The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

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Agent-based systems for manufacturing

Applications of agent-based systems in intelligent manufacturing: An updated review ☆

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM.

/pdf/the-application-of-discrete-event-simulation-and-system-7ofyjobvc3.pdf

The application of discrete event simulation and system dynamics in the logistics and supply chain context

We consider a manufacturer who uses a reverse, or procurement, auction to determine which supplier will be awarded a contract. Each bid consists of a price and a set of nonprice attributes (e.g., quality, lead time). The manufacturer is assumed to know the parametric form of the suppliers' cost functions (in terms of the nonprice attributes), but has no prior information on the parameter values. We construct a multiround open-ascending auction mechanism, where the manufacturer announces a slightly different scoring rule (i.e., a function that ranks the bids in terms of the price and nonprice attributes) in each round. Via inverse optimization, the manufacturer uses the bids from the first several rounds to learn the suppliers' cost functions, and then in the final round chooses a scoring rule that attempts to maximize his own utility. Under the assumption that suppliers submit their myopic best-response bids in the last round, and do not distort their bids in the earlier rounds (i.e., they choose their minimum-cost bid to achieve any given score), our mechanism, indeed, maximizes the manufacturer's utility within the open-ascending format. We also discuss several enhancements that improve the robustness of our mechanism with respect to the model's informational and behavioral assumptions.

An Inverse-Optimization-Based Auction Mechanism to Support a Multi-Attribute Rfq Process

Addressing sustainability in research on distributed production: an integrated literature review

https://isiarticles.com/bundles/Article/pre/pdf/26801.pdf

Production planning in reconfigurable enterprises and reconfigurable production systems

Designing multi-attribute auctions for engineering services procurement in new product development in the automotive context

Distributed production networks are considered organizational structures able to match agility and efficiency necessary to compete in the global market. Performances of such organization structures heavily depend on the ability of the network actors of coordinating their activities. The research proposes to model and design coordination problems within production network by using the Multiple Agent Technology. In particular, the paper proposes new strategies for coordinating production-planning activities within production networks. Such models have been developed and tested by using a proper simulation environment developed by using open source code and architecture. The results of the research can be located at two levels: (a) concerning the specific coordination problem addressed, the research provides some insights to make decisions about the choice of coordination approaches to be used in distributed production planning problems; (b) at more strategic level, the paper shows how Agent Technology and discrete event simulation can be used to build up efficient coordination structures for production networks.

G. Lo Nigro

Papers

Production planning in reconfigurable enterprises and reconfigurable production systems

Designing multi-attribute auctions for engineering services procurement in new product development in the automotive context

Coordination policies to support decision making in distributed production planning

High Level Planning of Reconfigurable Enterprises: a Game Theoretic Approach

An Agent Based Architecture for production planning and negotiation in catalogue based e-marketplace