Practical scheduling problems generally require allocation of resources in the presence of a large, diverse and typically conflicting set of constraints and optimization criteria. The ill-structuredness of both the solution space and the desired objectives make scheduling problems difficult to formalize. This paper describes a case-based learning method for acquiring context-dependent user optimization preferences and tradeoffs and using them to incrementally improve schedule quality in predictive scheduling and reactive schedule management in response to unexpected execution events. The approach, implemented in the CABINS system, uses acquired user preferences to dynamically modify search control to guide schedule improvement. During iterative repair, cases are exploited for: (1) repair action selection, (2) evaluation of intermediate repair results and (3) recovery from revision failures. The method allows the system to dynamically switch between repair heuristic actions, each of whi...

Distributed constraint optimization imposes considerable complexity in agents' coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent structures involves disparity among subproblems. We present a coordination mechanism, Anchor&Ascend, for distributed constraint optimization that takes advantage of disparity among subproblems to efficiently guide distributed local search for global optimality. The coordination mechanism assigns different overlapping subproblems to agents who must interact and iteratively converge on a solution. In particular, an anchor agent who conducts local best first search to optimize its subsolution interacts with the rest of the agents who perform distributed constraint satisfaction to enforce problem constraints and constraints imposed by the anchor agent. We focus our study on the well-known NP-comple...

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a #rst step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four di#erent versions of the auction mechanism using job shop scheduling proble...

We present a methodology, called Constraint Partition and Coordinated Reaction (CP&CR), where a problem solution emerges from the evolving computational process of a group of diverse, interacting, and well-coordinated reactive agents. Problem characteristics are utilized to achieve problem solving by asynchronous and well coordinated local interactions. The coordination mechanisms guide the search space exploration by the society of interacting agents, facilitating rapid convergence to a solution. Our domain of problem solving is constraint satisfaction. We have applied the methodology to job shop scheduling with non-relaxable time windows, an NP-complete constraint satisfaction problem. Utility of different types of coordination information in CP&CR was investigated. In addition, experimental results on a benchmark suite of problems show that CP&CR performed considerably well as compared to other centralized search scheduling techniques, in both computational cost and number of proble...

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new generation of scheduling methods based on a distributed and locally autonomous decision structure. Specifically, we propose a decision structure based on auction theory. The basic idea is to localize and distribute the functionality of scheduling, leaving the complexity of operational decisions to local decision makers, while maintaining a simple and generic central coordination mechanism. The proposed structure allows local decision makers to make their decisions dynamically and independently according to changes in their local environments. A central coordination mechanism then makes resource allocation based on an iterative auction process using information obtained from local decision makers. We propose following research endeavors: ffl We will study the decomposition of monolithic optimization models which provides the basis for...

In this report, we summarize the research performed under Advanced Research Projects Agency (ARPA) contract F30602-90-C-0119, Flexible Coordination in ResourceConstrained Multi-Agent Domains. The broad goal of this research, which was carried out as part of ARPA/Rome Laboratories Planning Initiative (PI), has been to investigate the use of constraint-based scheduling frameworks and techniques as a basis for more accurate and more flexible decision support at various stages of the military crisis-action planning, deployment and employment process. This work has led to development of a transportation scheduling system called DITOPS, which provides advanced capabilities for construction, analysis and revision of large-scale deployment schedules. A version of this report also appears as Rome Laboratories Technical Report RLTR -94-95. 1 Introduction and Overview In this report, we summarize the research performed under Advanced Research Projects Agency (ARPA) contract F30602-90-C-0119, F...