Hakki Ozgur Unver

An ontology-based multi-agent virtual enterprise system OMAVE: part 1: domain modelling and rule management

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.

A Multi-agent System Model for Partner Selection Process in Virtual Enterprise☆

Abstract Virtual Enterprise (VE) is a collaboration model between multiple business partners in a value chain. VE information system deals with highly dynamic information from heterogeneous data sources. In order to manage and store dynamic VE information in the database, an ontology based VE model has been developed. To select winner enterprises in VE, a Multi Agent System (MAS) has been developed. Communication and data transition among agents and system entities are based on defined rules in VE ontology model. One of the most important contributions of agents in VE system is in partner selection step of VE formation phase. In this step several agents with different goals and strategies are collaborating and competing each other to win the negotiation procedure or maximize the profit for their assigned enterprise. Different strategies are developed for the agents depending on their appetite for winning the auction against maximizing the profit. Several simulations were run and the results are stored. These results are fed into a neural network in order to predict which enterprise will win the auction and what will be the profit margin. The motivation is to provide a forecasting agent for the customers about the outcomes of the auctions so that they can plan ahead and take the necessary action. Early results indicate such simulated multi-agent VE formations can be used in real systems.

Energy efficiency of machining operations: A review:

Manufacturing processes are among the most energy intensive on earth. As negative ecological and economic impacts increase, reducing energy consumption is becoming critically important. In this article, a comprehensive overview of energy-saving strategies and opportunities for increasing energy efficiency in manufacturing operations is presented, with a focus on metal cutting processes. The issues and approaches involved in energy efficiency of machine tools and machining operations are reported in the literature and a structured research methodology is proposed for this purpose including prediction and modelling of machine energy consumption, determining the relationship between process energy consumption and process variables for material removal processes and optimization of cutting parameters in order to reduce energy consumption. Numerous techniques for increasing energy efficiency in manufacturing processes are identified and summarized, strengths and weaknesses of previous studies are discussed and potential avenues for future research are suggested.

An ontology-based multi-agent virtual enterprise system (OMAVE): part 2: partner selection

A virtual enterprise (VE) is a collaboration model between multiple business partners in a value chain. The VE model is particularly feasible and appropriate for small- and medium-sized enterprises (SMEs) and industrial parks containing multiple SMEs that have different vertical competencies. The VE consortium’s success highly depends on its members. Therefore, it is crucial to select the most appropriate enterprises when forming a VE consortium. In this study, a new multi-agent hybrid partner selection algorithm is developed for application in the development of an ontology-based multi-agent virtual enterprise (OMAVE) system. In this platform, the agent’s interactions are supported by agent ontology, which provides concepts, properties and all message formats for the agents. Different types of agents collaborate and compete with each other so that unqualified or inefficient enterprises are eliminated from the enterprise pool. Only the remaining enterprises would be allowed to enter the negotiation process and propose in the bidding. The agent-based auctioning platform is coupled with a fuzzy–AHP–TOPSIS algorithm to evaluate partners based on their proposals and background. Accordingly, the winning enterprise for each task is identified and the whole project can be accomplished by assigning tasks to the responsible partners. To test and verify the functionality of the developed OMAVE system, a sample module using OMAVE applications and tools was manufactured. The last section of this paper presents the results of this case study, which validate the applicability of the proposed technique.

Evaluation of Partner Companies Based on Fuzzy Inference System for Establishing Virtual Enterprise Consortium

Virtual Enterprise (VE) is one of the growing trends in agile manufacturing concepts. Under this platform companies with different skills and core competences are cooperate with each other in order to accomplish a manufacturing goal. Success of VE, as a consortium, highly depends on the success of its partners. So it is very important to choose the most appropriate companies to enroll in VE. In this study a Fuzzy Inference System (FIS) based approach is developed to evaluate and select the potential enterprises. The evaluation is conducted based on four main criteria; unit price, delivery time, quality and past performance. These criteria are considered as inputs of FIS and specific membership functions are designed for each. By applying fuzzy rules the output of the model, partnership chance, is calculated. In the end, the trustworthy of the model is tested and verified by comparing it with fuzzy-TOPSIS technique providing a sample.

Towards a Decision Support Framework for Sustainable Manufacturing

Every enterprise should track and asses its environmental impact for accountability reasons towards its consumers, employees, government, and society. The objective of this study is to design an eco-tracking framework and a decision support system architecture in order to provide energy visibility to enterprises by close monitoring the energy consumption of their manufacturing processes. The proposed architecture uses the energy consumption and production activity data as input. Energy consumption data is collected from meters and production activity data from manufacturing automation equipment (e.g. PLCs, CNCs, etc.). Collected energy data which is stored in a central data warehouse, is processed, contextualized with activity data and transformed to eco-KPIs in order to benchmark energy efficiency of the production processes and enable factory supervisors to make decisions towards better environmental footprint of the enterprise. The proposed decision support framework is founded on Software as a Service (SaaS) approach which can enable fast visibility of data throughout enterprise and provide easy scalability.

Partner Selection in Formation of Virtual Enterprises using Fuzzy Logic

Virtual Enterprise (VE) is a temporary cooperation among independent enterprises to build up a dynamic collaboration framework for manufacturing. One of the most important steps to construct a successful VE is to select the most qualified partners to take role in the project. This paper is a survey of ranking the volunteer companies with respect to four evaluation criteria, proposed unit price, delivery time, quality and enterprises’ past performance. Fuzzy logic method is proposed to deal with these four conflicting criteria, considered as input variables of the model. As each criterion is different in nature with the other criterion, various membership functions are used to fuzzify the input values. The next step is to construct the logical fuzzy rules combining the inputs to conclude the output. Mamdani’s approach is adopted to evaluate the output in this Fuzzy Inference System. The result of the model is the partnership chance of each partner to participate in VE. A partner with highest partnership chance will be the winner of the negotiation. Implementation of this model to the illustrative example of a partner selection problem in virtual enterprise and comparing it with fuzzy-TOPSIS approach verifies the feasibility of the proposed approach and the computational results are satisfactory.

Design and simulation of a SCADA system using SysML and Simulink

This paper describes a methodology and a case study through which system architecture and dynamic models of related system components are gathered in order to design and simulate the SCADA system of a new hydro turbine test laboratory. System architecture model is prepared in System Modeling Language, a system modeling language based on Unified Modeling Language, while the dynamic model of the laboratory is formed in Matlab/Simulink. Some simulations are performed in order to verify the preliminary system design studies and system requirements.