A simulation‐expert‐system‐based approach for machine sizing of production systems

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this research is to focus on the solution of the resource sizing problem for production systems (PSs), defined as the specification of the number of each type of resources to be used in a production process for a given time period. Design/methodology/approach -The resource sizing problem is tackled by a simulation-expert-system-based approach, coupling an expert system (ES) with a simulation tool. Hence, a number of "simulation -ES optimization" cycles are realized until obtaining non-improvable levels of performance. The main performance measures considered in this work are related to the manufacturing orders due dates (DD). Findings -Through the approach proposed in this work, it is possible to size machines in order to optimize DD related performance measures for a PS belonging to a specific application domain. PSs of this domain are characterized by a functional layout and feature no labor constraints. In addition, machines belonging to a same department are considered to be identical. Originality/value -The developed approach allows studying the machine sizing problem realistically, through the use of stochastic simulation. Also, by coupling an ES to the simulation tool, it avoids the try and error aspect characterizing most simulation-based approaches. It hence features a well-structured reasoning mechanism for the search of the best solution.

A manufacturing systems network model for the evaluation of complex manufacturing systems

“…More sophisticated methods for dimensioning manufacturing systems exist, yet they are usually very specific and require a large modeling effort (e.g. computer simulation) (Feyzioglu et al, 2005;Masmoudi et al, 2006).…”

Penambahbaikan Prestasi Fleksibiliti Dan Kecekapan Masa Bagi Syarikat Pembuatan: Satu Kajian Simulasipenambahbaikan Prestasi Fleksibiliti Dan Kecekapan Masa Bagi Syarikat Pembuatan: Satu Kajian Simulasi

“…Tannock et al [25] berpendapat bahawa "anggapan" perlu dibuat untuk menetapkan situasi atau satu fenomena kerana sekiranya terdapat perubahan terhadap ciri-ciri data dalam kajian simulasi, maka keputusan ujian boleh diragui. Malah, Kumar dan Phronmathed [26], dan Wassim Masmoudi et al [27] turut memberikan pendapat sedemikian. Berikut akan menerangkan pengenalan kepada projek, dan pembentukan model simulasi dan pemungutan data termasuk pekerja, waktu bekerja, syif, dan masa pemprosesan.…”