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The logistics industry in Japan is facing a severe shortage of labor.Therefore, there is an increasing need for joint transportation allowing largeamounts of cargo to be transported using fewer trucks. In recent years, the useof artificial intelligence and other new technologies has gained wide attentionfor improving matching efficiency. However, it is difficult to develop a systemthat can instantly respond to requests because browsing through enormouscombinations of two transport lanes is time consuming. In this study, we focuson a form of joint transportation called triangular transportation andenumerate the combinations with high cooperation effects. The proposedalgorithm makes good use of hidden inequalities, such as the distance axiom, tonarrow down the search range without sacrificing accuracy. Numericalexperiments show that the proposed algorithm is thousands of times faster thansimple brute force. With this technology as the core engine, we developed ajoint transportation matching system. The system has already been in use byover 150 companies as of October 2022, and was featured in a collection oflogistics digital transformation cases published by Japan's Ministry of Land,Infrastructure, Transport and Tourism.

Shipper collaboration matching: fast enumeration of triangular transports with high cooperation effects