A multi-agent knowledge model for SMEs mechatronic supply chains

The main concern of this research work is to analyse and model supply chains (SCs) in the particular context of small and medium enterprises (SMEs) in the field of mechatronic. The study is based on the analysis of the organisational features, the actors?? behaviour, and performance considerations. The development of the model relies on an iterative framework that progressively integrates different aspects into the model. This framework is the ArchMDE process, which is based on MDE (Model Driven Engineering). A major feature of this work lies in its contribution to two different areas of research. The first contribution of the work is to propose a generic metamodel for SCs. Based on a literature review, an incremental framework is proposed for the modelling of SCs in terms of concepts, structure and relationships. The application of the framework to the studied context is described and its result is a domain-metamodel for SCs. The second contribution of this work lies in the formalisation of the dynamic behaviour of the concepts in the metamodel. This formalisation is based on the multi-agent approach. An agentification of the metamodel is thus drawn, thanks to the natural links between multiagent theory and SC reality. This step leads to an agentified-domain-metamodel which also includes the monitoring of the SC and synchronisation protocols. By adding relationships and dynamic behavior aspects, we obtain a metamodel of the domain that can be implemented, with its static and dynamic aspects. To validate this model, an industrial case study is detailed and has been instantiated and encoded in JAVA.     

Software agents for knowledge management: coordination in multi-agent supply chains and auctions

A fundamental question that must be addressed in software agents for knowledge management is coordination in multi-agent systems. The coordination problem is ubiquitous in knowledge management, such as in manufacturing, supply chains, negotiation, and agent-mediated auctions. This paper summarizes several multi-agent systems for knowledge management that have been developed recently by the author and his collaborators to highlight new research directions for multi-agent knowledge management systems. In particular, the paper focuses on three areas of research:

• •Coordination mechanisms in agent-based supply chains. How do we design mechanisms for coordination, information and knowledge sharing in supply chains with self-interested agents? What would be a good coordination mechanism when we have a non-linear structure of the supply chain, such as a pyramid structure? What are the desirable properties for the optimal structure of efficient supply chains in terms of information and knowledge sharing? Will DNA computing be a viable tool for the analysis of agent-based supply chains?
• •Coordination mechanisms in agent-mediated auctions. How do we induce cooperation and coordination among various self-interested agents in agent-mediated auctions? What are the fundamental principles to promote agent cooperation behavior? How do we train agents to learn to cooperate rather than program agents to cooperate? What are the principles of trust building in agent systems?
• •Multi-agent enterprise knowledge management, performance impact and human aspects. Will people use agent-based systems? If so, how do we coordinate agent-based systems with human beings? What would be the impact of agent systems in knowledge management in an information economy?

     

A multi-agent architecture for outsourcing SMEs manufacturing supply chain

In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study.     

Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains

This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.     

Optimal newsvendor policies for dual-sourcing supply chains: A disruption risk management framework

We propose generic single period (newsvendor-type) inventory models for capturing the trade-off between inventory policies and disruption risks in a dual-sourcing supply chain network both unconstrained and under service level constraints, where both supply channels are susceptible to disruption risks. The models are developed for both risk neutral and risk-averse decision-makers and can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Analytical closed-form solutions are obtained and interesting managerial insights on the merit of contingency strategies in managing uncertainties and risks in dual-sourcing supply chains are discussed.     

A multi-agent system model for supply chains with lateral preventive transshipments: Application in a multi-national automotive supply chain

In this study, a multi-agent system model is developed to observe the effects of ordering parameters on a supply chain with lateral preventive transshipments. The proposed model involves ordering and premium freight bidding processes of the agents. The model is implemented to a multi-national supply chain considering both supply and demand side uncertainties. Effects of safety stock and supplier flexibility levels on performance are examined by simulation from both agent and system-wide perspective. The results reveal the viability of the proposed model.     

A multi-agent based approach for change management in manufacturing enterprises

Change management becomes an unavoidable necessity for manufacturing enterprises. Since change in business processes carries significant impact on the performance of manufacturing companies, a change management model is definitely required to remain competitive. Moreover, utilizing agent based systems will provide computational provision and integrity to manage and measure the capabilities to follow the change in a progressive approach by employing the cooperation and collaboration properties of various agents helping for retrieval of the required information in a rapid way. Therefore, in this paper, a multi-agent based change management model is proposed to handle the changes in manufacturing enterprises. The model is validated through a case study done to measure the performance of change management capabilities in a manufacturing company. A sensitivity analysis on the results of this case study is also conducted to reveal the system reactivity to various parameters.     

Co-Insights framework for collaborative decision support and tacit knowledge transfer

Abundant software tools use visual analytics (VA) techniques to support various decisions with the aim of boosting better insights. Large organizations, however, lose efficiency in selecting the right tools to support the persons who apply the tools to various decision tasks. Consequently, the creation and sharing of insights are far from optimal, leading consistently to sub-optimal decisions. In this work, the Co-Insights framework is introduced with automated collaboration support features to enable effective creation and sharing of distributed insights. A collaboration network (Co-Net) is established to model the collaborative decision making process in an organization. Two important features of the Co-Insights framework are developed: collaborative agent allocation analysis (CA3) for task–participant matching; and a robust mechanism for the recommendation of selected VA tools, by participant–tool matching. Thus, by better matching of tasks and tools with participants, the creation and sharing of insights are improved in any collaborative team for better decision making, accompanied with the tacit knowledge transfer to sustain the entire organization. To validate the effectiveness of these two main features, two experiments built on the Co-Net model are performed to test the newly developed algorithms. It has been found that CA3 significantly improves the matching scores by up to 35%, compared with conventional task–participant matching methods. The neural network based participant–tool matching mechanism yields robust results with 4% mismatches for 10% noise levels, and with 16% mismatches for 30% noise levels. Real case applications and implications are described, and further plans to extend this new framework are also outlined based on the reported experiments and evaluations.     

A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains

The coordination of the planning operations across the manufacturing supply chains (MSC) is considered as a major component of supply chain management. As centralized coordination requires relevant information sharing, alternative approaches are needed to synchronize production plans between partners of MSC characterized by decentralized decision making systems with limited information sharing. In this paper, a bi-level fuzzy-based negotiation approach is proposed in order to model collaborative planning between MSC partners. During negotiation, each manufacturer is optimizing a bi-objective planning model. In order to generate optimal production plans, a genetic algorithm is used. To evaluate the exchanged proposals and the satisfaction degree of each partner, the fuzzy logic approach is adopted in the both negotiation levels. The main result of the developed approach consists in a collaborative decision making mechanism allowing the MSC partners to define their optimal production plans while considering the whole negotiating process with the pre-negotiation and post-negotiation stages. Computational tests done for different MSC structures show the effectiveness of the proposed mechanism, which ensures the satisfaction of the manufacturers and the optimality of the final solution. By comparing the results with the ones obtained considering a centralized planning approach, it is shown that the developed negotiation mechanism yields to near optimal solutions with insignificant gaps from the global optimal solutions.     

An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing

With the development of a new generation of information technology, smart manufacturing has put forward higher requirements for supply chain. It is necessary to ensure the synchronization of the supply chain operation and maintain the reliability of the supply chain management, therefore the trust evaluation for the supply chain becomes extremely important. Traditional supply chain management has problems such as information flow is easy to be tampered with, logistics is difficult to trace, and capital flow is not true, which leads to increased opportunity costs due to the lack of trust among transaction entities in the supply chain. The emergence of blockchain technology provides an opportunity to improve the supply chain ecosystem. In this paper, an integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing is proposed to explain how to enhance trust management with the help of blockchain from the perspectives of information flow, logistics, and capital flow. An optimized trust management model is designed for better entities evaluation in supply chain. A coal mine equipment manufacturing industry scenario is presented to illustrate the effectiveness of the proposed framework.     

Route planning model of multi-agent system for a supply chain management

Sometimes in travel planning, finding the best route to the road transportation network by considering the environmental conditions that are affecting the actual time travel of the travellers are vital especially in handling the logistic operations in supply chain management (SCM). Furthermore, the policy strategy is needed in order to influence the managers or drivers to find the optimum and the most effective route for a trip plan in supporting the logistic operations of SCM. In this paper we analyze the effectiveness of the coordination model of the environmental conditions that are affecting for the travelling time based on multi-agent system for a road transportation network for supply chain management. A number of experimental cases have been used to evaluate the proposed approach transportation network problems in some Malaysian cities. Finally, experimental results affirmed that the proposed approach is practical and efficient.     

A service-oriented framework for agent-based simulations of collaborative supply chains

Current collaborative practices of supply chain management are limited to some known configurations where a dominant member sets the pace for the collaboration extent. Extending collaborative models to supply chains without a dominant member requires defining a fair assessment of costs and benefits and how they are distributed among members. To understand collaborative models and their mechanisms, simulation-based approaches are recommended as they can afford the complexity of real scenarios. However, building ad-hoc simulation models for studying complex supply chain interactions can be prohibitive in terms of both cost and time. Therefore, the availability of simulation frameworks, to be used easily by business managers and facilitating the development of those models, has a strong incentive in the quest of current business efforts to increase their supply chain performance. The objective of this work is to present a systematic and reusable serviceoriented framework for agent based simulation to support the analysis of collaborative interactions in supply chains. Results of a requirement analysis performed to this aim are described, and the fulfillment of identified requirements by the proposed framework, and capabilities thereof, are discussed.     

A systems thinking framework for knowledge management

Myriad frameworks have been developed for knowledge management. However, the field has been slow in formulating a generally accepted, comprehensive framework for knowledge management. This paper reviews the existing knowledge management frameworks and provides suggestions for what a general framework should include. The distinguishing feature of this research is that it emphasizes placing knowledge management in a larger context of systems thinking so that the influencing factors on its success or failure can better be recognized and understood.     

A collaborative methodology for tacit knowledge management: Application to scientific research

Tacit knowledge, which refers to the know-how, is critical to understand and reuse since it is located in the human heads. It represents the foremost element for human and team evaluation. Seeking for tacit knowledge is achieved only by communicating with the concerned persons, which makes losing it axiomatic if people leave their work without documenting their know-how. Thus, providing a collaborative environment based on a common conceptualization of the domain to formalize the experts’ knowledge and to share their outcomes is required. However, some barriers pertaining to cultural and social factors such as personality traits impede capturing the conceptual model. To cope with these issues, we have proposed a generic two-step methodology that copes with human barriers when capturing the domain experts’ tacit knowledge, their skills, and seeds terms in order to converge to a common knowledge representation. Considering the scientific research management as a use case, we followed the proposed methodology to formalize our scientific research knowledge in the context of network and communication research field. Based on the generated ontology, we have developed a semantic web platform that allows collaboratively annotating experts’ knowledge in a computer interpretable format that can be shared and reused by human and machines. Our evaluation is based on end users’ quality of experience and feedbacks.     

A hierarchical framework for visualising and simulating supply chains in virtual environments

This paper presents research into applying virtual environment (VE) technology to supply chain management (SCM). Our research work has employed virtual manufacturing environments to represent supply chain nodes to simulate processes and activities in supply chain management. This will enable those who are involved in these processes and activities to gain an intuitive understanding of them, so as to design robust supply chains and make correct decisions at the right time. A framework system and its hierarchical structure for visualising and simulating supply chains in virtual environments are reported and detailed in this paper.     

A framework of knowledge versioning management

Abstract: Knowledge is an inherently dynamic entity continuously changing and evolving. In many cases, the coexistence of different versions of the same core knowledge is a necessity. So is the availability of the proper environment and tools to deal with knowledge versioning. In this paper, a framework of knowledge versioning management is proposed and implemented dealing with hybrid knowledge representation models using frames and rules. This framework facilitates knowledge version handling and maintenance, improving, in parallel, knowledge sharing and reuse. Knowledge components are stored in a set of tables and handled as data under the auspices of a database management system. The proper structure of tables and their relationships allows the creation of independent knowledge modules. Several knowledge modules can be assembled to construct higher level modules, which finally form versions of knowledge. Corresponding knowledge base versions consist of several knowledge modules easy to handle and process in various application areas. The proposed framework has been implemented and thoroughly examined in an application area of great importance, such as pest management.     

A multi-agent based system for e-procurement exception management

E-procurement has become an important function of enterprise information systems. The process of e-procurement includes the automatic definition of product requirements, search and selection for suppliers, negotiation and contracting with suppliers. However, the adoption of e-procurement encounters various uncertainties from internal and external environments, such as inventory failure, sharp increased demand, and delivery delay. In this paper, we propose a novel agent-based architecture for an e-procurement system in which agent technology is applied to deal with the internal and external uncertainties. Through the collaboration and interaction between different agents, the architecture that we propose can enhance the flexibility to handle unexpected exceptions, thus leading to agile procurement management. To valid the feasibility of our approach, a case study has been conducted to investigate how our agents collaborate to manage the inventory failure exception which occurs in restaurant e-procurement.     

A new framework of consensus protocol design for complex multi-agent systems

This paper aims to find a simple but efficient method for consensus protocol design. This paper presents two consensus protocols to solve the consensus problem of complex multi-agent systems that consist of inhomogeneous subsystems. The limitations of current studies are analyzed, and a novel model based on transfer functions is presented. This model can be used to describe both homogeneous and inhomogeneous multi-agent systems in a unified framework. Based on this model, two sufficient and necessary conditions for the consensus of complex multi-agent systems have been obtained. One is for the systems without any external input, and the other is for the systems with the same external input. Then, two corresponding distributed consensus protocols are presented. Considering that the complex multi-agent systems may require different outputs sometimes, the relationship between inputs and outputs is analyzed. Finally, some simulations are given to demonstrate the performance and effectiveness of the proposed approaches.