Distributed Constraint Optimization and Scheduling in Dynamic Environments

Abstract

Effcient scheduling of resources in a complex dynamic environment poses a significant research challenge. The problem is compounded in the case of a distributed real world environment where several departments are working at optimizing their own resources. Multiple departmental schedules thus need to be optimized simultaneously to ensure proper utilization of resources. Much of the current eorts at solving scheduling problems with these characteristics fail to model the inherent complexity and dynamism, leaving the task of making the actual scheduling decisions to the user. The work described in this thesis aims to provide a multi-agent framework, driven by a distributed constraint optimization strategy, to model and eciently solve this class of problems. The foundation case studied in this thesis is the elective surgery scheduling problem, as it is an intrinsically distributed and complex real world problem. Ecient scheduling of elective surgery is of critical importance to the optimum utilization of the public health system, a topical issue with an aging population. A summary of the main contributions of this study is as follows: We present an intelligent agent based approach for solving complex distributed scheduling problems in dynamic environments. Motivated by the challenge of solving the real world problem of scheduling elective surgery in an underresourced and encumbered health system, we propose a methodology which can be used to build intelligent software agents that generate optimal schedules on behalf of their respective departments. In our solution, each agent, trained with the appropriate constraints, preferences and priorities, optimizes schedules for their respective department and then negotiates to resolve interagent constraints. The architecture of each agent incorporates an interface module to handle internal and external communication, an intelligence modi ule to perform decision making and learning, and a Distributed Constraint Optimization Problem (DCOP) engine to drive the optimization. [Part of this work was published at the MedInfo 2007 World Congress (Khanna et al., 2007b)]. We propose an algorithm, the Dynamic Complex Distributed Constraint Optimization Problem (DCDCOP) algorithm, for solving Dynamic Complex DCOP problems. DCDCOP does not rely on the static organization of agents into depth first search trees for problem solving, and is capable of handling complex sub-problems, found in many real world DCOP problems, without using decomposition or compilation. Further, it allows each agent the exibility to choose its own local solver. We also introduce a novel metric called Degree of Unsatisfaction (DU) and use it for guiding inter-agent negotiation for problem solving in the DCDCOP algorithm. Empirical evaluation of the DCDCOP algorithm and the DU metric is reported to establish their suitability for the class of problem we seek to address. [Parts of this work were published at the AAMAS 2009 conference (Khanna et al., 2009c) and the WI/IAT 2009 conference (Khanna et al., 2009c)]. We introduce ASES, an Automated Scheduler for Elective Surgery. Developed as a proof-of-concept prototype application, ASES represents the novel marriage of rational agency and distributed constraint optimization necessitated by the problem domain. It aptly demonstrates the suitability of using our proposed methodology for providing ongoing schedule optimization and can also be used to study the eect of uctuation in stang or resource levels on theatre utilization. [Part of this work was published at the PRIMA 2010 Conference (Khanna et al., 2010)]. ii