: This paper proposes an extension of the constraint-based approach to job-shop scheduling, that accounts for the flexibility of temporal constraints and the uncertainty of operation durations. The set of solutions to a problem is viewed as a fuzzy set whose membership function reflects preference. This membership function is obtained by an egalitarist aggregation of local constraint-satisfaction levels. Uncertainty is qualitatively described is terms of possibility distributions. The paper formulates a simple mathematical model of jobshop scheduling under preference and uncertainty, relating it to the formal framework of constraint-satisfaction problems in Artificial Intelligence. A combinatorial search method that solves the problem is outlined, including fuzzy extensions of well-known look-ahead schemes. 1. Introduction There are traditionally three kinds of approaches to jobshop scheduling problems: priority rules, combinatorial optimization and constraint analysis. The first kind ...

A Holonic Manufacturing System HMS is a manufacturing system where key elements, such as machines, cells, factories, parts, products, operators, teams, etc., are modeled as `holons' having autonomous and cooperatie properties. The decentralized information structure, the distributed decision-making authority, the integration of physical and informational aspects, and the cooperative relationship among holons, make the HMS a new paradigm, with great potential for meeting today's agile manufacturing challenges. Critical issues to be investigated include how to define holons for a given problem context, what should be the appropriate system architecture, and how to design effective cooperation mechanisms for good system performance. In this paper, holonic scheduling is developed for a factory consisting of multiple cells. Relevant holons are identified, and their relationships are delineated through a novel modeling of the interactions among parts, machines, and cells. The cooperation mechanisms among holons are established based on the pricing concept of market economy following `Lagrangian relaxation' of mathematical optimization, and cooperation across cells is performed without accessing individual cells' local information nor intruding on their decision authority. The system also possesses structural recursivity and extendibility. Numerical testing shows that the method can generate near-optimal schedules with quantifiable quality in a timely fashion, and has comparable computational requirements and performance as compared to the centralized method following single-level Lagrangian relaxation. The method thus provides a theoretical foundation for guiding the cooperation among holons, leading to globally near-optimal performance.

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue. In this paper

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There has been significant progress in the area of short-term scheduling of batch processes, including the solution of industrial-sized problems, in the last 20 years. The main goal of this paper is to provide an up-to-date review of the state-of-the-art in this challenging area. Main features, strengths and limitations of existing modeling and optimization techniques as well as other available major solution methods are examined through this paper. We first present a general classification for scheduling problems of batch processes as well as for the corresponding optimization models. Subsequently, the modeling of representative optimization approaches for the different problem types are introduced in detail, focusing on both discrete and continuous time models. A comparison of effectiveness and efficiency of these models is given for two benchmarking examples from the literature. We also discuss two real-world applications of scheduling problems that cannot be readily accommodated using existing methods. For the sake of completeness, other alternative solution methods applied in the field of scheduling are also reviewed, followed by a discussion related to solving large-scale problems through rigorous optimization approaches. Finally, we list available academic and commercial software and briefly address the issue of rescheduling capabilities of the various optimization approaches.

This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate the need for systems employing artificial intelligence methods for scheduling. This leads to a need for incorporating adaptive methods--learning.

Consumer demand and current computational capabilities are driving the manufacturing complex from mass production to mass customization. Current barriers to mass customization have less to do with manufacturing machinery and more to do with the manual and computerized information systems currently used to control that machinery. On-line commerce offers potential benefits and functionality far beyond the automated catalogs that characterize today's cybermarkets. The AARIA project (Autonomous Agents for Rock Island Arsenal) demonstrates how agent technologies and Internet communications can support this expanded vision. This paper outlines new directions that we expect trade on the Internet to take, and shows how AARIA's architecture, scheduling approach, and simulation capabilities support these new directions.

Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research. KEY WORDS: priority dispatching rules, single machine, rule discovery, genetic programming

This dissertation describes several advances to the theory and practice of artificial intelligence scheduling and constraint-satisfaction techniques. I have developed and implemented these techniques during the construction of DTS, the Decision-Theoretic Scheduler, and its successor, SchedKit, a toolkit of scheduling algorithms and data structures. The dissertation describes and analyzes the three orthogonal approaches to improving a scheduler’s performance. These are: (1) reducing the size of the state space to be searched, (2) reducing the per-state cost of state generation and evaluation, and (3) reducing the number of states examined by selective search. To reduce the size of the state space, I have developed several new preprocessing algorithms designed to exploit resource constraints, including resource capacity and resource/task compatibility. Experiments show that it is possible to exploit resource capacity constraints efficiently despite their inherently disjunctive nature. To reduce the cost of state generation, I employ computational geometry data structures that optimize incremental heuristic evaluation, constraint-checking and state-variable maintenance. These data structures can be compiled from a formal attribute grammar specification of the heuristics and constraints. Experience with

The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a prede®ned event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SI X, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate signi®cant improvements.