Combining Complementary Scheduling Approaches into an Enhanced Modular Software

Transcription

1 Combining Complementary Scheduling Approaches into an Enhanced Modular Software Jordi Cantón 1, Moisès Graells 1, Antonio Espuña 1, Luis Puigjaner 1* Wesley Alvarenga 2, Maria Teresa Rodrígues 2, Luis Gimeno 2. 1 Chemical Engineering Department, Universitat Politècnica de Catalunya. ETSEIB, Avda. Diagonal 647, Barcelona, Spain. 2 Chemical Engineering Department, Universidade Estadual de Campinas. UNICAMP, FEEC/DCA, CP , Campinas, SP, Brasil Abstract This work describes the integration of two scheduling tools addressing the scheduling problem from different points of view and using different strategies. An open framework allows combining software for detailed scheduling including shared storage and utility constraints with a tool based on the Constraint Based Search technique. The software aspects regarding the communication (open interfaces and XML messages) are described as well as the need for translating information at different levels of detail (e.g. ISA 88 and STN). Finally, the information provided by both approaches is discussed in terms of the advantages of a joint scheduling strategy as well as regarding the usefulness of such information for the decision-making process. Keywords: Scheduling, Constraint Based Search, information sharing 1. Introduction The scheduling and planning package MOPP (Multipurpose Operation Production Planner) is reengineered as a modular open framework following the CAPE-Open standards that allow using additional components in a distributed way (Cantón, 2003). Basic MOPP modules focus on detailed batch scheduling, which require taking into account a large amount of operational constraints that cannot be easily introduced in optimisation approaches. Hence heuristic/simulation approaches as those encountered in APS systems (Advanced Planning and Scheduling) may be easily integrated and used. These modules have been especially addressed to the modelling and optimisation of complex manufacturing systems (batch chemical processes, pharmaceutical, fine chemicals, etc.), for which recipes involved require a detailed description of tasks and subtasks as well as the hard time and storage constraints implicated (simultaneous activities, unstable intermediates, product synthesis and other concurrent processes...). This work describes the introduction (plug in) into the MOPP framework of a complementary module aimed to obtaining batches processing time windows utilizing constraints propagation mechanisms. This additional module has been obtained by wrapping the original PCPIP package (Planejamento e Controle da Produção na * Author to whom correspondence should be adressed : <luis.puigjaner@upc.es>

2 Indústria de Processos) (Rodrigues et al, 2000) by a different research group working in Constraint Based Search scheduling techniques in short term scheduling. One of the issues addressed is due date feasibility, and the analysis of different scenarios where some due dates are relaxed. Once processing time windows are send from PCPIP to MOPP this information becomes a useful tool that gives the user (or a software application) a measure of the remaining flexibility in batches allocation. This information aids selecting appropriate timing procedures and/or manual changes in the MOPP Gantt chart. Batches processing time windows are obtained initially from the planning phase. For each batch its time window is a interval starting at the time where input materials and equipment unit are available (est earliest starting time) and finishing at its due date (lft latest finishing time). Material balances and resource capacity limits (equipment units and cumulative resources) also bound these intervals since they may force orderings among batches, reducing time windows span through increases in est and/or cuts in lft. Such constraint propagation methods are used by commercial packages implementing Constraint Based Search techniques such as ILOG Solver and Scheduler (ILOG, 1997). 2. MOPP open framework MOPP is designed as an open environment allowing the connection between different modules (plug-ins) which collaborate in order to perform all the tasks needed in a scheduling environment. The basic idea of the MOPP structure is given in figure 1. The MOPP core has only an interfaces repository and deals only with the plug-in management. The different plug-ins available can be loaded into the system using a plug-in manager which takes care of the dependencies between the different plug-ins connected. Figure 1. MOPP structure Each plug-in is characterised by the exposed and required interfaces. Once loaded, the exposed interfaces of each plug-in are registered in the interface repository. The repository is accessible by all the plug-ins, allowing the communication between them. Usually the plug-ins are not designed to work alone, thus access to the other plug-ins trough the references contained in the interface repository is required to make possible that several plug-ins work together. Each plug-in can ask the interface repository for access to another plug-in connected to the system.

3 2.1 Standard Modules The different MOPP functions are given by a standard plug-in set. The main of them: Data: It manages all the data related to MOPP. The data structure is based in ISA S88, which allows the definition of complex recipe structures. Data persistence is given by XML files and also a relational SQL database. Timing & Modeling: This module provides the start and ending times, as well as the resource profiles, for a schedule defined by set of discrete decisions (batch sizes, assignment and sequence). This is successfully reached by implementing automatic generation and solving of an EON model, which can handle storage, calendar and resource constraints as well as any complex recipe structure defined in the data module. (Graells et.al., 1998; Cantón et.al., 2000; Cantón, 2003). Sequencing: This module input is the list of demands to be met, from which it generates a set of ordered batches with a specific assignments. This function is carried out by a standard set of greedy methods for sequencing, material balance and assignment (earliest due date, lowest storage level, etc). However, the very nature of these rules makes most of these sequences not to meet due dates. The approach is enhanced by interacting with a third-party module such as PCPIP. Optimiser: This module manager searches for improved or optimal discrete decisions, following a number of strategies and algorithms. MOPP offers by default four generic modules of this kind. Three of them implement stochastic optimisation algorithms (SA, MSES and GA). The other one generates a MILP based on the EON model and solves it using GAMS (Cantón et.al., 2001). OF Evaluation: This module allows the generation and evaluation of different objective functions. Completely new objective functions could be generated programming a new plug-in and adding it into the system. GUI: There are several modules which offer graphical user interfaces. These modules allow the interaction with all the data and calculation modules available in MOPP. New user interfaces can be plugged into the system to generate a complete customized solution for a given industrial case. With all the modules and architecture described, MOPP is capable to accept further improvements with little effort, as the integration of the PCPIP module has proved. 3. Tasks processing time windows approach PCPIP (Rodrigues et al., 2000) is a set of tools for planning and scheduling intended for make-to-order situations where end products due dates are of primary importance. From a specific demand of end products in terms of quantities and due dates, and an availability plan for raw materials, the planning phase determines the number of batches for each task and a processing time window for each batch. A batch processing time window starts at its earliest starting time- est and ends at its latest finishing time- lft. Processing time windows are used by Constrained Based Search (Das et al., 1998), (ILOG, 1997) to limit the domain of starting times. The domains are updated (reduced) after each single allocation by constraint propagation mechanisms (Caseau et al. 1994). A set of constraint propagation mechanisms has been implemented in PCPIP including mass balance constraints, ZW, NIS and FIS constraints, and capacity constraints on unary resources like equipment units. After the planning phase, and prior to the

4 scheduling phase, constraint propagation mechanisms are used to obtain batches time windows, which allow analysing plan feasibility in terms of due dates fulfilment. If feasible, estimated equipment workload and slack times allow plan flexibility analysis. A subset of PCPIP modules containing constraint propagation and plan analysis tools has been wrapped to be plug into the MOPP environment. It receives from MOPP planning results, namely batches, mass precedence constraints among batches and planning horizon. Its output are batches time windows and equipment units load. Both types of information are exchanged through XML files. 4. Modules interaction A drawback of some greedy algorithms used by the Sequencer module to generate the initial schedules (Cantón, 2003) is that they lack of look-ahead mechanisms that could help generating a feasible schedule in terms of due-dates. The use of the time-windows module allows contemplating this aspect, learning in advance if due-dates can be meet or not. Figure 2 shows this interaction: after running the sequencing module, batches, assignment, precedence constraints and time constraints (scheduling horizon and due dates) are sent to PCPIP. Constraint propagation next determines batches time windows, which in turn lead to equipment load estimation. This information is used in different ways. For example, calendar constraints can be relaxed in order to allow greater allocation flexibility. Begin Call to PCPIP Begin PCPIP Generate STN from ISA S88 model Read STN from XML file Generate Precedence Graph Read Precedence graph from XML Write XML Perform constraint propagation Call PCPIP Generate Time Windows Read Time windows XML Write Time windows results to XML End End Figure 2. Detailed interaction with PCPIP. Batches time windows can only be obtained after assignment. Different assignment scenarios determined by the sequencer module may lead to very different situations in terms of time windows and specially in terms of equipment load. This allows to utilise time windows as a new filter layer for the sequencer algorithm and to test alternative assignment rules in order to find a sequence meeting the due-dates provided. The whole process is illustrated below:

5 Begin Global material balance Assignment Generate Batches PCPIP Plugin Get Time-Windows Generate New Assignment Yes Can due-dates be met? Yes Final Sequence & timing using time windows information No New Assignment? No Final Sequence & timing without using time windows information End User Warning Figure 3. Sequencer algorithm interacting with the PCPIP. 5. Case Study Figure 4 shows the use of the information given by the time windows analysis. The case is that by Papageougiou and Pantelides (1993) and the results given by PCPIP are sent via XML to the Electronic Gantt Chart. A GUI utility has been implemented to show the user the time window where manual shifting would produce feasible schedules. Figure 4. View of the time window associated to the task being manually moved (via mouse). Time windows are given in the XML file produced by the PCPIP module.

6 Since time-windows provide a narrower search space, a next step for future work is to use the time windows information to speed up the optimisation procedures. 6. Conclusions A flexible and modular framework has permitted to implement two complementary approaches to scheduling problems, and to combine their advantages by using the same data model. This allowed overcoming some practical limitations of both approaches, by reducing the search space and by adding to the optimisation procedure new degrees of freedom (such as ending times or sequencing constraints) that are usually considered as data when each one of these approaches is used alone. Software integration can be envisaged in a similar way: the MOPP structure, based on the emerging standards, can support fully customized user interface and calculation modules that allow incorporating all the know-how of the specific industrial scenario. Additionally, the time windows approach would allow the generation of new constraints that can be also used to eliminate a priori unfeasible solutions in the search procedure. Acknowledgements The work has been partially supported by Project 034 of the CAPES/MECD (Brasil/Spain) program. References Caseau Y. and Laburthe F., 1994, Improved CLP Scheduling with Tasks Intervals. Proceedings Eleventh International Conference on Logic Programming. Ed. P. van Hentenryck, The MIT Press. Canton, J., Graells, M., Espuña, A., Puigjaner L., 2000, Modeling the complexity of the intermediate storage for the scheduling of multipurpose batch chemical processes using event operation networks, 14 th International Congress of Chemical and Process Engineering, CHISA-00. Cantón, J. Graells, M., Espuña, A. Puigjaner, L., 2001, A New continous time model for the short-term scheduling of batch processes, 4 th Conference on Process Integration, Modeling and Optimisation for Energy Saving and Pollution Reduction (PRES 01). Cantón, J., 2003, Integrated support system for planning and scheduling of batch chemical plants. Ph.D. Thesis, Universitat Politècnica de Catalunya. Graells, M., Cantón, J. Peschaud, B., Puigjaner L., 1998, General approach and tool for scheduling of complex production systems, Comput. Chem. Engng., 22S, S395- S402. Das B.P., Shah N. and Chung P.W.H., 1998, Off-line scheduling a simple chemical batch process production using the ILOG scheduler. Comput. Chem. Engng., 22S, S947-S950. ILOG., 1997, Ilog Scheduler 4.0 User Manual. Mountain View, USA. Papageorgiou, L.G. and C.C. Pantelides, 1993, A hierarchical approach for campaign planning of multipurpose batch plants, Comput. Chem. Engng, 17, Rodrigues M.T., L.G. Latre and L.C Rodrigues, 2000, Production Planning Using Time Windows for Short-Term Multipurpose Batch Plants Scheduling Problems. Ind. Eng. Chem. Res., 39,