Fairs for e-commerce: the benefits of aggregating buyers and sellers

In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allow to study effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic

A Framework to Support Coalition Formation in Supply Chain Collaboration

This paper proposes a framework for agents in globally collaborative supply chain by applying the concept of coalition formation, a cooperation game in game theory. This framework provides mutual benefits to every party involved buyers, sellers and logistics providers. It provides a common gateway that allows individual parties to locate the right partners, negotiate with them, and form coalition in the best possible ways. The framework is applicable to real world e-business models, including B2C, B2B, supply chain and logistics, SME, etc. We firstly discuss common needs existing in today e-business. We then discuss about our framework, i.e., negotiation protocol and decision mechanism

Agent-based coalitions in dynamic supply chains

Coalition formation is an important issue in multi-agent systems. Recent work in the area has focused on reducing the complexity of forming coalitions, i.e., each agent deliberately searches for potential coalition members before negotiating with them. We propose a framework for coalition formation of agents in a dynamic supply chain environment. The framework is composed of a negotiation protocol and a decision mechanism. The negotiation protocol allows thorough communication among agents across sectors (buyers, sellers, logistic providers). With the decision mechanism, agents take two steps to form coalitions: i) agents in each sector form loosely-coupled coalitions in order to decrease the complexity of the negotiation, and ii) agents form coalitions across sectors in order to deliver goods to end customers. We provide an example of how they can help agents form coalitions successfully

Heuristic methods for coalition structure generation

The Coalition Structure Generation (CSG) problem requires finding an optimal partition of a set of n agents. An optimal partition means one that maximizes global welfare. Computing an optimal coalition structure is computationally hard especially when there are externalities, i.e., when the worth of a coalition is dependent on the organisation of agents outside the coalition. A number of algorithms were previously proposed to solve the CSG problem but most of these methods were designed for systems without externalities. Very little attention has been paid to finding optimal coalition structures in the presence of externalities, although externalities are a key feature of many real world multiagent systems. Moreover, the existing methods, being non-heuristic, have exponential time complexity which means that they are infeasible for any but systems comprised of a small number of agents. The aim of this research is to develop effective heuristic methods for finding optimal coalition structures in systems with externalities, where time taken to find a solution is more important than the quality of the solution. To this end, four different heuristics methods namely tabu search, simulated annealing, ant colony search and particle swarm optimisation are explored. In particular, neighbourhood operators were devised for the effective exploration of the search space and a compact representation method was formulated for storing details about the multiagent system. Using these, the heuristic methods were devised and their performance was evaluated extensively for a wide range of input data