Abstract
Purpose This paper proposes a data analytics approach that effective solves a container loading problem. The problem is to decide how much products must be loaded on a container that is delivered by a third party logistics (3PL) providers. The main difficulty in the problem is due to government regulation on the total gross weight. It regulates the maximum gross weight but 3PL’s trucks have random weights. Methods This paper develops an efficient data analytics method that efficiently incorporates the randomness of truck weight using an empirical distribution of historical data. Results Using detailed shipping data from a real company, this paper provides numerical results that reveal that the proposed analytics method provides a significant cost reduction. Conclusion Considering the fact that many manufacturing companies are subject to a similar logistics practice, the data analytics method studied in this paper can be applied in diverse industries.
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