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Agent-Based Simulation and Modeling for Service Allocation in Digital Manufacturing Market

Agent-Based Simulation and Modeling for Service Allocation in Digital Manufacturing Market

Maedeh Dabbaghianamiri, Farhad Ameri, Jesus Jimenez
Copyright: © 2015 |Volume: 7 |Issue: 2 |Pages: 18
ISSN: 1943-0744|EISSN: 1943-0752|EISBN13: 9781466676824|DOI: 10.4018/IJATS.2015040101
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MLA

Dabbaghianamiri, Maedeh, et al. "Agent-Based Simulation and Modeling for Service Allocation in Digital Manufacturing Market." IJATS vol.7, no.2 2015: pp.1-18. http://doi.org/10.4018/IJATS.2015040101

APA

Dabbaghianamiri, M., Ameri, F., & Jimenez, J. (2015). Agent-Based Simulation and Modeling for Service Allocation in Digital Manufacturing Market. International Journal of Agent Technologies and Systems (IJATS), 7(2), 1-18. http://doi.org/10.4018/IJATS.2015040101

Chicago

Dabbaghianamiri, Maedeh, Farhad Ameri, and Jesus Jimenez. "Agent-Based Simulation and Modeling for Service Allocation in Digital Manufacturing Market," International Journal of Agent Technologies and Systems (IJATS) 7, no.2: 1-18. http://doi.org/10.4018/IJATS.2015040101

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Abstract

Manufacturing supply chains are increasingly becoming agile to keep up with the rapid changes in the market. Ability to assess and select new suppliers quickly is a necessity for rapid formation of agile supply chains. The Digital Manufacturing Market (DMM) was proposed as a virtual market for trading manufacturing services. In the DMM, buyers and sellers are represented by intelligent software agents. The DMM enables autonomous deployment of service-oriented supply chains from a pool of suppliers that are distributed geographically. In this paper, a simulated model of the DMM is proposed and implemented. The objective is to evaluate the performance of the market under different scenarios. The metrics used for evaluating the performance of the market include average customer wait time, utilization rate of the suppliers in the system, the number of matched services in a given time period, and the overall score of the created supply chains. The results show that a combination of dynamic capacity adjustment and discount policy improves the performance of the market.

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