Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation
- 796 Downloads
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.
KeywordsLocation-allocation problem Cold chain configuration Coordination Big data
The authors acknowledge the support from National Natural Science Foundation of China (Grant No. 71471092), Ningbo Science and Technology Bureau (2014A35006) and the International Academy for Marine Economy and Technology (IAMET) for funding this research.
- Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2016). Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Annals of Operations Research,. doi: 10.1007/s10479-016-2276-3.
- Google Maps API. (2016). https://developers.google.com/maps/documentation/geocoding/usage-limits.
- Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2016). Back in business: operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research,. doi: 10.1007/s10479-016-2226-0.
- Sinnott, R. W. (1984). Virtues of the Haversine. Sky and Telescope, 68(2), 159.Google Scholar
- Tayal, A., & Singh, S. P. (2016). Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem. Annals of Operations Research,. doi: 10.1007/s10479-016-2237-x.
- Wang, G., Gunasekaran, A., & Ngai, E. W. T. (2016). Distribution network design with big data: model and analysis. Annals of Operations Research,. doi: 10.1007/s10479-016-2263-8.
- Zhang, Z., Zhang, K. & Song, B. (2012). The information construction of third-party warehousing in the cold chain logistics. In LISS 2012: Proceedings of 2nd International Conference on Logistics, Informatics and Service Science.Google Scholar