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An intelligent logistics support system for enhancing the airfreight forwarding business
Author(s) -
Lau H.C.W.,
Choy K.L.,
Lau Peter K.H.,
Tsui W.T.,
Choy L.C.
Publication year - 2004
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2004.00283.x
Subject(s) - computer science , heuristics , workflow , scheduling (production processes) , decision support system , container (type theory) , benchmarking , operations research , operations management , business , artificial intelligence , mechanical engineering , marketing , database , engineering , economics , operating system
Recent research related to the aircraft container loading and scheduling problem for airfreight forwarding business has seen significant advances in terms of load plan optimization, taking into account the cost and volume of packed boxes. In today's competitive industrial environment, it is essential that freight forwarders are able to collaborate with carriers (airline companies) to achieve the best possible selection of logistics workflow. However, study of contemporary research publications indicates that there is a dearth of articles related to the design and implementation of an intelligent logistics system to support decision‐making on carrier selection, aircraft container loading plans as well as carrier benchmarking. This paper presents an intelligent logistics support system (ILSS) which is able to provide expert advice related to the airfreight forwarding business, enhancing the logistics operations in relevant activities within the value chain of tasks. ILSS comprises a heuristics‐based intelligent expert system which supports carrier searching and cargo trading planning as well as load plan generation. The proposed approach is meant to enhance various operations in the airfreight forwarding business, adopting computational intelligence technologies such as rule‐based reasoning to provide domain advice and heuristics to support the generation of load plans. After potential outcomes are generated by the heuristics‐based intelligent expert system, a neural network engine is applied to support prediction of unexpected events. To validate the viability of this approach, a production system using the ILSS has been developed and subsequently applied in an emulated airfreight forwarding environment. The application results indicate that the operation time from searching for potential carriers to the execution of the order is greatly reduced. In this paper, details related to the structure, design and implementation of the ILSS are also covered with the inclusion of the actual program codes for building the prototype.