Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Enhancing Enterprise Resilience through Enterprise Collaboration

Current environments, characterised by turbulent changes and unforeseen events, consider resilience as a decisive aspect for enterprises to create advantages over less adaptive competitors. Furthermore, the consideration of establishing collaborative processes among partners of the same network is a key issue to help enterprises to deal with changeable environments. In this paper both concepts, resilience and collaborative processes establishment, are associated in order to help organisations to handle disruptive events. The research objective is to identify collaborative processes whose positive influences assist enterprises against disruptions, reducing the effects of disturbances in dynamic environments.Andres, B.; Poler R. (2013). Enhancing Enterprise Resilience through Enterprise Collaboration. IFAC papers online. 7(1):688-693. doi:10.3182/20130619-3-RU-3018.00283S6886937

Cooperative production networks - multiagent modeling and planning

Consumer goods are mainly produced in multiple steps through a long process. These steps are often done by separate, independent production nodes (enterprises), linked by supply chains. The networks of enterprises — where members have their own objectives and act in an autonomous, rational way to reach their goals — can be naturally modeled by agent-based methodology. The inner structure of each enterprise is similar in the sense that it contains separated planning functions (e.g., production-, inventory-, capacity planning). While the operation inside an enterprise can be controlled centrally, the interaction between the nodes could be synchronized only by negotiation and coordination. Coordination can be based on protocols which regulate information, material and financial flows alike. In this paper we expose an agent-based organizational model of production networks and suggest some planning algorithms which can handle the uncertainty of demand. In addition, we outline the first results of our ongoing research, an analysis of the asymmetric information case and an appropriate coordination mechanism