Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

Singh, Adarsh Kumar and Subramanian, Nachiappan and Pawar, Kulwant S. and Bai, Ruibin (2016) Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation. Annals of Operations Research . ISSN 1572-9338

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Abstract

The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation. Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain. Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver. The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

Item Type: Article
Additional Information: The final publication is available at link.springer.com via http://dx.doi.org/10.1007/s10479-016-2332-z
Keywords: Location-allocation problem; Cold chain configuration; Coordination; Big data
Schools/Departments: University of Nottingham Ningbo China > Faculty of Business > Nottingham University Business School China
University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
Identification Number: 10.1007/s10479-016-2332-z
Depositing User: Eprints, Support
Date Deposited: 23 Nov 2017 11:49
Last Modified: 27 Nov 2017 10:56
URI: http://eprints.nottingham.ac.uk/id/eprint/48341

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