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Multi-job production systems: definition, problems, and product-mix performance portrait of serial lines

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journal contribution
posted on 2017-08-17, 13:30 authored by Pooya Alavian, Peter Denno, Semyon M. Meerkov
This paper pursues two goals: (a) Define a class of widely used in practice flexible manufacturing systems, referred to as Multi-Job Production (MJP) and formulate industrially motivated problems related to their performance. (b) Provide initial results concerning some of these problems pertaining to analysis of the throughput and bottlenecks of MJP serial lines as functions of the product-mix. In MJP systems, all job-types are processed by the same sequence of manufacturing operations, but with different processing time at some or all machines. To analyse MJP with unreliable machines, we introduce the work-based model of production systems, which is insensitive to whether single- or multi-job manufacturing takes place. Based on this model, we investigate the performance of MJP lines as a function of the product-mix. We show, in particular, that for the so-called conflicting jobs there exists a range of product-mixes, wherein the throughput of MJP is larger than that of any constituent job-type manufactured in a single-job regime. To characterise the global behaviour of MJP lines, we introduce the Product-Mix Performance Portrait, which represents the system properties for all product-mixes and which can be used for operations management. Finally, we report the results of an application at an automotive assembly plant.


This work has been supported, in part, by the National Institute of Standards and Technology [Award Number 70NANB16H017].



  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Journal of Production Research


1 - 26


ALAVIAN, P., DENNO, P. and MEERKOV, S.M., 2017. Multi-job production systems: definition, problems, and product-mix performance portrait of serial lines. International Journal of Production Research, 55(24), pp. 7276-7301.


© Taylor & Francis


  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date



This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 28 Jun 2017, available online: http://dx.doi.org/10.1080/00207543.2017.1338779






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