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...

A great deal of research has been focused on solving the job-shop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive...

Coordination of the participants in the supply chain of a manufacturing enterprise is a key to agile reaction to unexpected events. As a starting point, we take a mediated approach to coordination: a single agent is responsible for recovery of the supply chain from a disruptive event. This mediator gathers commitment information from other agents and forms a constraint graph. If the event is truly disruptive, this graph will reflect an infeasibility: a subset of agents can no longer meet commitments. Repair of the graph is done via constraint relaxation controlled by the mediating agent. We present a schema for constraint relaxation algorithms and experimental results on Partial Constraint Satisfaction Problems (PCSPs). We sketch the coordination protocol that is being developed. 1.0 Introduction Dynamic events in a multiagent environment can have significant impact on the ability of agents to meet commitments made to other agents. If the network of commitments is viewed as a constra...

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There are generally three approaches to constraint satisfaction and optimization: domain-filtering, tree-search labelling and solution repair. The main attractions of repair-based algorithms over domainfiltering and/or tree-search algorithms seem to be their scalability, reactivity and applicability to optimization problems. The main detraction of the repair-based algorithms appear to be their failure to guarantee optimality. In this paper, a repair-based algorithm, that guarantees to find an optimal solution if one exists, is presented. The search space of the algorithm is controlled by no-good backmarking, a learning process of polynomial complexity that records generic patterns of no-good partial labels 3 . These no-goods serve to avoid the repeated traversing of those failed paths of a search graph and to force the search process to jump out of a local optimum. Unlike some similar repair-based methods which usually work on complete (but possibly inconsistent) labels, the propose...

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Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The notion of dynamic CSP (DCSP) [DD88] has been proposed to represent such evolutions. The problem we consider here is the solution maintenance problem in a DCSP. Naively applying usual satisfaction algorithms to this problem results in redundant search and inefficiency. Two distinct approaches to this problem are proposed in this paper. The first one, called the Local Changes approach, reuses the previous solution (if any) to compute a new solution. The second one, called the Nogood Recording approach, tries to store in a concise way, an approximate description of the front...

This paper presents the design and implementation of an air-crew assignment system for producing and refining a solution to this problem based on the Artificial Intelligence principles and techniques of abductive reasoning as captured by the framework of Abductive Logic Programming (ALP). The system offers a high-level of flexibility in addressing both the tasks of crew scheduling and rescheduling. It can be used to generate a valid and good quality initial solution and then to help the human operators adjust and refine further this solution in order to meet extra requirements of the problem. These additional needs can arise either due to new foreseen requirements that the company wants to have or experiment with for a particular period in time or due to unexpected events that have occurred while the solution (crew-roster) is in operation. Our work shows the ability and flexibility of abduction, and more specifically of ALP, in tackling problems of this type with complex and changing r...

The Constraint Satisfaction Problem (CSP) is one of the areas of artificial intelligence where a significant amount of problem classification and complexity analysis has co-occurred. For a large class of scheduling problems, described in a CSP notation, we propose a hypergraph as an underlying structure. We show that using causal theories, based on the nature of constraints, can lead to a significant efficient improvement in solving this class of scheduling problems. The complexity of the new strategy is analyzed as well.

This paper presents a non-classical approach supported in fuzzy theory to solve short-range planning and scheduling problems. The proposed method is based on the hierarchical structure presented in [8, 9]. It has three control levels, each one of them responsible for a different production problem with a different time scale. The higher level defines the safety stock levels used to compensate future resource failures. The loading rates are computed by the middle level using a predefined demand target and the stock levels supplied by the upper level. This is accomplished using a method based on fuzzy control concepts. Finally, the lower level controls the flow of parts between the resources, deciding at each time instant which part will be produced by the resources available. These decisions are generated using a modified version of the Yager's fuzzy decision method. Simulation results for two different production systems are presented, showing that the proposed method achieves a good p...