Title: Robust optimisation model for the cold food chain logistics problem under uncertainty

Authors: Golnar Behzadi; Balan Sundarakani; Elham Mardaneh

Addresses: Faculty of Business and Management, University of Wollongong in Dubai, Knowledge Village, Dubai 20183, UAE ' Faculty of Business and Management, University of Wollongong in Dubai, Knowledge Village, Dubai 20183, UAE ' Department of Mathematics and Statistics, Western Australian Centre of Excellence in Industrial Optimization, Curtin University of Technology, G.P.O. Box U1987, WA, 6845, Australia

Abstract: In the last two decades, food safety has become one of the main concerns in the area of logistics and supply chain management and also in cold chain. Safety is a critically sensitive area in this category as if the required safety conditions are not satisfied during the logistics process, foods will soon deteriorate and probably become unsafe to use by customers. Thus, the problem of cold food safety has encouraged serious attentions among the logistics practitioners. However, because of the complexity in nature of such problems, research so far is limited to the quantitative models with deterministic parameters and the robustness of this nature still remains unanswered. In this paper, a robust optimisation model has been developed aiming to maximise the food safety aspects and thus to minimise the logistics cost of the cold chain system under various uncertainties and customers time windows restrictions. The model has been solved by artificial bee colony intelligence algorithm through MATLAB 8 software. Finally, the results are analysed for possible real world considerations in order to propose some key practical highlights.

Keywords: cold food supply chains; robust optimisation; food safety; uncertainty; artificial intelligence; food logistics; supply chain management; SCM; modelling; artificial bee colony; ABC.

DOI: 10.1504/IJLEG.2013.058821

International Journal of Logistics Economics and Globalisation, 2013 Vol.5 No.3, pp.167 - 179

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 22 Jan 2014 *

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