정규 분포 모델을 이용한 화물 적재 문제의 이론적 해법 도출 및 활용
On the Theoretical Solution and Application to Container Loading Problem using Normal Distribution Based Model
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This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.
Abstract
This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.
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