Jürgen Dorn

Comparison of iterative improvement techniques for schedule optimization

Abstract Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental comparison of four iterative improvement techniques for schedule optimization that differ in the local search methodology. These techniques are iterative deepening, random search, tabu search and genetic algorithms. To compare the performance of these techniques, we use the same evaluation function, knowledge representation and data from one application. The evaluation function is defined on the gradual satisfaction of explicitly represented domain constraints and optimization functions. The satisfactions of individual constraints are weighted and aggregated for the whole schedule. We have applied these techniques on data of a steel making plant in Linz (Austria). In contrast to other applications of iterative improvement techniques reported in the literature, our application is constrained by a greater variety of antagonistic criteria that are partly contradictory.

Reactive scheduling: improving the robustness of schedules and restricting the effects of shop floor disturbances by fuzzy reasoning

Abstract Practical scheduling usually has to reach to many unpredictable events and uncertainties in the production environment. Although often possible in theory, it is undesirable to reschedule from scratch in such cases. Since the surrounding organization will be prepared for the predicted schedule, it is important to change only those features of the schedule that are necessary. We show how, on one side, fuzzy logic can be used to support the construction of schedules that are robust with respect to changes due to certain types of event. On the other side, we show how a reaction can be restricted to a small environment by means of fuzzy constraints and a repair-based problem-solving strategy. We demonstrate the proposed representation and problem-solving method by introducing a scheduling application in a steelmaking plant. We construct a preliminary schedule by taking into account only the most likely duration of operations. This schedule is iteratively repaired until some threshold evaluation is found. A repair is found with a local search procedure based on Tabu Search. Finally, we show which events can lead to reactive scheduling and how this is supported by the repair strategy.

Iterative Improvement Methods for Knowledge-Based Scheduling

For large industrial applications the constraint-based formulation of scheduling problems fits better than mathematical representations from Operational Research, because the constraint approach is more flexible and can be adapted more easily to organizational changes in the production. However, the search for a good solution for realistic applications can be very expensive and furthermore, in scheduling one is not only interested in a feasible solution but also in an optimized solution.In this paper I present iterative improvement methods that can be used to optimize a schedule that is represented by constraints. These methods start with any schedule and try to optimize it by iterative modifications. The goal of the optimization method may be a minimization of the number of constraint violations or a maximization of a function that aggregates the satisfaction degrees of all involved soft constraints. Additionally, consistency techniques for constraints can be used to check a schedule after each modification. These problem solving methods have several benefits for realistic industrial applications. Since they can start with any preliminary schedule and can be interrupted anytime, they can be applied easily to reactive and cooperative scheduling. Further, they produce often better solutions in less time than other methods as was shown in several experiments.The paper gives an overview on important design decisions for iterative improvement methods, presents some of these methods in more detail and shows some results of our experiments made in applications of the steel-making industry.

Scheduling high-grade steelmaking

This expert system for scheduling high-grade steel production cuts engineering planning time, allows for greater what-if experimentation, and improves quality control. The complexity of production-cycle scheduling problems had defeated earlier, purely conventional software approaches.

Case-based reactive scheduling

Scheduling of operations in a production process is usually seen as a combinatorial search for a feasible and perhaps optimal plan. In actual industrial practice however, constraints and optimization criteria can often not be given exactly because they are unknown or vague. Therefore a combinatorial approach often does not meet the actual requirements. Furthermore, the representation of all potential alternatives of the production process would lead to a combinatorial explosion that cannot be solved in a reasonable time frame.

Expert Systems in the Steel Industry

EEL PRODUCTION INVOLVES A number of stages, such as melting, casting, rolling, and forging, that entail complex chemical and thermic reactions as well as intricate mechanical operations. Because these processes do not lend themselves to exact mathematical modeling, steel manufacturers must turn to techniques for reasoning with incomplete and uncertain data. Their decisions often rely on the experience of individual experts. Nearly all steelmakers worldwide now use expert systems, fuzzy logic, and neural nets to improve quality assurance and production efficiency. This special track of IEEE Expert looks at several typical, successfully fielded systems. For many years, the steel industry’s main objective has been to maximize production by automating processes and streamlining plant organization. As with the Republic of Korea’s Kwangyang Works, steel manufacturers have been erecting new plants from scratch, locating them near the sea to make the delivery of steel from blast furnace to final shipment as direct as possible. Because they restricted the diversity of their products, such new plants have become very competitive. Asia’s steel industry, in particular, used these approaches in the 1980s to produce high-quality steel cheaper than its Western competitors. (See the sidebar for historical overview of steel production.) However, the continuing improvement of substitutes for steel has raised the demand for even higher-quality steel with dedicated characteristics. By using different alloying metals and various heat and surface treatments, steelmakers now can offer a manifold of products. Ongoing research into new steel qualities has produced a broad range of products, which present many new’control problems. Although other industries reflect the same tendency toward processing in smaller lot sizes, the steelmaking environment shows more diversity than most because of the particular characteristics of its matenal and manufactunng technology Furthermore, the capital-intensive nature of the industry can make unanticipated violakons of technological constraints extremely costly A look at several typical factors will illustrate these considerations Most steelmaking processes are temperature-sensitive For each process, the steel must have aprescribed temperature, and any time it spends waiting on the next processing step will incur a costly reheating. Moreover, because chemical reactions depend on temperature, any loss of heat during processing may degrade the steel’s quality Iithe prescribed sohkfication temperature profile is violated, an incorrect internal structure of the steel might result Although process ames are difficult to predict precisely, steelmakers do exercise some degree of control. Treatment time in furnaces depends on the temperature, which can be controlled through heat input, generally sub-

Co-Operating Scheduling Systems Communicating Through Fuzzy Sets

Abstract A communication procedure for communicating scheduling expert systems based on fuzzy set theory is proposed. Fuzzy sets are used to express and to exchange constraints and their possible relaxations with other scheduling systems that can interpret these constraints. The procedure is intended to optimize the global evaluation among the communicating systems. An example from steel industry is taken to illustrate the approach. Here, a scheduling system of the steel making shop and that of the rolling mill try to optimize the global costs by optimizing their own schedules under the observation of the other systems constraints.

DEJA VU - A reusable framework for the construction of intelligent interactive schedulers

We describe the techniques of the DEJA VU Scheduling Class Library to achieve a library of reusable and extendible classes for the construction of interactive scheduling systems. The constructed systems shall be efficient and user centered. We describe abstract scheduling objects, constraints between them, and potential user interactions with the system. A first scheduling system was developed for the steel plant of Bohler Kapfenberg. We demonstrate which extensions were necessary and show prototypical examples from the graphical user interface.

Dependable reactive event-oriented planning

Abstract This paper presents a planner that reasons explicitly about time and a safe reaction in time. A distributed architecture supports usage of knowledge-based techniques to find effective solutions and procedural knowledge to react very fast on asynchronous events. If enough time is available, the knowledge-based planner ‘programs’ the procedural component with an improved reaction. The approach is illustrated by a railway control and transportation application that is relevant since it is safety-critical and co complex that knowledge-based techniques are necessary to master the complexity appropriately. The Planner is implemented in a PROLOG-environment.

Vague data management in production process scheduling applied to high-grade steelmaking

Abstract Mathematical-analytical methods as used in Operations Research approaches are often insufficient for planning problems. This is due to three reasons: The imprecise informations in the production process, combinatorial complexity of the search space, and conflicting objectives for production optimizing. The combination of several knowledge-based techniques, especially approximate reasoning and constraint relaxation, is a promising way to handle these problems. We use a case study to demonstrate how knowledge-based scheduling works with the desired capabilities. The applied knowledge representation technique covers the vagueness inherent in the problem domain by using fuzzy set theory. Based on this knowledge representation, the importance of jobs is defined. This classification of jobs is used for the straightforward generation of a schedule. We introduce a control strategy which comprises several types of constraints, namely organizational, spatial, and chemical ones. This strategy supports the dynamic relaxation of conflicting constraints in order to improve the schedule. To show the benefits of this strategy, the generation of a schedule for one day is explained in detail.