Research on multi-agent system based trust model of partner selection of virtual enterprise

Partner selection is one of the core problems in the phase of virtual enterprise (VE) creation, since the selection of right partners is crucial to the success of VE. To effectively solve the partner selection and optimization problem in VE practice, a mathematical model with the objective of minimizing the total manufacturing cost of tasks within the due date is discussed in this paper. As the objective formulation is not continuous and differential, it cannot be solved exactly by integer programming, an improved ant colony optimization algorithm (IACO) is proposed to solve the problem. A crossover operator which is usually used in genetic algorithm (GA) is introduced into IACO, so it can improve the search ability of ant colony and make consequently solution better. Finally, an illustrative example is presented to show the efficiency of the algorithm.

The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: 1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. 2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems.

Partner selection is one of the most critical issue in virtual enterprise, which has been extensively researched in recent years. It is difficult to be solved by the traditional optimization methods for partner selection is a combinatorial optimization problem. In this paper, an improved quantum swarm evolutionary algorithm based on time-varying acceleration coefficients (IQSEA_TVAC) is proposed for partner selection optimization problem. The results of simulation experiments show that the improved algorithm is valid and outperforms the conventional quantum evolutionary algorithm.

Selection of right partners is vital for virtual enterprise (VE) to take the advantage of market opportunity. Partner selection problem belongs to combinatorial optimisation category and known as NP-hard. A modified discrete particle swarm optimisation (PSO) algorithm is proposed, in this work, to select alliances in a VE. The velocity terms, which is commonly used in the standard PSO algorithm, is avoided; rather, the concept of proportional likelihood with modifications, a method used in data mining techniques, is adopted to improve solution quality. A set of experiments have been conducted and results indicate PSO algorithm is an effective and efficient way to solve the partner selection problems with precedence, risk of failure and due date constraints. A design of experiment (DoE) approach is adopted to study statistical significance of various model parameters on solution quality.

Recent advances in networking technology have increased the potential collaborations for virtual enterprises (VEs) on a global scale. Due to the dynamic nature of collaborations, building and evolving trust is essential to support the formation of VEs. There is a critical need for the new approach suited to this environment. This paper presents a trust-based approach for partner selection problem in the VEs. The proposed model explores the dynamic properties of trustworthy index, which provides a multiperspective and interactive overview of potential partners to the decision-makers. The model uses the hybrid learning algorithm of fuzzy cognitive map (FCM). As FCM has the excellent ability in modeling complex systems, the proposed model can support historical data mining automatically, and revise the existing model according to the new requirements. Results of three experiments show that this model provides reasonable performance and high adaptability for diverse partner selection problems.

The partner selection problem (PSP) in virtual enterprise has been comprehensively investigated from the aspects of research fields, contents, attributes or criteria been considered, and algorithms. With the consideration of environmental protection, the importance of “green criteria” in PSP is introduced, and two new green criteria, i.e., carbon emission and lead content in manufacturing production, are firstly brought into PSP. A formulation of PSP with green criteria is established which includes four objectives and six constraints. A new improved algorithm, named Pareto genetic algorithm for PSP (Pareto-PSGA), is designed for addressing the specific PSP. With Pareto solution ideas, vector encoding, random selection, two-point crossover, and single-point mutation for Pareto solutions are designed in the Pareto-PSGA. Experimental results demonstrate that compared with other typical intelligent algorithms such as simulated annealing and particle swarm optimization, Pareto-PSGA shows high performance in solving the specific PSP with more suitable Pareto solutions in shorter time.

Within the scope of the collaborative research centre Non-hierarchical regional production at Chemnitz University of Technology, a virtual enterprise model is developed. This is based on very small performance units - the competence cells (CCs). The precondition of the efficient and thereby competitive operation of those networks is a network controlling for objectively selecting the best suitable CCs for every order. This problem gains complexity because the manufacture of a product can be carried through in different ways.

The virtual enterprise organizes the enterprises that are geographically and organizationally. Through the effective cooperation among enterprises, the enterprises will fulfill the goal as “win-win”. The present study are mainly focused on the virtual enterprise partner selection in the field of virtual enterprise cooperation, and the research of virtual enterprises' cooperative mode is so few. A new cooperation model of virtual enterprise based on agent theory and multi-phase cooperation process are presented. It analyses the optimal incentive mechanism in virtual enterprise.

When it comes to modeling social life, few paradigms are more suitable than multi-agent systems (MASs). MASs have helped gain a deeper understanding of countless social phenomena (through agent-based social simulation) that were hard to study using analytical approaches. More recently, MASs have evolved into handy assistants to the human users they can act on behalf of. One example of such use of MASs is in implementing virtual enterprises (VEs), i.e. temporary alliances of geographically distributed organizations created in order to exploit a specific business opportunity. Competition, negociation and cooperation mechanisms are usually copresent in the creation and operation of VEs, with individual members being simultaneously autonomous self-interested entities and parts of a larger whole, the benefits of which they are also trying to maximize. In turn, VEs have application areas such as commerce, manufacturing, and tourism, offering important contributions to the universal contemporary strife to obtain customer-driven products while optimizing time, costs, and quality. In this chapter, we aim to review the most important applications of MASs in social sciences, with a focus on VEs as a special case.

It is a typical multi-objective optimization problem for the scientific decision of bidding to seek cooperating partner in virtual enterprise. With the optimization model proposed, partner selection is solved by the improved genetic algorithm. In the evolution process, individual survive rate is dynamic according to queue of individuals 'fitness values before roulette wheel selection, avoiding premature convergence. Crossover and mutation operators are accordingly adaptive to fitness value and iterative degree, which endows individuals with self- adaptability with the variation of the environment. Finally, the example demonstrates the validity of the adaptive genetic algorithm.

With the rapid development of the Internet, information technology, and globalization of the economy, Some small and medium-sized companies know that they cannot compete with their limited capacity alone. As a result, they are beginning to seek collaboration and a collective approach to meet the dynamic needs of customers and increase their power for competition in the market. Virtual enterprise is a temporary platform for working with different companies that share their core tasks to meet customer’s demand. Partner selection is a major issue in the formation of a virtual organization. This is especially difficult due to the uncertainties regarding information, market dynamics, customer expectations, and rapidly changing technology, with highly random decision making. As a generalization of fuzzy sets and intuitionistic fuzzy sets, Neutrosophic sets are created to show the uncertain, and inconsistent information available in the real world. The main purpose of this paper is to identify and select partners in the formation of Virtual Enterprises under uncertainty and contradictory factors using the extended VIKOR group decision making technique using the Interval Neutrosophic fuzzy approach. For this purpose, after identifying the factors affecting partner selection, the factors are weighted using the Maximizing deviation method and the partners are ranked using this method. Finally, a sensitivity analysis for assessing the validity of the method is also presented. The results show that the Willingness to share information criterion is the most important partner selection criterion in this enterprise.

Nowadays, virtual enterprise (VE) is an important paradigm of business management in an agile environment. VE exists in several kinds of business organization through multi-layered product flows. It is obvious that a mechanism, through which these different enterprises can be integrated together, is required for the better management of VE. In this paper, we focus on the negotiation process in VE formulation as a basic research to clarify its effective management. Each enterprise in VE is defined as a software agent with multi-utilities, and three types of primitive business models are targeted, such as the vertically integrated business model, horizontally specialized business model and hybrid business model. We develop a three-layered VE model for computer simulation so as to clarify VE formulation dynamism with the proposed negotiation mechanism.

Multiagent systems (MASs) are societies whose individuals are software delegatees (agents) acting on behalf of their owners or delegators (people or organizations). When deployed in an open network such as the Internet, MASs face some trust and security issues. Agents comeand go, and interact with strangers. Assumptions about security and general trustworthiness of agents and their deployers are inadequate in this context. In this paper, the design of a security infrastructure is presented applicable to MASs in general. This design addresses both security threats and trust issues. In this design, there are mechanisms for ensuring secure communication among agents and secure naming and resource location services. And two types of trusts are addressed: trust that agents will not misbehave and trust that agents are really delegatees of whom they claim to be. To establish the first type of trust, deployers of agents are made liable for the actions of their agents; to establish the second type of trust, it is proposed that agents prove that they know secrets that only their delegators know.

Selection of partners is one of the critical problems of Virtual Enterprise. In the paper, according to the deficiency in the existed quantitative methods, a partners-selection of optimization mathematic model is proposed, taking the advantages of simulated annealing algorithm and genetic algorithm, a hybrid genetic algorithm is used to slove the problem of partners selection in Virtual Enterprise. Finally, a typical simulation study is carried out to illustrate the algorithm's validity.

New advancements in computers and information technologies have yielded novel ideas to create more effective virtual collaboration platforms for multiple enterprises. Virtual enterprise (VE) is a collaboration model between multiple independent business partners in a value chain and is particularly suited to small and medium-sized enterprises (SMEs). The most challenging problem in implementing VE systems is ineffcient and inflexible data storage and management techniques for VE systems. In this research, an ontology-based multi-agent virtual enterprise (OMAVE) system is proposed to help SMEs shift from the classical trend of manufacturing part pieces to producing high-value-added, high-tech, innovative products. OMAVE targets improvement in the flexibility of VE business processes in order to enhance integration with available enterprise resource planning (ERP) systems. The architecture of OMAVE supports the requisite flexibility and enhances the reusability of the data and knowledge created in a VE system. In this article, a detailed description of system features along with the rule-based reasoning and decision support capabilities of OMAVE system are presented. To test and verify the functionality and operation of this system, a sample product was manufactured using OMAVE applications and tools with the contribution of three SMEs.