DPACS: A SELF-ADAPTATIVE PRODUCTION ACTIVITY CONTROL STRUCTURE Damien Trentesaux 1,2 Christian Tahon 1 1 Laboratoire d'automatique et de Mécanique Industrielles et Humaines. Université de Valenciennes et du Hainaut-Cambrésis, Le Mont Houy, BP 311 59304 Valenciennes cedex, France Tel:(33) 27.14.13.54. Fax:(33) 27.14.12.88. e-mail: chip@univ-valenciennes.fr 2 Laboratoire d'automatique de Grenoble., E.N.S.I.E.G., BP 46 38402 Saint Martin d'hères cedex, France Abstract In this paper we present the concept of a Distributed Production Activity Control Structure (DPACS) and we prove that such a structure is self-adaptative. The first part introduces the concept of a DPACS as a set of entities considered as autonomous agents able to communicate with other agents to achieve a global production program split in many subtasks. Multicriteria algorithms supporting non-quantitative and estimated data are embedded into the decision support system of each agent of the DPACS to solve the task allocation problem. The second part presents the concept of self-adaptability applied to a production structure. This term describes the ability for a system to support random events and modification of the current structure of the production. We check if both DPACS and hierarchical structure support self-adaptativity. We show that such an integration is easier with the DPACS than the hierarchical one and we provide then a set of examples to illustrate the integration of self-adaptability feature into the DPACS. Keywords: distributed production structure, production activity control, self-adaptability, simulation. Application domain: distributed production systems 1. Introduction The aim of our work is to propose a production structure able to support random events such as breakdowns and modifications of the production structure. To provide such a support, we have defined and developed a particular production structure based on the concept of agent. This distributed structure needs high-level communication support and high-level decision making for task allocation. Some previously released papers have described the motivation for developing the DPACS and its concepts. This paper aims at developing the concept of self-adaptability and illustrates how our distributed production structure integrates by nature selfadaptability through simulation case studies. First, we sum up the proposed distributed structure and shows how to apply the distributed approach to the operational level of production, in particular, the production activity level. We then introduce the concept of self-adaptability for the operational level of a production structure, which is the encapsulation of random events and flexibility concepts relating to the current state of the production system. Finally, we describe the difficulty to integrate self-adaptability into a classical hierarchical production process and we illustrate the ability of the DPACS to support self-adaptability through a set of studies.
2. Distributed production activity control structure In this first part, we present a conceptual model of the DPACS. Such a distributed structure (cf. fig. 1) inherits from the concepts of an agent: an agent /FERBER 88/ is defined as an abstract or a physical entity able to act on itself and on its environment and to communicate with other agents. It aims at performing a set of tasks, parts of a global problem. To execute these tasks, the agent may use a set of objects. The Control defines the cooperation between agents, the group organization, and its evolution. The cooperation is defined by a cooperation degree, which ranges from fully cooperative to antagonistic agents. Communication between agents depends on the selected protocol, that is the set of rules that specifies the way to synthesize messages to make them significant and correct. Each agent controls a set of production resources (objects as a set of production tools such as mill, lathe, robot, etc.). Thus, tasks are operations to be performed by these production tools. Two kinds of cooperation control can be defined: horizontal (between agents for data exchanges) and vertical (between each agent and the human operator associated to this agent as a supervisor). The human operator has the responsibility for the task resolution and allocation (vertical cooperation through a decision support system). The task allocation consists in allocating responsibility for processing on products. Figure 1: the distributed production structure The user level may be composed of several user groups or even only on user who supervises the whole production processing, which is the case for the simulation studies. 2.1. The IMS concept: "Integrated Management Station" The Integrated Management System (IMS) /TCHAKO 94-a/ /TCHAKO 94-b/ represents an agent responsible for operation processings (objects are production facilities such as mill or lathe) and allocations in a DPACS integrating an operational-level Decision Support System (DSS) for the task allocation /TRENTESAUX 94-a/. An IMS includes: - A decision system: it is based on a representation of the different agents and supports the procedures (rules, algorithms, etc.) for decision making. It participates, by cooperating with the different agents, to attempt to process the task. This, for example, will make or refuse a task announcement. - A control system: it is responsible for the command orders for the handling automated system (e.g., command orders for a conveyer, or a control signal to motor). - A communication system: it is responsible for the information exchanges through a local area network. - An information system: it supports the required information for the other sub-systems (local database). - An interface system: it insures the dialog with the human operator, and the interactions between the different sub-systems. The DSS model for task allocation is based upon Sprague's concept for a DSS defined as a set of three sub-systems (data, modeling and dialog systems) /SPRAGUE 87/.
2.2. Dynamic task allocation Fig. 2 presents the integration of the dynamic task allocation in the global plan of production. Task performing is dynamically realized through the task allocation process. A global planning is first created regarding the whole set of constraints of the production orders (workshop capacity smoothing, etc.). The DPACS is responsible for the monitoring of the work processing. A set of manufacturing orders is sent to the DPACS that manages this whole set as a sum of sub-tasks (operations, parts of a manufacturing order) to be allocated and executed. A detailed scheduling is a subset of the cartesian what*when*where product. An allocated task is an element (an operation) of a detailed scheduling (set of manufacturing orders). The detailed scheduling is incorporated in the real time production control part. Thus, the detailed scheduling is established dynamically throughout the task allocations. The decision making typology presented in this paper concerns non-optima and semistructured decisions of the operational level (dynamic task allocation integrating qualitative data), which justifies the DSS approach. Figure 2: The production management structure 2.3. The vertical and horizontal cooperation The vertical cooperation is managed by a specific operational-level DSS responsible for task allocation support. The horizontal cooperation supports fully cooperative data exchanges and cooperation through negotiation. To be satisfactory, the task allocation must be performed by a set of cooperative IMS supporting global (static) and local (dynamic) constraints. 2.4. The communication The basic principle of the communication protocol (negotiation paradigm) is based on a simplified cooperative strategy. No backtracking for negotiation is allowed: when an operation has been performed, the IMS sends a request about the next operation of the manufacturing order to be executed. Each of the IMS able to perform the next operation returns an acceptance. The requesting IMS selects one of the proposed IMS through cooperation with human-operator, dialog and decision making (DSS) and sends to this IMS a reservation and a release to the others. A discharge from the selected IMS concludes the protocol. The complete protocol has been modeled using colored and Temporized Petri nets and is presented in /TRENTESAUX 94-b/. 2.5. Multicriteria decision support system for task allocation A Study has been made by /TRENTESAUX 95/ on the integration of multi-criteria algorithms into the DSS. The conclusion of this study is that multicriteria algorithms suit well the problem of task allocation in a discrete production environment (the pertinence of
the set of criteria is a key for the pertinence of the selection), although almost no real application exists at the operational level. 2.6. Qualitative data integration Our multicriteria algorithm has to support several constraints /TRENTESAUX 95/: - The typology of criteria can either be qualitative (quality, robustness, etc.) or quantitative (cost, inventory, etc.). - To support unknown or bad-known weights of criteria, they can either be qualitative or quantitative. - The value of data can either be: (i) Quantitative and measurable: number of operations made till now, total time elapsed since the last operation, etc. (ii) Quantitative and estimated (for instance, by an exponential probability function): operating time, MTBF, MTTR, etc. (iii) Qualitative: quality of IMS production, technical capacity of a IMS human operator, etc. - The high-flexible structure of a distributed production control requires real time data capture (global and local production data) and real time modifications of parameters of the algorithms (criteria, weight of criteria, etc.). The participation of human operator is not only required for these modifications of parameters, but also for extra-rules (priority or forced task allocation, etc.). Thus, levels of cooperation have been defined. The task allocation processing in distributed environment requires the definition of a set of criteria and compatibility checks with boolean constraints associated to resources, products, and flows. These constraints are difficult to estimate because of the wide range of production systems. We have established a non-exhaustive list of these constraints to define faithfully the task allocation problem. The task allocation processing is decomposed in two specific steps: one for constraint compliances, one for resource selection. These two specific steps have been widely developed in /TRENTESAUX 95/ and in /TRENTESAUX 94-a/. We present the main topics: 2.6.1. First step: Constraints restriction for the alternative IMS When an IMS receives a request for a task, it checks if its characteristics are compatible with the proposed task regarding some or all the following non-exhaustive list of constraints: - Resource constraints: life duration of tools, qualification of the human operator vs. task, availability of the operating resources (calendar, failures), etc. - Production constraints: latest start date, earliest start date, quality, compatibility product vs. resource, etc. - Flow constraints: raw material, capacity of the resource vs. product, non splitting batch, etc. 2.6.2. Second step: Selection of the target IMS through a multi-criteria DSS The IMS that are able to perform the task -that is, their characteristics comply with the constraint set regarding the nature of the task processing- send an acceptance. The set of alternative IMS are estimated regarding a list of criteria. We can define such a list based upon several axes of significance for the problem /ROY 93/. These axes represent a specific point of view of the problem. - Criteria for the Lead time axe of significance: running time of the task/ resource, preparation time task/ resource, travel time product/ resource and resource availability time. - Criteria for the quality axe of significance: damage rate for the tools/ task, reliability and Qualification required for the human operator/ task.
- Criteria for the cost axe of significance: estimation of the production cost of the oncoming task -including specific costs such as local storage cost of the fabrication order, human operator qualification cost, etc. The multi-criteria DSS helps the human-operator to select a satisfying IMS (regarding the previous criteria) and the requesting IMS sends a reservation to this satisfying IMS and a release to the unselected ones. The next part deals with self-adaptability as a key for flexibility and random events supports. 2.7. Drawbacks The drawbacks implied by such a real-time and distributed resolution for a scheduling are beyond the scope of this paper and are presented in /TRENTESAUX 94-a/ /TCHAKO 93/. We present here the summary of this study. The characteristics of the drawbacks inherit from the real-time an distributed aspects of the task allocation/processing control: - Real time control: this implies no ability for any feedback, backtracking nor anticipation for requirements or finishing dates. Hence, this may reduce the performance of the realtime scheduling and the industrial viability of such an approach. - Distributed control: it is based on a highly heuristic approach, that is, the restriction of knowledge provides non optimum decision makings and should alter the effectivness of the scheduling too. 3. Self-adaptability Self-adaptability can be defined as the ability for a structure to support random events and flexibility of the production structure relating to the current state of the production system. 3.1. Random events We define a random event as a new real time constraint that was not forewarned because of its random sight. Three axes have been developed: 3.1.1. Resource viewpoint This includes but is not limited to: - Breakdowns or unavailabilities of human operators or production means/ resources, - Breakdowns of components of the production structure (wearing parts, etc.), 3.1.2. Product viewpoint - Product modifications: the manager may modify some production parameters according to customers' wishes (e.g.: routing), - Supplier mistake, etc. 3.1.3. Flow viewpoint - Stock outage (sub-contracting/ supplier problem, etc.) - Priority manufacturing order: the manufacturing order priorities may be modified. (e.g., some new high-priority manufacturing orders may appear), etc. 3.2. Flexibility Higher a production structure is flexible, easier it is to re-modify the production process/ structure to support new objectives or new planned constraints. That is, easier it is to integrate dynamically new constraints/ objectives that have been planned or scheduled avoiding as possible to question the production process/ structure. Again, three axes are defined: 3.2.1. Resource viewpoint - Modification of the structure: such as addition or suppression of resources (global workload modification), etc.
- Modification of the production facilities: production facility change/ improvement, etc. 3.2.2. Product viewpoint - Modification/ integration/ suppression of new/ old products according to the life cycle, etc. 3.2.3. Flow viewpoint - Routing/ production flow optimizations (e.g., depending on the product modifications), etc. 4. Self adaptability and classical/ hierarchical structures Global flexibility of a hierarchical production system has been widely developed and published. Different conceptual models have been defined to integrate flexibility: CIM&FMS /ARTIBA 90/, /BAKKER 88/, /DUFFIE 86/, /DUFFIE 88-a/, /DUFFIE 88-b/, /GIARD 88/, /LAMY 91/, /BENASSY 90/. But we have shown that the difficulty for such kind of structure is to support random events /TCHAKO 93/ in a simple or effective way. This is due principally to the very strong coupled links existing between the production level with the higher decision levels. Of course, several algorithms for hierarchical control able to cope with uncertainty have been developed (algorithms for altering some estimated finishing dates of some lead manufacturing orders, algorithms using safety-margins for production times or algorithms based on forecasts, and so on), but they all imply alterations of the planned scheduling, which is harmful to the effectivness of such hierarchical control. The optimization of the scheduling is one of the most important advantage of this kind of control, indeed. The next part shows that both flexibility and random event supports can be integrated to the DPACS. 5. Self adaptability and the DPACS In this part, we show the integration of the previous characteristics into the DPACS. Some examples are provided to illustrate the main features of the DPACS (screen printings from a simulated DPACS on a single 486-PC-DX66Mhz: 9 manufacturing orders, with an average of 3-6 operations each, are to be processed by the DPACS and a human operator as a supervisor).
figure 4: a complete detailed scheduling (no breakdown). A sample of exchanged messages is shown. The fig. 4 presents the complete detailed scheduling obtained without any extra random event nor try for flexibility. We now consider this detailed scheduling as the main scheduling to be compared to the oncoming different schedulings. The character in front of each IMS indicates the disponibility date for an IMS to perform a task. 5.1. Random events Two kinds of solution can be provided to support random events such as fails: redundancy and 'step by step' transactions using confirmation stages. Both are used to prevent from random events. 5.1.1. Resource viewpoint Different levels of breakdowns have been established, cf. fig 3. It is important to note that only the network breakdown will make the whole system collapse. The basic protocol for communication between IMS presented above /TRENTESAUX 94-b/ has been extended with a set of delays linked to every step of the protocol (such as waiting for acceptance, for discharge/ release, etc.). Those delays allow to reiterate specific steps of the communication protocol that have not been completed at the end of a temporized period (step by step transaction). That leads to support automatically IMS fails. 5.1.2. Product viewpoint A product that can not be performed may stay on the deficient IMS until repair or can be re-allocated to other IMS. This aspect needs particular rules or decision help to make a correct decision. 5.1.3. Flow viewpoint For example, the manufacturing order priorities can be modified readily by the humanoperator and the IMS, both responsible for the monitoring of the queue of jobs. On the other hand, weights of criteria (from the multi-criteria DSS) can be dynamically modified to support modifications on global/ local objectives (interactive mode of the DSS).
Breakdowns Network breakdown IMS breakdown Operator breakdown Sub-system breakdown production facility breakdown Communication system breakdown Decision system breakdown Interface system breakdown Information system breakdown Control system breakdown Figure 3: breakdowns typology 5.1.4. Example The two following screens show how random fails are automatically supported by the distributed structure, breakdowns only alter the global processing for the allocation. Each detailed scheduling is established dynamically as and when task allocations are performed, the time scaling is automatic. Failing IMS can neither work on product during the breakdown duration nor communicate with other IMS (breakdown symbolized by a black and white striped rectangle included in the working time). figure 5: a complete detailed scheduling with two breakdowns for the 3rd IMS. figure 6: a complete scheduling with two breakdowns for two IMS (2nd and 3rd IMS) 5.2. Flexibility 5.2.1. Resource viewpoint - Modification of the production facility of an IMS: the decision system will be automatically acquainted with such a modification and will take this modification into account when responding to oncoming requests.
- Modification of the DPACS: addition (respectively suppression) of IMS will be automatically integrated to the structure when this IMS will be connected (respectively disconnected) to the IMS network: this IMS will naturally be able (respectively not able) to answer to oncoming requests. - Modification of the production facility of an IMS: the decision system will be automatically informed and will take this modification into account when answering to oncoming requests. 5.2.2. Product viewpoint The modification of any product characteristics is automatically integrated by the DPACS since the decision for processing is established dynamically. Of course, new products must be compatible with the production facilities. 5.2.3. Flow viewpoint The human operator may forbid/allow task execution, modify the input and output inventories of an IMS to perform priority tasks, force a particular task allocation to a specific IMS, re-allocate a task, etc. The DSS and the decision sub-system of each IMS have to support such alternatives. 5.2.4. Example This example (fig. 7&8) deals with the flexibility of the production structure. As shown on the following screens: from the date 0 up to the date 32, only six IMS form the set of the production resources (fig. 7). At the date 32, a seventh mill-ims is integrated to this structure (fig. 8) to absorb part of the extra-workload of the mill-ims1 considered as a bottleneck. figure 7: between the time 0 and 32, 6 IMS were working. figure 8: the complete scheduling, IMS 7 being integrated at the date 32. 6. Conclusion This paper shows that the DPACS provides very interesting characteristics for flexibility and random event supports. This kind of approach should be interesting for highly-
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