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Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 A case-based expert system for scheduling problems with sequence dependent set up times B.L. MacCarthy, P. Jou Department of Manufacturing Engineering and Operations Management, University of Nottingham, Nottingham NG7 2RD, Abstract Scheduling is an important application area for expert systems. However, the development of expert systems in scheduling poses many problems and many different approaches have been attempted in prototype systems. This paper discusses the use of case-based reasoning (CBR) in the development of expert scheduling systems. The design and development of an expert system using CBR is described. The application area is an important class of scheduling problems those involving sequence dependent set up times. This type of problem is important in many production environments and arises frequently in the process industries. The paper addresses general problems and research issues related to the application of CBR to scheduling problems. 1 Introduction Scheduling is defined by Baker [1] as the allocation of resources over time to perform a collection of tasks. In classical deterministic scheduling theory a set of jobs must be completed on a set of machines in a manner which optimizes some performance measure. All the parameters are assumed to be known in advance. In the classical theory this often reduces to a sequencing problem. Since the paper of Johnson [2] on the two-machine flowshop problem, the sequencing literature has grown enormously. Many such deterministic models have been studied over the past forty years in operational research (OR). However, the relevance of this research to production management practice has been questionable. Harrison [3] pointed out that long lead times, excessive inventories, poor due date performance and low customer service levels are problems still faced by many manufacturing managers and production Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 90 Artificial Intelligence in Engineering controllers. Most researchers would admit that scheduling research has not given the kind of payback that the volume of research deserved (MacCarthy and Liu [4]). In general, from a combinatorial perspective, scheduling problems are NPhard and real scheduling environments tend to have further layers of complexity. Parunak [5] describes scheduling complexity, showing that existing techniques address only isolated parts of the problem. Charalambous and Hindi [6] state that classical scheduling fails to meet practical requirements in three ways : models are oversimplified, approaches cannot deal with multiple objectives and heuristics cannot be integrated into the mathematical models. Scheduling environments are inherently dynamic, uncertain and complex and consequently are difficult to cope with. Different environments require different techniques. The two main research themes in the literature are OR and artificial intelligent (AI) approaches. OR employs a modelling approach where the emphasis is on algorithmic and heuristic solution methods. However there is a considerable gap between the OR research literature and real scheduling problems (MacCarthy and Liu [4]). Many AI scheduling prototype systems have been reported - ISA. DEVISER, ISIS, ISIS-2, OPIS, OPAL, FIXER and PLANNET (White [7]). However, many problem areas arise in using AI approaches in scheduling (White [7] and Grant [8]). A key insight of AI scheduling work is the importance of constraints and constraint modelling (Grant [8] and Fox et al [9]). Charalambous and Hindi [6] reviewed 20 systems, noted that the majority were still prototypes and concluded that hybrid solution may be the way forward. The combination of OR and AI approaches may enhance scheduling system capability significantly. Case-based reasoning (CBR) is an important development in AI in the last decade with a number of important successful applications (Kolodner [10]). This paper discusses the application of CBR to scheduling problems. The approach is discussed in relation to an important class of practical scheduling problems. The approach allows the utilization of both OR and AI techniques. A generic control strategy is described for a CBR based expert system for the problem domain. Important research issues in system design and implementation are highlighted. 2 Case-based Expert Systems in Scheduling Sadeh [11] describes as a major challenge, the development of a scheduling tool that can: meet more precisely actual constraints and objectives; be used for on-line real-time scheduling; allow users to manipulate the schedule interactively to reflect user-dependent preferences and constraints. The user must be helped if critical situations arise e.g. : where the user may have little or no experience. The most appropriate technology available, as Paul [12] indicated, to address such intellectually demanding tasks is expert system technology. The first requirement in using an expert system approach is that the relevant knowledge Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 Artificial Intelligence in Engineering 91 must exist. If there is insufficient heuristic or episodic knowledge an expert system approach cannot be applied. In scheduling problems, theoretical knowledge and some heuristics exists. Unfortunately, practical knowledge and experience may be of limited value. A number of review papers have been published in this area (Steffen [13], Kusiak and Chen [14], Charalambous and Hindi [6], Noronha and Sarma [15]). It is clear that most researchers use rules in knowledge representation and constraint directed search in control strategies. Ye and Hughes [16] note that job shop scheduling can be viewed as a constraint satisfaction problem. Case-based reasoning (CBR) was conceived in the late 1970's and the first systems started to be built in the early 1980's by Schank's students (Kolodner [10]). Riesbeck and Schank [17] provide an introduction to CBR. Watson and Marir [18] give a detailed review of CBR and CBR tools. At its simplest a case-based reasoning system focuses on previous situations similar to the current one and uses them to help in solving a new problem. Many management decision areas rely on previous experience and CBR has many potential applications in managerial decision making. Although CBR does have limitations it has many potential advantages. Experience may be re-used for instance. CBR is also useful where information may be uncertain. Measures of similarity may be used to retrieve cases and potential solution strategies. CBR will usually require adaptation techniques to develop solution strategies for the current temporal case. However it allows solutions to be proposed quickly for problem domains which are not completely understood and it gives a means of obtaining solutions when no algorithmic methods are available. On the negative side cases may be used blindly without adequate validation and bias may be introduced (Kolodner [10]). The work described in this paper uses a CBR approach for the development of an expert scheduling system for a particular application. In the proposed system CBR is treated like a meta-rule in developing solution strategies. First, simulation is used to generate cases including satisfied and unsatisfied schedules; second, cases are retrieved to a temporal case base where matching is by constraints; third, methods are selected from temporal case base by global objectives and a relative ranking system. The problem domain is the class of scheduling problems known as sequence dependent set up problems. 3 The Sequence Dependent Set Up Problem Production scheduling in many industries must consider sequence dependent set up constraints - environments in which the set up time is dependent on what a machine is currently producing and what is scheduled to follow. This type of scheduling problem is encountered frequently (Gupta [19], Srikar and Ghosh [20], Selen and Heuts [21]) and in some industries these are the dominant scheduling constraints, particularly in process industries e.g. paint manufacture, printing, paper and container manufacture, and textile dyeing and weaving. Though there has been extensive research on scheduling, much of this either Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 92 Artificial Intelligence in Engineering totally ignores set up times, or considers them as being independent of the job sequence. Set up times occupy a substantial percentage of the available production time on the manufacturing equipment in many industries. Daoud and Purcheck [22] used statistical analysis of machine utilization to show that about 45% of machine time is spent in tool changes between jobs. Reducing job set up times can therefore make a substantial difference in machine utilization. As an example, Zhou and Egbelu [23] present the following three machine, four job problem. Figure 1 shows the machine and the processing time units, respectively, required for each operation of each job. Operation 1 Operation2 Operation 3 Operation4 Jobl 1 / 5 3 /9 2 /9 3 /9 Job2 1 / 9 2/ 15 1 / 13 1 / 7 Job3 3 /6 3 /6 — Job4 2 /9 2 / 10 Figure 1 The machine and the processing time units Operation5 2 / 12 - - - - - In addition the set up times in changing between job operations for machines 1, 2 and 3 were specified (not given here). Zhou and Egbelu [23] proposed a solution approach integrating a scheduling algorithm with the experience of an expert human scheduler. The approach performed reasonably well. For the problem above which has an optimal schedule of 80 time units (obtained from integer programming) the approach gave a solution of 82 time units in attempting to minimize the maximum flow time (Fmax). However this approach is applicable for one goal only. Other algorithms would be necessary for other goals and there is no guarantee that this algorithm will always do as well in other problems, even for maximum flow time. Different goals might be necessary at different times and for different circumstances. The specified constraints here are all hard constraints. In practice some constraints may be soft and may be violated in some circumstances. The data may be subject to inaccuracies. Some constraints may arise which are temporary e.g. it may be desirable in this instance to complete job 1 before job2. Only an expert system approach can handle this required level of functionality. The approach adopted here is to use GBR techniques in developing such an expert system. 4 Proposed System Structure The structure of the proposed system combines 12 modules and 3 bases. An overview of the top level architecture is illustrated in Figure 2. Some of the Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 Artificial Intelligence in Engineering 93 Figure 2 : System Architecture main elements of expert system architecture are discussed here. The case base consists of problem solving cases and data relevant to the problem domain. The method base contains optimization and heuristic procedures relevant to sequence dependent set up problems. The constraints module contains the relevant constraints specified. The relaxation module is activated when all constraints cannot be met. The case selector contains the principles that determine, for different constraints, criteria and data, the selection of previous cases. The temporal case module stores the cases that are selected by the case selector module. The adaptor module adjusts variables after scheduling and rebuilds the case base. This high level architecture facilitates interactive and reformulative scheduling strategies by using combined heuristics and algorithms to generate schedules, case-based reasoning to obtain more powerful and precise solution strategies and interactive user manipulation of the variables that are time dependent. 5 Research Issues in Developing, Testing and Implementing the System The expert system shell KAPPA-PC has been chosen as the implementation tool for the system development because it allows object-oriented programming, has a graphics user interface and is easy to interact with other software by dynamic data exchange (DDE). The use of an object-oriented approach considers modularity and reusability so that the system can be expanded in a step by step manner. The system can communicate by DDE with graphical software for plotting during run time. Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 94 Artificial Intelligence in Engineering It is important that schedules reflect accurately the current environment including the external constraints as well as the internal operational constraints. The quality of scheduling is affected by the view of the user about the combination of constraints that currently applies. During scheduling, there are different sets of key constraints having direct impact on the quality of scheduling. Constraints are not all of the same type or importance in scheduling. Typical constraints include : • time dependent constraints : specifying the variables which change with time and their distribution functions. • machine dependent constraints : limiting the types, the availability and the capacity of machines that can operate on a job. • precedence dependent constraints : existing between jobs or operations. • sequence dependent constraints : existing between jobs, operations in a job and machines. • user dependent constraints : specifying personal preferences, organizational objectives etc. • environment dependent constraints : restricting physical location, layout, handling and transportation etc. It may be possible to satisfy some subset of constraints in scheduling but some may conflict with each other. Relaxation of constraints may be necessary in some instances and desirable in others. For instance, a due date - user dependent constraint - may need to be relaxed (a so-called soft constraint) for some jobs because of lack of availability in a machine dependent constraint. Constraint relaxation is a suitable means of handling such a conflict. Some constraints cannot be relaxed (a so-called hard constraint) e.g. precedence constraints for job operations. Constraints may change with time in such a manner that we are left with a situation where constraints are not either strictly true or false. With this uncertainty, it would be unwise to attempt to program constraints into rules in the expert system. However, using constraint directed retrieval in case-based reasoning may provide a workable approach. In previous scheduling expert systems, the control module which generates the schedule acts like a black box. Using constraint directed retrieval can allow users to define their constraints. This allows system "tuning" via user's knowledge. Although some researchers claim that knowledge acquisition is not a problem in CBR (Watson and Marir [18]) the issue does arise in the development of the system proposed here. An example is case indexing where indices are assigned to cases in order to direct the case retrieval process. Rules for indexing contain knowledge that determines which constraints will have an impact on scheduling. In the proposed system the case selector is one of the most important modules. The system allows the user to set up his/her own priority of constraints in the constraint directed retrieval of cases from the case base. This user view allows relaxation of soft constraints and control over the degree of relaxation. When creating a detailed user view, a temporal checking list is included to deal Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 Artificial Intelligence in Engineering 95 with the conflict or interaction of constraints. When the user specifies the current constraints, the constraint module searches the case base for previous cases in which constraints are in conflict. The searching result is kept in the temporal checking list. Nearest-neighbour matching is applied by checking the similarity of both constraints and performance measure values. Simulation is used to generate cases for the case base when no cases exist but just algorithms and heuristics in the method base. The starting point is to generate data and obtain performance measures and case fitness values by simulation and then incrementally build the case base. The structure of the case base includes each constraint and performance measure types and their values. The development environment first includes the single machine with sequence dependent set up times problems. More complex flow-shop and jobshop environments follow from this initial work. Relevant algorithms and heuristics are being programmed into the method base. Gradually, the system hopes to cover job-shop environments and the heuristics will include opportunistic scheduling approaches (Ow and Smith [24] and Sadeh [11]). 6 Conclusion and Future Work Current work is concerned with the development and evaluation of different algorithms and heuristics for the method base and the development of structures for case representation and indexing in the proposed system. The methods for the method base are being explored in order to exploit the full potential of the algorithms and heuristics. The work has highlighted the need for fresh comparative studies of different methods and different approaches for case indexing. Control strategies to allow user interaction and reformulation are being considered, particularly in order to utilize the user's knowledge of current environmental conditions and factors. The research work so far has shown that there are many opportunities for future work on improving the linkage of casebased reasoning to expert scheduling systems. References 1. Baker, K.R. Introduction to Sequencing and Scheduling, John Wiley, 1974. 2. Johnson, S.M. Optimal two- and three-stage production schedules with setup times included, Naval Research Logistics Quarterly, 1954, 1, 61-68. 3. Harrison, M. Planning to Control Shop Floor Activities, Engineering Computers, July, 1991, 12-17. 4. MacCarthy, B.L. and Liu, J, Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling, Int. 7. offW. /?<%., 1993, 31, 59-79. 5. Parunak, H.V.D. Characterizing the manufacturing scheduling problem, Journal of Manufactunng System, 1990, 10, 3, 241-259. 6. Charalambous, O. and Hindi, K.S. A review of artificial intelligence-based job shop scheduling systems, Information and Decision Technologies, 1991, Transactions on Information and Communications Technologies vol 8, © 1995 WIT Press, www.witpress.com, ISSN 1743-3517 96 Artificial Intelligence in Engineering 17, 189-202. 7. White, P.K.Jr. Advances in the theory and practice of production scheduling, Control and Dynamic Systems, 1990, 37, 115-157. 8. Grant, T.J, Lessons for OR from AI, Journal of the Operational Research Society, 1986, 37, 1, 41-57. 9. Fox, M.S., Smith, S.F., Allen, B.P. and Strohm, G.A. Job-shop scheduling: an investigation in constraint-directed reasoning, Proceedings ofAAAI-82, Carnegie-Mellon University, Pittsburgh, Pa, U.S.A., 1983. 10. Kolodner, J. Case-based reasoning, Morgan Kaufmann, 1993. 11. Sadeh, N. Micro-opportunistic scheduling: the micro-boss factory scheduler, Topics in Intelligent Scheduling, Morgan Kaufmann, 1994. 12. Paul,J. Building expert systems for knowledge-poor domains, Proceedings of Expert Systems 93, pp. 223-233, Cambridge, December, 1993. 13. Steffen, M.S. A survey of artificial intelligence-based scheduling systems, pp, 395-405. Proceedings of the Industrial Engineering Conference, 1986. 14. Kusiak, A. and Chen, M. Expert systems for planning and scheduling manufacturing systems, Eur. J. ofOper. Res., 1987, 34, 113-130. 15. Noronha, S.J. and Sarma, V.V.S. Knowledge-based approaches for scheduling problems: A survey, IEEE Transactions on Knowledge and Data E/72z"f7ffnV?g, 1991, 3, 2, 160-171. 16. Ye, P. and Hughes, J.G. A method for solving the job shop scheduling problem in the CLP paradigm, pp. 331-339, Proceedings of Expert Systems 94, Cambridge, December, 1994. 17. Riesbeck, C.K. and Schank, R.S. Inside Case-based Reasoning, Northvale, NJ:Erlbaum, 1989. 18. Watson, I.D. and Marir, F. Case-based reasoning: a review, TJie Knowledge Engineering Review, 1994, 9, 3. 19. Gupta, S.K. 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