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Hybrid Algorithms for Manufacturing Rescheduling: Customised vs. Commodity Production

Hybrid Algorithms for Manufacturing Rescheduling: Customised vs. Commodity Production
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Author(s): Luisa Huaccho Huatuco (University of Leeds, UK)and Ani Calinescu (University of Oxford, UK)
Copyright: 2013
Pages: 29
Source title: Industrial Engineering: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-1945-6.ch080

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Abstract

This chapter investigates manufacturing rescheduling of customised production and compares the results with those found for commodity production in earlier research by the authors. The hybrid rescheduling algorithms presented in this chapter were obtained by combining two key rescheduling-related elements found in the literature (a) rescheduling criteria (i.e., job priority, machine utilisation and right-shift delay) with (b) level of disruption transmitted to the shop-floor due to rescheduling (i.e., High disruption and Low disruption). The main advantage of hybrid rescheduling algorithms over individual rescheduling algorithms consists of their ability to combine the main features of two different algorithms, in order to achieve enhanced performance, depending on the objective of the organisation. The five hybrid rescheduling algorithms taken into account in this chapter are: Priority High, Priority Low, Utilisation High, Utilisation Low and Right-Shift. The authors’ case study research in three manufacturing companies has identified the use of a set of these hybrid algorithms in practice. Each of the case studies is evaluated in terms of time-based performance in three main areas: suppliers’ interface, internal production and customers’ interface. This evaluation is carried out for both customised and commodity production, using the same hybrid rescheduling algorithms and performance measure the authors used in their previous research work, for comparability purposes (i.e. the entropic-related complexity). The findings show that customised production exhibits a lower entropic-related complexity than commodity production. Although this behaviour may seem unexpected, the entropic-related complexity analysis allows for an interpretation / understanding of its underlying reasons. For example, companies making customised products first agreed the specifications of the products with the customer, and then they mutually agreed on a contract which would financially protect manufacturers (should last minute customer changes occur), by specifying analytically determined penalties or premium charges. Furthermore, a set of recommendations were made to the companies involved in this research study based on the analysis presented in this chapter, such as the need for manufacturing organisations of customised products to ensure they have dependable suppliers, and that, internally, they plan for and embed sufficient spare capacity to cope with internal or external disturbances.

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