Simulation-based optimisation using simulated annealing for crew allocation in the precast industry


Numerous different combinations of crew alternatives can be deployed within a labour-intensive manufacturing industry. This can therefore often generate a large number of possible crew allocation plans. However, inappropriate selection of these allocation plans tends to lead to inefficient manufacturing processes and ultimately higher labour allocation costs. Thus, in order to reduce such costs, more allocation systems are required. The main aim of this study is to develop a simulation-based multi-layered simulated annealing system to solve crew allocation problems encountered in labour-intensive parallel repetitive manufacturing processes. The ‘multi-layered’ concept is introduced in response to the problem-solving requirements. As part of the methodology used, a process simulation model is developed to mimic a parallel repetitive processes layout. A simulated annealing module is proposed and embedded into the developed simulation model for a better search for solutions. Also, a multi-layered dynamic mutation operator is developed to add more randomness to the searching mechanism. A real industrial case study of a precast concrete manufacturing system is used to demonstrate the applicability and practicability of the developed system. The proposed system has the potential to produce more cost-effective allocation plans, through reducing process-waiting times as compared with real industrial-based plans.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Uncontrolled Keywords: Simulated annealing,crew allocation,parallel repetitive processes,precast concrete industry,manufacturing simulation
Publication ISSN: 1745-2007
Full Text Link:
Related URLs: https://www.tan ... 07.2017.1313721 (Publisher URL)
PURE Output Type: Article
Published Date: 2018
Published Online Date: 2017-04-21
Accepted Date: 2017-03-28
Authors: Al-Bazi, Dr Ammar (ORCID Profile 0000-0002-5057-4171)
Dawood, Nashwan


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