of theactual activity and the corresponding resources and time-slots into account. By explicitly representing and using the knowledge of human planners we are able to deal with the large differences between various production environments. Instead of chronological or dependency directed backtracking with constraint violations we apply – although developed independently –a variant of iterative repair [Zweben et al. 94]. The main differences from our propose-and-exchange method [Poeck & Puppe 92] to iterative repair are first that we explicitly look formulti-step repairs and may perform an A-search in the space of possible repairs and second that we use an anytime variant [Body & Dean 94]. The differences between the methods are discussed in greater detail in [Poeck 95].Unlike the fixed order based planning in ISIS [Fox 87] or the mainly resource based planning in OPIS [Smith et al. 86] this scheduling scenario allows us to offer different planning strategies custom tailored for the specific application. It covers generating of new schedules as well as correcting and adapting existing schedules, for example to fit in newly acquired orders or to react to the unforeseen breakdown of machines.4Extensions for mid-term planningMid-term planning has roughly the same goals as short-term. It essentially operates on orders with or without due-dates and resources. The differences are the following:•The detailed orders must be condensed. Only one resource, normally the machine, is considered;its availability results roughly from the working hours of the personnel. Often several machines of the same type are combined; doing this multiple times introduces planning on different hierarchical layers. We also allow the combination of several activities executed in sequence and in the same department.•For mid-term planning it is not necessary to do date calculations as detailed as in short-term planning. Instead an activity can be scheduled on a day-to-day or even weekly base. We are developing a quantitative temporal reasoning subsystem to cope with these requirements.•In addition to the resources the materials must be planned. In contrast to the classical MRP-approach the lead times are not stored in the parts' description data but are calculated from the capacity-situation thus drastically reducing the work-in-progress.•In short-term planning most orders are considered as single tasks processed on their own. Mid-term planning has to consider the fact that complex products consist of multiple purchase and production orders which have to be coordinated in order to meet the due-date of the customer's order.•The result of mid-term planning are earliest start- and latest finish-dates for the various orders and their activities as well as purchase order proposals for the materials needed. These data are in turn used by the short-term planning described above.Despite these differences the underlying problem structure is very similar to short-term planning. Orders and their activities plus the materials needed have to be scheduled regarding the constraints resulting from resource and material availability. That's my we may use the same scheduling method both for short- and mid-term planning.5Discussion and previous workUp to now we have implemented the scheduling toolbox COKE [Poeck & Gappa 93] for assignment problems. The main application of COKE is hitherto time table planning for schools. We have evaluated the time table planner retrospectively with data from a school in Karlsruhe [Poeck & Puppe 92], a computed schedule is currently in use in a school in Würzburg [Poeck 95]. We have prototypically extended this toolbox to handle the requirements of short term planning in close co-operation with the production planning and control system VPPS [Kernler 93, Mai & Jankowski 92] of infor GmbH. After designing a data model as an extension to wide-spread structures used in commercial production planning systems we have implemented a file based interface to VPPS to transfer problem specification data like orders with due-dates, availability of resources, etc. We intend to improve this prototype with a more efficient ODBC-interface to the corresponding relational database. We have extended the scheduling methods to handle continuos and multiple assignment. An interactive plan table has been developed that allows the interactive generation and manipulation of schedules. We are about to start a research project [Hestermann 95] in co-operation with the supplier of VPPS and three industrial partners to complete, evaluate and refine the presented methods within three industrial partners. We also intend to cover mid-term planning within the week or month range by applying intelligent data reduction mechanisms.ReferencesBoddy, M., Dean, T.L. (1994) Deliberation scheduling for problem solving in time constrained environments. AI 67, 245-285Fox, M.S. (1987). Constraint-Directed Search: A Case Study of Job-Shop Scheduling, Morgan Kaufmann. Hestermann, C. (1995). An intelligent co-operative support system for short- and mid-term planning in production planning and control systems (In German). Internal project report, University of Würzburg.Keng, N., Yun, D.Y.Y. (1989). A planning/scheduling methodology for the constrained resource problem, Proceedings of the 11'th IJCAI, 998-1003.Kernler, H. (1993). Production planning and control in the third generation – foundations, methods and suggestions, (In German), Hüthig Verlag Heidelberg.Mai, W, Jankowski, F. (1992). CIM market survey (In German). Series advances in CIM-technology, Vol. 4, vieweg. Poeck, K. (1995). Configurable problem solving methods for classification and assignment (in German). PhD-thesis, University of Würzburg (to be published in Infix Verlag).Poeck, K., Gappa, U. (1993). Making role-limiting shells more flexible. Knowledge Acquisition for Knowledge Based Systems, Proceedings of the 7th EKAW, Aussenac, N. et al. (Eds.), LNAI 723, Springer-Verlag, 103-122. Poeck, K., Puppe, F. (1992). COKE: Efficient solving of complex assignment problems with the propose-and-exchange method. Proceedings of the 5th International Conference on Tools with Artificial Intelligence, Arlington, Virginia, USA, 136-143.Puppe, F. (1993). Systematic Introduction to Expert Systems: Knowledge Representations and Problem-Solving Methods. Springer-Verlag.Smith, S.F., Fox, M.S., Ow, P.S. (1986) Constructing and Maintaining Detailed Production Plans: Investigations into the Development of Knowledge-Based Factory Scheduling Systems, AI Magazine, Fall 1986, 45--61Zweben, M., Daun, B., Davis, E., Deale, M. (1994). Scheduling and rescheduling with iterative repair. In [Zweben & Fox 94], 241-255.Zweben, M., Fox, M.S. (Eds, 1994). Intelligent Scheduling. Morgan Kaufmann.。