Multi Agent Systems and the Distributed Constraint Optimization Problem (DCOP) formalism offer several asynchronous and optimal algorithms for solving naturally distributed optimization problems efficiently. There has been good application of this technology in addressing real world problems in areas like Sensor Networks and Meeting Scheduling. Most of these algorithms however exploit static tree structures and are thus not well suited to modeling and solving problems in rapidly changing domains. Also, while in theory most DCOP algorithms can be extended to handle complex local sub-problems, we argue that this generally results in making their performance sub-optimal, and thus their application less suitable. In this paper we present new measures that emphasize the interconnectedness between each agent's local and interagent sub-problems and use these measures to guide dynamic agent ordering during distributed constraint reasoning. The resulting algorithm, DCDCOP, offers a robust, flexible, and efficient mechanism for modeling and solving dynamic complex problems. Experimental evaluation of the algorithm shows that DCDCOP significantly outperforms ADOPT, the gold standard in search-based DCOP algorithms.