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Batching decisions for assembly production systems

Batching decisions for assembly production systems

Kovalyov, M.Y., Potts, C.N. and Strusevich, V.A. (2003) Batching decisions for assembly production systems. European Journal of Operational Research, 157 (3). pp. 620-642. ISSN 0377-2217 (doi:https://doi.org/10.1016/S0377-2217(03)00250-9)

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Abstract

The two-stage assembly scheduling problem is a model for production processes that involve the assembly of final or intermediate products from basic components. In our model, there are m machines at the first stage that work in parallel, and each produces a component of a job. When all components of a job are ready, an assembly machine at the second stage completes the job by assembling the components. We study problems with the objective of minimizing the makespan, under two different types of batching that occur in some manufacturing environments. For one type, the time to process a batch on a machine is equal to the maximum of the processing times of its operations. For the other type, the batch processing time is defined as the sum of the processing times of its operations, and a setup time is required on a machine before each batch. For both models, we assume a batch availability policy, i.e., the completion times of the operations in a batch are defined to be equal to the batch completion time. We provide a fairly comprehensive complexity classification of the problems under the first type of batching, and we present a heuristic and its worst-case analysis under the second type of batching.

Item Type: Article
Additional Information: [1] Accepted: 12 March 2003. [2] First published online: 27 August 2003. [3] Published in print: 16 September 2004.
Uncontrolled Keywords: assembly scheduling problem, flow shop, batching, batch availability, complexity, approximation
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TJ Mechanical engineering and machinery
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
School of Computing & Mathematical Sciences > Statistics & Operational Research Group
Related URLs:
Last Modified: 14 Oct 2016 09:01
URI: http://gala.gre.ac.uk/id/eprint/725

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