Optimization and Control

Abstract

Abstract. Considering the foundations, tools, and emerging discoveries of collaborative e-Work, as discussed in Chapters 1 and 2, it is realized that optimization and control are focused primarily on the core elements of e-Systems; agents, protocols, and workflows. In this chapter, we will show that these elements compose a solid framework for optimization and control of collaboration in emerging distributed e-Work systems. In order to be able to efficiently pass the benefits on to more complex constructs such as autonomous agents systems, production units configuration, highly reactive control protocols, and so on, these elements must be optimized as well. In order to show the evidence of the latest developments in optimization and control involving agent, protocol, and workflow theories, this chapter reviews the state-of-the-art techniques for achieving optimal design and operational control, and collaboration engineering. This chapter covers the incentives to construct autonomous agent-based systems, the key e-Criteria emerging from the transformation from traditional centralized work systems to decentralized e-Work systems, and several real-life applications of agent-based systems. Basic agent-based optimization and control architectures are reviewed along with pioneering bioinspired mechanisms based on swarm intelligence and natural evolution, and their impact on the intelligence and autonomy of agents. Several techniques for protocol and workflow optimization are also discussed. Emerging Impacts of AgentsWith the transformation from traditional monolithic systems to decentralized e-Work networks, optimization and control of e-Systems are facing new challenges. The emerging requirements for timely response to the growing complexity and dynamicity of e-Systems call for higher intelligence and autonomy of agents for smarter interaction and collaboration (Rosenschein and Ephrati, 1993). Classic monolithic and hierarchical systems are no longer sufficient for addressing such requirements, and often lead to situations where the entire system collapses by a single error or conflict at one point of the system hierarchy 116 4 Optimization and Control (Colombo et al., 2006). Various e-Criteria have been defined to formalize the emerging needs of e-Systems at different levels, and with different contextual relationships (Figure 4.1): adaptability, flexibility, reconfigurability, changeability, robustness, and scalability (Putnik et al., 2013). The prerequisites for addressing these requirements, however, mean to enable and delegate intelligence and autonomy to the lowest-level individual units collaborating within the e-System, namely the agents. Fig. 4.1 (a) Flexibility is interpreted as the ability of a system to change its behavior without changing its configuration, and reconfigurability is interpreted as the ability to change the behavior of a system by changing its configuration (both implying a changeable system). Scalability implies changing configuration by adding/removing the configuration elements, which is...