| At present, the industry of logistics is developing towards profession andsocialization. The logistics distribution plays a very important role in the enterprisesrelated, and the Vehicle Scheduling Problem(VSP) is the key problem of logisticsdistribution. Therefore, it is meaningful to study on VSP.VSP is a typical NP-hard problem and is difficult to be solved by using puremathematical methods。With the development of intelligent technology, more andmore people use intelligent methods to solve the Vehicle Scheduling Problem. Thesemethods includes genetic algorithm, simulated annealing algorithm, tabu searchalgorithm and Hopfield network. But current studies are basically only involved withcomparatively simple VSP, and the researches are scattered. No matter from theperspective of depth or width , they cannot meet the rapid growth of logistic industry.To realize the problem, in this paper, the deployment of various static VSPwith non-full load is studied systematically based on current theoretical methods. Inorder to find the best suitable algorithm, a summary of concerning references is givenand the current insufficiencies of domestic and foreign researches of VSP arereviewed. Then this paper uses improved genetic algorithm, simulated annealingalgorithm, tabu search algorithm and Hopfield network to optimize VSP with loadlimit. At last, an intelligent method is given to study on various static VSP. Theseconditions include single-depot VSP with load and time windows limits, single-depotVSP with multi-type vehicle limits, single-depot VSP in complex conditions,multi-depots VSP with single-type vehicle limits, multi-depots VSP with multi-typevehicle limits, single-depot integrated VSP, multi-depots integrated VSP andmulti-depots open VSP, etc.In this paper, many representational examples are given . Compared with theresults attained by some other references, the experiments indicate the validity andfeasibility of the intelligent method to the VSP. |
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