A Market-Based Approach to Multi-factory Scheduling

Vytelingum, Perukrishnen; Rogers, Alex; Macbeth, Douglas K.; Dutta, P.S.; Stranjak, Armin; Jennings, Nicholas R.

doi:10.1007/978-3-642-03821-1_11

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…We showed that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach. Second, we consider work on developing strategies for auctions that allocate aircraft engines that require maintenance to repair and overhaul bases (Vytelingum et al, 2009). In this context, the structure of the electronic institution is fixed.…”

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confidence: 99%

Computational Service Economies: Design and Applications

Jennings

Rogers

2009

Studies in Computational Intelligence

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Many modern computing systems have to operate in environments that are highly interconnected, highly unpredictable, in a constant state of flux, have no centralized point of control, and have constituent components owned by a variety of stakeholders that each have their own aims and objectives. Relevant exemplars include the Web, Grid Computing, Peer-to-Peer systems, Sensor Networks, Cloud Computing, Pervasive Computing and many eCommerce applications. Now, although these areas have somewhat different areas of emphasis, we believe they can all be viewed as operating under the same conceptual model. Namely, one in which: (i) entities offer a variety of services in some form of institutional setting; (ii) other entities connect to these services (covering issues such as service discovery, service composition and service procurement); and (iii) entities enact services, subject to service agreements, in a flexible and context sensitive manner. Moreover, we believe agent-based computing, with its emphasis on autonomous, flexible action and interaction, is an appropriate computational model for conceptualizing, designing and implementing such systems (Jennings, 2000;Jennings 2001). In particular, such agents are a natural way of viewing flexible service providers and consumers that need both to respond to environmental change, while being able to work towards their individual and collective aims. Moreover, the interactions between these agents need to take place in some form of electronic institution that structures the interactions and can provide an effective matching of the appropriate producers and consumers. Everyday examples of such institutions might include: eBay, Second Life, Betfair exchanges or World of Warcraft. Nevertheless, in the work described here, we will focus specifically on trading institutions and in particular computational service economies that mediate the exchanges between software agents in the area of software services.In more detail, when thinking about the design of such computational service economies there are two major perspectives that need to be considered (Dash et al., 2003). First, there is the design of the institution itself; this can essentially be thought of as the rules of the game. This covers issues such as who are the valid participants (e.g. buyers, sellers, intermediaries), what are the interaction states (e.g. accepting bids, negotiation closed), what are the events causing state transitions (e.g. bid, time out, proposal accepted), what are the valid actions of the participants (e.g. propose, accept, reject, counter-propose) and what are the reward structures (e.g. who pays and who gets paid for what). Second, there is the design of the strategies for the agents that participate in the institution; this can essentially be thought of as how to succeed in the game. Such strategies are very much determined by the institutional rules and are essentially the decision making employed to

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confidence: 99%

Computational Service Economies: Design and Applications

Jennings

Rogers

2009

Studies in Computational Intelligence

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“…Similar inter-firm cases can also be found in the airport ground handling service scheduling (Mao et al, 2009) and supply chain scheduling (Lau et al, 2006). In a distributed environment, the approach that respects the privacy of self-interested decision makers is preferable as stated in (Lau et al, 2006;Stranjak et al, 2008;Vytelingum et al, 2009), because decision makers may be potential competitors in the same marketplace.…”

Section: Distributed Multi-project Schedulingmentioning

confidence: 99%

“…start time, duration, resource requirements) is inevitably required by the mediator. Note that in a competitive environment, the project agent may still be unwilling to reveal its valuable sensitive information, even if the mediator is an automated agent (Vytelingum et al, 2009). Secondly, when the individual objective of each PA cannot be decomposed precisely to each activity (e.g.…”

Section: Other Approachesmentioning

confidence: 99%