Benchmarking of optimisation techniques based on genetic algorithms, tabu search and simulated annealing

The airfreight forwarding business requires the application of stochastic search techniques to support the development of the industry. In the workflow of airfreight forwarding, the cargo loading process is believed to be the most probable step to find room for further improvement. How to carry cargoes efficiently needs to be taken into consideration to maximise the profit without any violation of the volume and weight constraints. Among those search techniques, Genetic Algorithms (GA), Tabu Search (TS) and Simulated Annealing (SA) are prevalently used to deal with the optimisation problems. As an illustration of the application of the three search techniques to the cargo loading problem, it is suggested that GA is the most appropriate method to apply in the optimisation of freight forwarding application. This paper begins with a glance at the cargo loading problem and the airfreight forwarding profit model. Then the working procedures of stochastic search techniques, including GA, TS and SA, are described as they are applied to the cargo loading problem. Subsequently, a qualitative comparison among these three approaches is made to suggest a search technique that is found to be suitable for optimising cargo loading plans in the airfreight forwarding business.