Work

Design and control of manufacturing systems: a discrete event optimisation methodology

Year: 2017

Type: article

Abstract: Simulation optimisation has gained a great attention due to its success in the design of complex manufacturing systems. In this paper, we look at manufacturing as a special class of queueing systems and propose the Discrete Event Optimisation (DEO) methodology, which provides a formal way to develop integrated mathematical models for the simultaneous simulation and optimisation. In the case, the obtained model is a mixed integer linear programming model; the methodology provides a formal way to generate approximations of them. The analytical properties of DEO models are analysed for the first time in the framework of sample path optimisation and mathematical programming. The methodology represents a reference for the use of mathematical programming as a way to model simulation optimisation for queueing systems. The applicability of the DEO methodology to complex problems is showed using the task and buffer allocation problem in a production line.

Source: International Journal of Production Research

DOI: 10.1080/00207543.2017.1412532

Authors: Giulia Pedrielli, Andrea Matta, Arianna Alfieri, Mengyi Zhang

Institutions: Arizona State University, Politecnico di Milano, Politecnico di Torino, Shanghai Jiao Tong University

Language: English

Topic: Simulation Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Shanghai Jiao Tong University

Award: G3037388294